1056 research outputs found
Sort by
Dataset for 'Cell wall microstructure, pore size distribution and absolute density of hemp shiv'
No full textThis dataset includes all the raw data underlying the manuscript 'Cell wall microstructure, pore size distribution and absolute density of hemp shiv', published in Royal Society Open Science.
The raw data were generated from analysing hemp fibre with transmission electron miscroscopy (TEM), scanning electron microscopy (SEM), field emission scanning electron microscopy (FESEM), mercury intrusion porosimetry (MIP), CT scans, confocal microscopy, colour resin penetration, and absolute density testing.
The data can be accessed via the Dryad Digital Repository: https://doi.org/10.5061/dryad.8t8v
Dataset for "Elevated production of the aromatic fragrance molecule, 2-phenylethanol, using Metschnikowia pulcherrima through both de novo and ex novo conversion in batch and continuous modes"
No full text2-phenylethanol (2PE) is a fragrance molecule predominantly used in perfumes and the food industry. It can be made from petrochemicals inexpensively, however, this is unsuitable for most food applications. Currently, the main method of production for the bio-derived compound is to extract the trace amounts found in rose petals, which is extremely costly. Potentially fermentation could provide an inexpensive, naturally sourced, alternative. In this investigation, 2PE was produced from the yeast Metschnikowia pulcherrima, optimised in flasks before scaling to 2L batch and continuous operation. 2PE can be produced in high titres under de novo process conditions with up to 1,500 mg/L achieved in a 2L stirred bioreactor. This is the highest reported de novo titre to date, and achieved through high sugar loadings coupled with low nitrogen conditions. The process successfully ran in continuous mode also, with a concentration of 650 mg/L of 2PE being maintained. The 2PE production was further increased by the ex-novo conversion of phenylalanine and semi-continuous solid phase extraction from the supernatant. Under optimal conditions 14,000 mg/L of 2PE was produced. The work presented here offers a novel route to naturally sourced 2PE through a scalable fermentation with a robust yeast highly suited to industrial biotechnology.
In this dataset the underlying data is presented including calibration curves. Files are named according to the figure that they are related to in the original paper.This is detailed in the methods section of the accompanying paper.This is detailed in the methods section of the accompanying paper.The spreadsheets are in Excel (XML) format
Data for the publication: 'Detection of low frequency continuum radiation'
No full textThese data illustrate the theory, data analysis, and exemplary results of the array processing described in the corresponding publication entitled 'Detection of low frequency continuum radiation'
Dataset for "Evaluation of InSAR monitoring data for post-tunnelling settlement damage assessment".
No full textData supporting figures in the publication "Evaluation of InSAR monitoring data for post-tunnelling settlement damage assessment". This dataset contains InSAR-based monitoring data of ground and building displacements. The data was collected using multitemporal synthetic aperture radar interferometry.The data was collected using multitemporal synthetic aperture radar interferometry (InSAR) and includes structural displacements and derived deformations. For full details of the methodology used, please refer to the ‘Methodology’ section of the associated paper.The data is presented as .fig files in MATLAB Level 5 MAT-file format.Data files are named after the corresponding figure in the associated paper
Data for "High resolution air-clad imaging fibers"
No full textThe data supporting the results presented in the paper "High resolution air-clad imaging fibers".
This body of work is designed to provide an indication of the imaging performance of a novel, high resolution imaging coherent fibre bundle fabricated in the University of Bath's Centre for Photonics and Photonic Materials' facilities.
Traditionally, the imaging fibres such as those used in medical endoscopes consist of solid doped and undoped silica glass. The novelty of our design is in having the cladding that is comprised of air-filled silica capillaries, achieving a cladding index close to that of air. This increase in index contrast with the cores (still solid doped silica) provides a higher numerical aperture waveguide. As a result, the light is better confined to the cores, and higher resolution, or a wider operational bandwidth, can be achieved.For the images:
Supercontinuum light sources were used as the excitation source throughout all visible and IR imaging experiments.
Lenses with NA greater than 0.5 were used for all imaging experiments.
A silicon camera and bandpass filters were used to obtain 500-1000 nm wavelength images.
a short wave infra-red camera was used from 1000-1600 nm wavelength.
For the spreadsheet data:
This is loss data that was obtained using the cutback method by coupling a 633 nm helium neon gas laser into a group of 100 or more cores of the imaging fibre
Mock juror perceptions of credibility and culpability in an autistic defendant 2018
No full textOne-hundred-and-sixty-one jury-eligible participants read a vignette describing a male who was brought to the attention of police for suspicious and aggressive behaviours and displayed atypical behaviours in court. Half of participants were informed that the defendant had ASD and were given background information about ASD; the other half received no diagnostic label or information. The provision of a label and information led to higher ratings of the defendant’s honesty and likeability, reduced blameworthiness, fewer guilty verdicts, and more lenient sentencing. Thematic analysis revealed that participants in the label condition were more empathetic and attributed his behaviours to his ASD and external factors, while participants in the No label condition perceived the defendant as deceitful, unremorseful, inappropriate and aggressive.The study employed a between-participants survey design whereby mock jurors were randomly assigned to one of two label conditions: ‘Label+info’, in which mock jurors were informed that the defendant was autistic and were given further information about the condition and how the individual was affected by it; or ‘No label’ in which no diagnosis or information about ASD was provided. All participants completed the study on the online Qualtrics data system. More information is given in the 'Methods' file
Dataset for "Hydrothermal conversion of lipid-extracted microalgae hydrolysate in the presence of isopropanol and steel furnace residues"
No full textMicroalgae have a high potential as a feedstock for the production of biofuels, either indirectly, through the extraction of lipids, which can be transformed into biodiesel, or directly via whole cell conversion using hydrothermal liquefaction (HTL). Both approaches have disadvantages, due to the high cost of cultivating microalgae with sufficient lipid content (>40%), while the whole cell conversion produces low quality oils, which require significant further upgrading. This work investigated the possibility of realising the benefits of both processes, by studying the liquefaction reaction of a lipid-extracted algae hydrolysate. Included in this dataset are the Excel spreadsheets containing the raw data presented in the linked publication and detailing the mass balances, the metal analysis and the characterisation of the products.Mass balance data was collected gravimetriclly, all other data was collected as per the methods section. IPA (iso-propyl alcohol)/water property data was calculated according to standard methods detailed in the spreadsheet.Calculations were performed in Microsoft Excel 2016.The data are presented as three Excel spreadsheets, providing the SEM–EDX (scanning electron microscopy and energy-dispersive X-ray spectroscopy) analysis, the reaction yields, and the IPA/water property data, respectively
Feeding influences adipose tissue responses to exercise in overweight men
Get PDFDataset for the following study:
Feeding profoundly affects metabolic responses to exercise in various tissues but the effect of feeding status on human adipose tissue responses to exercise has never been studied. Ten healthy overweight men aged 26 ± 5 years (mean ± SD) with a waist circumference of 105 ± 10 cm walked at 60% of maximum oxygen uptake under either FASTED or FED conditions in a randomised, counterbalanced design. Feeding comprised 648 ± 115 kcal 2 h before exercise. Blood samples were collected at regular intervals to examine changes in metabolic parameters and adipokine concentrations. Adipose tissue samples were obtained at baseline and one hour post-exercise to examine changes in adipose tissue mRNA expression and secretion of selected adipokines ex-vivo. Adipose tissue mRNA expression of PDK4, ATGL, HSL, FAT/CD36, GLUT4 and IRS2 in response to exercise were lower in FED compared to FASTED conditions (all p ≤ 0.05). Post-exercise adipose IRS2 protein was affected by feeding (p ≤ 0.05), but Akt2, AMPK, IRS1, GLUT4, PDK4 and HSL protein levels were not different. Feeding status did not impact serum and ex-vivo adipose secretion of IL-6, leptin or adiponectin in response to exercise. This is the first study to show that feeding prior to acute exercise affects post-exercise adipose tissue gene expression and we propose that feeding is likely to blunt long-term adipose tissue adaptation to regular exercise
Data from "A bodipy based hydroxylamine sensor"
No full textThis repository contains the experimental data discussed in the manuscript. Including, 1H NMR, 13C NMR (FID and PDF) and Mass Spectra for all the intermediates (A-H); probe 1; the hydroxylamine nitrone cyclic product; N-Methylhydroxylamine cyclic product; N,O,-BocHydroxylamine alkyne and N-Hydroxylamine alkyne. Fluorescence analysis data of probe 1 including the hydroxylamine titration curve; selectivity data against other hydroxylamines; amines and amino acids, Fluorescence intensity changes for probe 1 as a function of time with increasing concentrations of hydroxylamine (time drive) and cell images
Data for "Validating the anatomical positioning of the Actigraph Accelerometer in UK Military Amputees in the prediction of physical activity energy expenditure"
No full textThese data were collected to support a study that assessed the influence of the anatomical placement of a tri-axial accelerometer on the prediction of physical activity energy expenditure (PAEE) in traumatic lower-limb amputees during walking in order to develop valid population-specific prediction algorithms.
The dataset consists of indirect calorimetric data and accelerometer data collected from participants whilst walking on a treadmill at range of velocities and performing an upper body exercise protocol on the arm crank ergometer at incremental speeds with a fixed resistance.Thirty participants, consisting of unilateral (n=10), and bilateral (n=10) amputees, and non-injured controls (n=10) volunteered to complete eight activities; resting in a supine position, walking on a flat (0.48, 0.67, 0.89, 1.12, 1.34 m.s-1) and an inclined (3 and 5% gradient at 0.89 m.s-1) treadmill. Participants were then asked to complete an arm crank ergometer (ACE) exercise at three difference cadences (50, 70 and 90 rpm) and a fixed resistance (50W). Patient information relating to age, body mass, body height, hip and waist circumference, level of amputation and length of rehabilitation was measured prior to commencing the activities. During each task, expired gases were collected using indirect calorimetry and an Actigraph GT3X+ accelerometer was worn on the right hip, left hip and lumbar spine.The Metamax 3B and the three GT3X+ activity monitors were synchronised before use. Breath-by-breath data was exported into Microsoft Excel from the Metamax 3b. PAEE was then calculated using the V̇O2 and CO2 values (l·min-1) from the Metamax in an Excel spreadsheet using the Weir equation. Resting metabolic rate (RMR; kcal·min-1) was subtracted from total energy expenditure to determine PAEE. Metabolic equivalent (METs) were then calculated using measured exercise V̇O2 divided by resting V̇O2 to derive individual METs in the last 2 minutes of each treadmill intensity. Comparisons between accelerometer outputs (PAC) and criterion PAEE were made between the final two-minutes of each activity (representative of steady-state).
The GT3X+ accelerometer units were downloaded using ActiLife software (ActiGraph, Pensocola, FL, USA). Data was exported to Microsoft Excel in a time and date stamped comma-separated value (CSV) file format. Activity counts (counts·min-1) from the GT3X+ were then averaged over the corresponding final two minutes of each activity. This data is presented in the attached file.
Statistics:
PAEE prediction models were developed using corresponding data from each task for devices at each location, using linear regression analysis. The dependent variable was PAEE (kcal·min-1) during the final 2 minutes of each task (that is 80 values in each group). The independent variable was accelerometer outputs (counts·min-1) for the GT3X+. Pearson product moment correlation coefficients (r) and coefficients of determination (R2) statistics were reported to assess the association between criterion PAEE and outputs from devices at each location. Standard Error of the Estimate (SEE) was also calculated for each correlation (Model 1).
The GT3X+ worn at the anatomical position with the strongest relationship to the criterion PAEE was then selected for further analysis, to develop a predictive model for PAEE. Covariates, which included age, body mass, waist circumference, time since amputation, and level of amputation, were analysed to determine their association with the criterion PAEE depending on if data was discrete or continuous. These covariates were selected due to their influence upon mobility in US military amputees. Significant covariates were included in the stepwise regression analysis to strengthen the predictive PAEE equations in each group (Model 2).
These predictive models were cross-validated using an independent sample. However, this is not always possible in hard to reach populations due to recruitment issues. Therefore, we adopted a leave-one-out analysis as performed previously by Nightingale et al. Error statistics involved calculating the mean absolute error, mean absolute percentage error and mean signed error for each activity; the later displayed graphically using Bland and Altman plots and limits of agreement analysis. A two way mixed model ANOVA was performed to determine differences between criterion PAEE and predicted PAEE at each treadmill task. Where a significant interaction effect was observed, a Bonferroni correction was applied to Post Hoc tests where multiple comparisons were considered. This was to identify the specific treadmill tasks in which there was a significant difference between the criterion and predicted PAEE. Statistical significance was set a priori of P < 0.05. All analysis was performed using IBM SPSS Statistics 21 for Windows (IBM, Armonk, NY, USA).Technology used:
Accelerometer: Actigraph GT3X+
Treadmill: Woodway Desmo
Portable metabolic system: Metamax 3B
IBM SPSS Statistics 21
Acronyms: RMR = resting metabolic rate, VO2 = volume of oxygen, VCO2 = volume of carbon dioxide, METS = metabolic equivalent, TEE = Total Energy Expenditure, PAEE = physical activity energy expenditure, RPE = rate of perceived exertion, ACE = Arm Crank Ergometer.The tabs along the bottom of the spread sheet demonstrate the indirect calorimetry and accelerometer outputs at during each activity. On each sheet the data is presented in relation to their group (Unilateral, Bilateral and Control).
Resting metabolic rate is highlighted in green and 3 alternate methods(kcal.min-1, MJ/day, kcal/ day) are used to demonstrate its value.
Indirect calorimetry outputs are highlighted in yellow. Outputs taken from the Metamax 3B software include VO2, METs, CO2, TEE and PAEE. This data is expressed as the mean and standard deviation.
Actigraph acceleromter outputs are highlighted in red and categorised according to the anatomical location that they were worn during the activity (longest Limb, spine, shortest limb). raw signal (physical activity counts) are displayed as the mean and standard deviation (counts·min-1). Highlighted in bold along the bottom of the unilateral, bilateral and control data is the mean and standard deviation of each group
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇