1056 research outputs found
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Data set for "A hidden web of policy influence: The pharmaceutical industry’s engagement with UK’s All-Party Parliamentary Groups"
No full textThis dataset contains all payments from external donors received by health related All-Party Parliamentary Groups (APPGs) between 2012-2018. Additional tabs within the dataset look specifically at payments made by pharmaceutical companies, payments made by patient organisations funded by pharmaceutical companies, and payments made by pharmaceutical companies to patient organisations which supported APPGs.The data collection method is outlined in the associated publication.We supplemented our data with data on drug company disclosures of payments to patient organisations we previously collected for "Exposing drug industry funding of UK patient organisations" (https://doi.org/10.1136/bmj.l1806). The only change we made to the data was extended the yearly range (from 2012-2016 to 2012-2018). We extracted relevant payments (namely payments made to patient organisations which had also supported APPGs) from the data.All data were collected manually.The final tab 'Key-Information_Tabs' contains a brief explanation of the contents of each tab in the Excel dataset
Dataset for "Costs and cost-effectiveness of user-testing of health professionals’ guidelines to reduce the frequency of intravenous medicines administration errors by nurses in the United Kingdom: a probabilistic model based on voriconazole administration"
No full textThis dataset relates to a paper describing the costs and cost-effectiveness of user-testing injectable medicines guidelines, which was analysed using a probabilistic decision-analytic model. The dataset contains the Excel models used in the analysis and the STATA meta-analysis output used to determine one of the model inputs (the risk ratio for a medication administration error following a double-check by a nurse [compared with no double-check]).Please see the detailed description given in the related open-access paper.Microsoft Excel 365
STATA 16.0The data contained in the two Excel model files are described within the files using cell comments. These models are based on a number of Excel worksheets in each, which are described below:
Analysis - this worksheet is used to enter model input parameters (for time horizon, annual number of voriconazole doses, number of user testing interviews) and display the results. The run the model, first press the 'Monte Carlo Macro' button, then the 'Cost-effectiveness & EVPI curves' button
Control model figure - this worksheet displays a diagramatic representation of the decision tree model for the no user testing scenario
UT model figure - this worksheet displays a diagramatic representation of the decision tree model for the user testing scenario
CE Curve - this worksheet displays the calculated cost-effectiveness acceptability curve
EVPI - this worksheet displays the calculated expected value of perfect information curve
Parameters - this worksheet displays and defines all the model input parameters and their distributions
Dirichlet dist - this worksheet derives the Dirichlet distributions for error types and error severity
Inflation - this worksheet derives the value for inflation
Live model outputs - this worksheet derives the output for each simulation when the model is run. These outputs are then recorded in the 'Recorded model outputs' worksheet
Recorded model outputs - this worksheet records the output for each simulation when the model is run. These data are then used by the 'Analysis' worksheet to calculate mean values and 95% credible intervals
Dataset for "Sensing pH of individual microdroplet by combining SERS and indicator paper"
No full textThis dataset contains the simulated maximum plasmon induced electric field enhancement factor of spherical metallic nanoparticles. Three different particle diameters were investigated, 20 nm, 40 nm and 60 nm. The simulations were finite-difference time-domain simulations performed in Lumerical. The simulation domain was a three-dimensional cube void spanning 1 µm in each direction. The mesh granularity was 1 nm in the volume occupied by the nanoparticle providing high fidelity electric field data. A broad-spectrum Mie source of light (100 nm to 800 nm) was employed and encapsulated the nanoparticle. The electric field distribution in the plane perpendicular to the incident wave vector of the light, bisecting the nanoparticle, was monitored to determine the maximum electric field enhancement. In the first instance, three different material property annals for Au in Lumerical (Palik, CRC handbook of chemistry and physics (CRC) and Johnson & Christy) were modelled and simulated to determine the sensitivity of simulations on the selected material model. Subsequently, a range of metallic material models were trialled to assess the sensitivity of the electric field enhancements on material: Cr (CRC), Fe (CRC), Ti (CRC), Al (CRC), Pt (Palik), Ni (CRC), Pd (Palik), Cu (CRC), Ag (Palik). The excel file contains the maximum electric field enhancments for the different materials.The simulations were performed in Lumerical using finite-difference time-domain. The material models were provided by the software's material libraries
Dataset for "Stratification in a Reservoir Mixed by Bubble Plumes under Future Climate Scenarios"
No full textDatasets used for the paper "Stratification in a reservoir mixed by bubble plumes under future climate scenarios". This includes models results from Blagdon Lake with both observed weather data and the downscaled future climate data. These runs cover five year intervals from 2030 to 2080 as well as 2017. These weather datasets are also given along with the scripts required to downscale the future data. Observations from Blagdon Lake from Late May to Early September 2017, including a heatwave from 2017.Two temperature chains were deployed within Blagdon Lake from May to September in 2017. These recorded every 10 munities at a meter resolution. These were anchored with weights to prevent drifting and with a floatation device at the surface to keep the rope taught. These were deployed and recovered via boats.Data files produced form AEM3D were processed in MATLABMATLAB or other software that can read .mat files is required to read the results.
The 2018 version of AEM3D was used to run the models.The Model results are saved inside of MATLAB structure called "Data" within each of the saved model run mat files. This includes all the model outputs saved from the model
Dataset for paper entitled "Do emotions benefit investment decisions? Anticipatory emotion and investment decisions in non-professional investors"
Get PDFThis dataset contains behavioural performance and psychophysiological data from four trading games created by Dr Neal Hinvest and Dr Richard Fairchild. The games present participants with the opportunity to invest in a share or keep money in the "bank". The dataset contains data on individuals behaviour while playing the stock market. Measures such as amounts invested insnares and the bank were recorded. Participant performance such as returns made per trial were created. Skin Conductance Response (SCR) was measured while playing the games to measure anticipatory emotion, a covert emotion signal that impacts upon decision-making. Self-reported emotion felt during the tasks was measured via provision of several instances of the Positive and Negative Affect Schedule (PANAS, Watson et al., Journal of Personality and Social Psychology, 54(6), 1063-1070).Participants completed four stock market games where they allocated their (hypothetical) wealth between investing in a share vs. the bank. The bank paid a steady, risk-free, interest while money invested in the share could increase or decrease depending on its behaviour. Anticipatory Skin Conductance Response (aSCR) was measured in a critical time period. Participants also completed the Positive and Negative Affect Scale (PANAS). There were 30 participants, recruited in and around the University of Bath, U.K.The dataset is a .csv file. A codebook is provided as a .txt fil
Dataset for "Screw tightness and stripping rates vary between biomechanical researchers and practicing orthopaedic surgeons"
No full textTen orthopaedic surgeons and ten researchers inserted 60 cortical screws each into artificial bone, for three different screw diameters, with half of the screws inserted through plates and half through washers. This dataset contains the raw measured screw insertion values for both surgeons and researchers; it includes calculated tightness and stripping rates. Confidence values are reported for each screw insertion.Ten orthopaedic surgeons and 10 researchers inserted 60 cortical screws each into artificial bone, for three different screw diameters (2.7, 3.5 and 4.5 mm), with 50% of screws inserted through plates and 50% through washers. Screw tightness, screw hole stripping rates and confidence in screw purchase were recorded. Three members of each group also inserted 30 screws using an augmented screwdriver, which indicated when optimum tightness was achieved
Identification of RNA-RNA interactions in Bacillus Subtilis by in vivo UV crosslinking
No full textSmall RNAs (sRNAs) are a taxonomically-restricted but transcriptomically-abundant class of post-transcriptional regulators. While potentially of importance, we know the function of few. This is in no small part because we lack global-scale methodology enabling target identification, this being especially acute in species without known RNA meeting point proteins (e.g. Hfq). We apply a combination of psoralen RNA cross-linking and Illumina-sequencing to identify RNA-RNA interacting pairs in vivo in Bacillus subtilis, resolving previously well-described interactants. Although sRNA-sRNA pairings are rare (compared with sRNA/mRNA), we identify a robust example involving the unusually conserved sRNA (RoxS/RsaE) and an unstudied sRNA that we term Regulator of small RNA A (RosA). This interaction is found in independent samples across multiple conditions. Given the possibility of a novel associated regulatory mechanism, and the rarity of well-characterised bacterial sRNA-sRNA interactions, we mechanistically dissect RosA and its interactants. RosA we show to be a sponge RNA, the first to be described in a Gram-positive bacterium. RosA interacts with at least two sRNAs, RoxS and FsrA. Unexpectedly, it acts differently on each. As expected of a sponge RNA, FsrA is sequestered by RosA. The RosA/RoxS interaction is more complex affecting not only the level of RoxS but also its processing and efficacy. Importantly, RosA provides the condition-dependent intermediary between CcpA, the key regulator of carbon metabolism, and RoxS. This not only provides evidence for a novel, and functionally important, regulatory mechanism, but in addition, provides the missing link between transcriptional and post-transcriptional regulation of central metabolism.Sample collection: Bacteria were grown to the required O.D before 10 O.D 600 nm units were harvested by centrifugation (4000 g, 5 minutes, 4C). Bacteria were resuspended in 2 ml PBS either containing no AMT (to identify background and levels of spurious interactions) or 0.7mM AMT. Bacteria were incubated for 10 minutes at 37C for 10 minutes before being transferred to a 6 well plate. The bacteria were exposed to UV 365 nm at 0.120 Jcm-2in a for 10 minutes before being added to 1 ml of ice cold killing buffer (20 mM Tris-HCl [pH 7.5], 5 mM MgCl2, 20 mM Na-azide). The bacteria were harvested by centrifugation at X g, the supernatant was discarded and the pellet flash frozen in liquid nitrogen. We determined the in vivo RNA interactome of B. subtilis grown in M9 minimal media supplemented with 0.3% glucose at three points in the growth curve (exponential phase O.D.600nm 0.5, stationary phase O.D.600nm 1.4 and just after lysis had started to occur, and in LB at mid-exponential phase (O.D.600nm of 1.0). Samples were prepared in duplicate.
Nucleic acid extraction: The RNA was extracted by resuspending the cell pellet in 800 µl LETS buffer (10 mM Tris-HCl [pH 8.0], 50 mM LiCl, 10 mM EDTA, 1% sodium dodecyl sulfate [SDS]) and bead beating in a FastPrep using 0.1 µm glass beads for three rounds of 40 seconds. The tubes were transferred to ice in between cycles. The tubes were briefly spun to remove the bubbles created during bead beating. Two rounds of phenol chloroform isoamyl alcohol extraction and one round of choloroform isoamyl alcohol extraction were carried out. Before the addition of 10 % v/v NaAcetate and 1 ml Isopropanyl and precipitation of RNA overnight at -20C. The RNA was pelleted by centrifugation at maximum speed at 4C and the pellet was washed with 70% Ethanol before being air dried and resuspended in water. The RNA was quantified using the Qubit kit (Fisher Life Science). 10 µg of RNA was treated with Turbo DNase (Fisher Scientific) to remove contaminating DNA. Ribosomal RNA was removed using Ribozero (Illumina) according to the manufacturer’s instructions. To form the chimeric RNAs between RNAs crosslinked with AMT the protocol described by Sharma et al was followed. The only modification was the use of CircDNAligase (Epicentre) instead of CircRNAligase as this has been discontinued.
Nucleic acid library construction: Following uncrosslinking at UV 254 nm, RNA was purified and resuspended in 10 µl H2O and processed through the TruSeq RNAseq kit (Illumina) according to the manufacturers instructions.
Nucleic acid sequencing: The prepared libraries were sequenced on the MiSeq (Illumina).
Growth: Growth experiments were performed in LB, M9 medium supplemented with glucose at a final concentration of 0.3%.
Normalization data transformation: The output Sam files and Ban files were generated by aligning the fastq files to the Bacillus genome sequence using STAR aligner (with single end)
Project PXD015051: "Identification of an RNA sponge that controls the levels, processing and efficacy of the RoxS riboregulator of central metabolism in B. subtilis"
No full textOnly a few small regulatory RNAs (sRNAs) have been characterized in B. subtilis, the paradigm of Gram-positive bacteria, and one of the major challenges is target identification. Here we use global in vivo RNA psoralen cross-linking to identify RNA-RNA partners in Bacillus subtilis. Two sRNAs, RoxS and FsrA, play key roles in balancing the metabolic state of the cell in response to carbon sources and iron limitation, respectively. In this study, we identify new mRNA targets for both RoxS and FsrA, and a small RNA (S345/RosA) that is able to interact with both sRNAs. We report that RosA controls the maturation and degradation of RoxS and acts as a sponge to limit the efficacy of RoxS on its targets. Expression of RosA is catabolically repressed by the transcription factor CcpA. We provide evidence that the RosA/RoxS interaction plays a key role in regulating metabolism in response to a switches in carbon source.Strains were grown to O.D. 600 nm 1.0 in LB. 20 O.D. units were harvested and washed 3 X with PBS to remove media components. Cells were resuspended in 200 µl urea buffer (8M urea, 50 mM Tris and 75 mM NaCl). 200 µl of urea buffer washed 0.1 µM beads were added to the cells before being disrupted using three rounds of bead beating for 40 seconds using a FastPrep. Cells were placed on ice between the three rounds of bead beating. The disrupted cells were then sonicated in a water bath for 15 minutes. Cell extracts were centrifuged at 15,000 x g, 5 min and supernatants used for protein quantification (Qubit protein assay kit). Protein reduction and alkylation was conducted by mixing 150 µg of total protein with 10 mM TCEP and 40 mM CAA, at 600 rpm, for 20 min at room temperature. After, proteins were predigested with 1.5 µg of rLysC (Promega) for 3 h at room temperature and samples diluted with 50 mM ammonium bicarbonate, 2 M urea final concentration. Protein digestion was performed with 1.5 µg of Trypsin (Promega) overnight at room temperature. The reaction was stopped by adding 1% TFA and 10 µg of peptides were desalted using StageTip (Rappsilber et al. Nat Protoc. 2007; DOI: 10.1038/nprot.2007.261). Reversed phase chromatography was used to separate 1 µg of tryptic peptides prior to mass spectrometric analysis. The cell proteomes were analysed with two columns, an Acclaim PepMap µ-precolumn cartridge 300 µm i.d. x 5 mm, 5 μm, 100 Å and an Acclaim PepMap RSLC 75 µm i.d. x 50 cm, 2 µm, 100 Å (Thermo Scientific). The columns were installed on an Ultimate 3000 RSLCnano system (Dionex) at 40ᵒC. Mobile phase buffer A was composed of 0.1% formic acid and mobile phase B was composed of acetonitrile containing 0.1% formic acid. Samples were loaded onto the µ-precolumn equilibrated in 2% aqueous acetonitrile containing 0.1% trifluoroacetic acid for 8 min at 10 µL min-1 after which peptides were eluted onto the analytical column at 250 nL min-1 by increasing the mobile phase B concentration from 8% B to 25% over 90 min, then to 35% B over 12 min, followed by a 3 min wash at 90% B and a 15 min re-equilibration at 4% B. Eluting peptides were converted to gas-phase ions by means of electrospray ionization and analysed on a Thermo Orbitrap Fusion (Thermo Scientific). Survey scans of peptide precursors from 375 to 1500 m/z were performed at 120K resolution (at 200 m/z) with a 2x105 ion count target. The maximum injection time was set to 150 ms. Tandem MS was performed by isolation at 1.2 Th using the quadrupole, HCD fragmentation with normalized collision energy of 33, and rapid scan MS analysis in the ion trap. The MS2 ion count target was set to 3x103 and maximum injection time was 200 ms. Precursors with charge state 2–6 were selected and sampled for MS2. The dynamic exclusion duration was set to 60 s with a 10 ppm tolerance around the selected precursor and its isotopes. Monoisotopic precursor selection was turned on and instrument was run in top speed mode.
Thermo-Scientific raw files were analysed using MaxQuant software v1.6.0.16 (Tyanova et al. 2016, The MaxQuant computational platform for mass-spectrometry based shotgun proteomics, Nature Protocols 11, 2301-2319; DOI: 10.1038/nprot.2016.136) against the UniProtKB B. subtilis database (UP000001570, 4,260 entries). Peptide sequences were assigned to MS/MS spectra using the following parameters: cysteine carbamidomethylation as a fixed modification and protein N-terminal acetylation and methionine oxidations as variable modifications. The FDR was set to 0.01 for both proteins and peptides with a minimum length of 7 amino acids and was determined by searching a reversed database. Enzyme specificity was trypsin with a maximum of two missed cleavages. Peptide identification was performed with an initial precursor mass deviation of 7 ppm and a fragment mass deviation of 20 ppm. The MaxQuant feature ‘match between runs’ was enabled. Label-free protein quantification (LFQ) was done with a minimum ratio count of 2. Data processing was performed using the Perseus module of MaxQuant v1.6.0.16 (Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., et al., The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 2016, 13, 731-740). Proteins identified by the reverse, contaminant and only by site hits were discarded. Only protein groups identified with at least two assigned peptides were accepted and LFQ intensities were log2 transformed
Dataset for "Selecting fermentation products for food waste valorisation with HRT and OLR as the key operational parameters"
No full textThis dataset includes the results summary and data overview from a lab-scale bioreactor experiment as discussed in the research paper "Selecting fermentation products for food waste valorisation with HRT and OLR as the key operational parameters", published in Waste Management.
The study comprised three sets of operating conditions tested in duplicate reactors fed with food waste. The data comprises a summary on feedstock composition, operating conditions tested, averaged results, product outcome and kinetic study, and microbial community analysis per reactor and per operating condition. The archaeal and bacterial community data includes the final sequences of the operational taxonomic units found and their relative abundance in each sample as determined by 16s rRNA amplicon sequencing, and rarefaction curves. The raw data files have been submitted in the specialized EMBL-EBI database and are available under the accession number PRJEB40478.Full details of the methodology may be found in the associated manuscript.This dataset was prepared and processed in Microsoft Excel from raw analytical data
Dataset for "Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running"
No full textThis dataset includes the input features and target labels needed to train and test FootNet. The input features include the distal tibia anteroposterior velocity, ankle plantar/dorsi flexion angle and foot centre of mass anteroposterior and vertical velocities. Additionally, ground reaction force data and trial names are also included.This dataset includes data coming from five different datasets collected in three independent laboratories (see associated publication for more details). It includes treadmill running kinematics and kinetics processed to obtain the previously mentioned variables and chopped in running gait cycles.The original datasets were fully reprocessed as described in the Methods section of the associated publication.The project directory StepDetectionStudy is organised as follows:
- Data > OriginalDatasets: Folder containing the entire datasets (*_dataset.npy files).
- Data > DataFolds.npy: File containing the training data grouped in 5 folds.
- Data > TestingSet.npy: File containing the testing set.
Data are organised as Python dictionaries containing the kinematic input features ['X'], label vectors ['Y'], metadata about the trials ['meta'] and vertical GRF ['GRFv']. Each of those dictionary keys contains a list with nested lists with the structure participant > trial > stride. For instance, `dataset['X'][0][0][0]` accesses the kinematic input features characterising the first stride recorded in the first trial of the first participant in dataset.
- CrossValidation > Models: Folder containing the five models developed during cross validation.
- CrossValidation > Results: Folder containing the summary performance metrics for each model on its corresponding validation set and Bland-Altman plots comparing foot strike, toe off and contact times as predicted by FootNet vs gold standard method.
- FinalTest > FootNet_best_candidate: Folder containing the best set of parameters resulting from cross validation.
Summary performance metrics on testing set and Bland-Altman plots comparing foot strike, toe off and contact times as predicted by FootNet vs gold standard method.
- FinalTest > y_and_yhat.mat: File containing testing predictions, target labels and metadata from testing stride cycles for posterior analyses in Matlab presented in the paper.
- FinalModel: Folder containing the final updated model resulting from FinalTest as a SavedModel directory (Tensorflow model format) and as .h5.
- Notebooks > TrainTest_Split.ipynb: Google Colab (Jupyter) notebook demonstrating how the dataset splitting was performed, including training and testing (70/30) and further folding of training dataset in 5 folds.
- Notebooks > CrossValidation.ipnyb. Google Colab (Jupyter) notebook that performs 5-fold cross-validation and selects the best set of weights as best candidate for the final test.
- Notebooks > FinalTest.ipnyb: Google Colab (Jupyter) notebook that updates the best candidate model resulting from cross-validation with the 5 folds as training set and performs the final test on the testing set
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