1056 research outputs found
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Dataset for "Augmenting corn starch gel printability for architectural 3D modeling for customized food"
No full textThis dataset results from a study that aimed to bolster the printability of normal corn starch (NCS) through integration with pregelatinized (PG) high-amylose starch (G50 and G70, with 55% and 68% amylose contents, respectively) and proteins (soy, wheat, pea protein isolates, and whey protein). The PG starch was prepared by disorganizing the high-amylose starches in 33% CaCl₂ solution and then precipitating them with ethanol.
The dataset contains all raw data for the characteristics (rheological properties, expansion rate, texture, digestibility, height, water loss, and moisture content) of different formulations involving the effects of PG high-amylose type, PG-G70 content, protein type, and soybean protein isolate (SPI) content. It also contains the Origin (Unicode) Project files used to generate the plots shown in the associated paper, "Augmenting corn starch gel printability for architectural 3D modeling for customized food".For details of the methodology, see the "Materials and methods" section of the associated paper.The files were created using Excel and Origin software programs.The names of the Origin (Unicode) Project (.opju) files correspond to the figure numbers in the associated paper. For the detailed information regarding the samples tested, refer to the respective figure captions in the associated paper
Data for publication Girvan et al (2025) "Tetramethylrhodamine self-quenching is a probe of conformational change on the scale of 15 – 25 Å"
No full textRaw and partially processed data for Girvan, Ying and Dodson (2025) Chem Commun.
Tetramethylrhodamine (TMR) is a fluorescent dye whose self-quenching has been used as a probe of multiple biological phenomena. We determine the distance-dependence of self-quenching and place bounds on the timescale of TMR dissociation. Our results validate fluorescence self-quenching as an alternative to FRET and enable future assays to be designed with confidence.
Please cite our publication in any use of this data
Dataset for Hydrogen Storage Capacity of Freeze Cast Microporous Monolithic Composites
No full textThis dataset presents the nitrogen adsorption isotherms for a range of powders and monoliths formed from them used to calculate their BET surface area. Hydrogen isotherms are also produced to show the storage capacity for the same entities. A comparison of the mass of hydrogen stored using the monoliths compared with compression is also included. A comparison of the mass of hydrogen stored by compression at different conditions with activated carbon AX21 at different conditions is included. The effect of the volume of adsorbent on the mass of hydrogen stored is also included.Most of this data was obtained experimentally from machines producing adsorption isotherms.
Some of the data was collected from literature. Please see the associated papers for full methodological details.Data is directly from literature and referenced.Excel, matlab and Origin were used to create this data.
Experimental data was collected from a 3flex machine from micromertics
Data relating to the development of pseudoplastic mortars for aerial additive manufacturing
No full textThis dataset covers the development of pseudoplastic mortars, for aerial additive manufacturing. It covers strength tests, rheology tests, calorimetry tests, deformation tests, force required tests, autonomous extrusion tests and mix formulation.Materials and drone extrusion tests were recorded by camera, observation and manual recording. Test data were input into Microsoft Excel.
Mechanical tests took place on a 50 kN Instron Universal 2630-120/305632 device. Axial force tests were loaded at 5 mm/minute. Settlement test material was compressed at a rate of 2 mm/minute.
Trajectory design tests: Dental plaster was applied to the hand-printed specimens to create flat upper and lower surfaces for strength testing. The devices used were the Instron 2630-120/305632 and Automax 5 50-C46W2.
Flexure: prisms were tested in accordance with BS EN 12390-5:2009 (BSI, 2009), using four-point bending tests to ensure failure by flexure rather than by shear. Compressive strength used an Automax 5 50-C46W2 device in accordance with BS EN 1015-11:1999 (BSI, 1999).
Rheometer tests were conducted on a TA Instruments DHR2 rheometer at a constant temperature of 25°C. Oscillatory tests used disposable aluminium flat plates with a 40 mm base plate and 25 mm diameter upper plate. Flow tests used a steel cross-hatched 40 mm base plate and upper plate. In all rheology tests, a 1000 μm geometry gap was used and material was placed upon the base plate immediately following mixing. Displacement-controlled oscillation tests were conducted over a two-hour period. An angular velocity of 5.0x10−5 radians per second and frequency was maintained at 1 Hz.
Calorimetry tests were conducted using a Calormetrix I-Cal 4000 high precision isothermal calorimeter with chambers maintained at 20°C.
Microscopy tests: a 10 nm gold coating was applied to samples, which were placed in a JEOL SEM6480LV scanning electron microscope.Experimental test data was exported from the laboratory instruments' software and imported by Microsoft Excel.Data has been organised and analysed in Microsoft Excel and figures were assembled in Microsoft PowerPoint to be exported to PDF for use with LaTeX
Carbon Capability survey
No full textThis survey uses a nationally representative sample of UK households, and asks over 300 questions relating to climate change and carbon capability. The questions were designed to span key sources of household emissions: energy, transport, food and consumption, as well as other factors which contribute to individuals' carbon capability: influence on others, and citizenship behaviours.
It was conducted in March and April 2022, in two waves due to the large number of questions.We commissioned survey agency Dynata to issue this large, two-wave survey to a representative panel of UK households.Data is provided in SPSS format
Dataset for "Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy"
No full textThe dataset contains a single directory which includes the following files, supporting the associated publication "Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy":
- MasterDataSheet.xlsx contains demographics, anthropometrics and physiological information from each of the participants. Participants have a unique identifier, preserving their anonymity.
- MasterDataSheet_column_naming.txt describes the column names and units in MasterDataSheet.xlsx
- AllCurves_ptavgs.npy contains a python dictionary storing the average spatiotemporal (stride time and duty factor) and continuous kinematics variables (centre of mass vertical displacement normalised to leg length, trunk to pelvis, hip, knee and ankle joint angles in the sagittal plane and the pelvis segment angle in the sagittal plane) for the three stages of the incremental running test considered in the study: STG_02 (11 km/h), STG_03 (12 km/h) and STG_04 (13 km/h). The dictionary is organised as follows:
data
STG_* (running stage code)
P*** (participant code)
variables (RCOM_2, RTRUNK2PELVIS_0, RHIP_0...)
These variables are the average of all the strides in the stage.
- Clust_multispeed_avgepelvictilt.csv contains the average pelvic tilt angle during quiet standing for each participant alongside the cluster label assigned in the multispeed condition.
Scripts to replicate the results of the study can be found in the associated "clustering_runners" GitHub repository and they show how to access the data within these files.This dataset includes motion capture, indirect calorimetry and lactate concentration data. Full details on the systems and procedures used can be found in the associated publication.The .npy files may be read with the NumPy Python library
Dataset for "Facile fabrication of a starch-based wood adhesive showcasing water resistance, flame retardancy, and antibacterial properties via a dual crosslinking strategy"
No full textThe dataset presented herewith is that of a research paper entitled "Facile fabrication of a starch-based wood adhesive showcasing water resistance, flame retardancy, and antibacterial properties via a dual crosslinking strategy" by Chen et al. in the International Journal of Biological Macromolecules for publication (https://doi.org/10.1016/j.ijbiomac.2024.137180).
This paper presents a one-pot process utilising corn starch, sodium hypochlorite, itaconic acid and borax to synthesise a starch-based adhesive with dual crosslinking. The samples, as reflected in the dataset, were prepared with the following compositions: Ost (oxidised starch), Ost/Ita (oxidised starch crosslinked with taconic acid), and Ost/Ita/Borax (oxidised starch crosslinked with both taconic acid and borax).For details of the methodology, see the "Materials and methods" section of the associated paper.The files were created using Excel and Origin software programs.The names of the Origin (Unicode) Project (.opju) files correspond to the figure numbers in the associated paper. For the detailed information regarding the samples tested, refer to the respective figure captions in the associated paper.
In addition, the dataset contains NMR results, which are not included in the manuscript
Dataset for "Nanoplastics Detected in Commercial Sea Salt"
No full textThe electric field distributions of the Au-AAO-50 membrane was simulated in Lumerical according to the geometrical measurements obtained from SEM and AFM, (Fig. 1d and Fig. 1e of the manuscript). Electric field distribution data from 1 nm above (z=534nmPlane_E-Field folder) and at the midsection of the conical tapered holes (z=551nmPlane_E-Field folder) of the SERS substrate are included in the zip folder. Also included are the setup parameters and metadata in a text file which describes the material properties fitting (as shown in the .png files) and .stl files which can be opened in any CAD software to view the simulated geometry.Finite-Difference Time-Domain (FDTD) simulations were performed in Lumerical revealing the electric field distribution on the surface of the Au-AAO-50 SERS substrate. The model SERS substrate was created in Autodesk Inventor based on SEM and AFM of the surface and mapped to Lumerical for simulation. A bicentric hexagonal pattern of holes was generated with side length 500 nm and hole diameter 250 nm. The edges of the orifices were sloped forming a conical cut out based on the AFM. The result of this resembled that of an array of countersunk holes with a chamfer angle of 84° and a diameter of 345 nm at the surface. The AAO was modeled using the built in wavelength dependent material model in the Lumerical database as in refs 41, 42. A 50 nm layer of Au was superimposed onto the surface of the AAO substrate and modelled using a Johnson and Christy material model (based on ref.43) to simulate the substrates used in experiments reported here. Three other metallic layers were modelled for comparison, Ag (Palik 0 - 2 µm material model; ref. 44), Al, and Cu (both CRC material models; ref. 45).
The three-dimensional domain space encompassed 2 µm × 1.732 µm of the surface (x and y directions) of the substrate and a depth of 2 µm (z direction). 3 µm of void space was placed above the substrate surface. The boundaries of the domain were periodic in the x and y direction and perfectly matched layers in the z direction which enabled the reduction of the simulation domain without compromising the validity of the simulation. The mesh resolution was approximately 2% of the hole diameter giving high fidelity results.
A Bloch plane wave source with amplitude 1 V/m was introduced from 2 µm above the SERS substrate model leaving an additional (but crucial) 1 µm of space between the source and the upper boundary. The source was a Fourier transform limited pulse of light with approximately Gaussian spectral profile centered at 785 nm with a wavelength span of 300 nm. The duration of the pulse was approximately 4.2 fs. The plane wave was linearly polarised parallel to the x-axis of the FDTD domain. A frequency domain field and power monitor were placed in the x-y plane 1 nm above the surface of the SERS substrate to extract the electric field distribution on the surface of the sample
Dataset for "Calorie restriction-induced leptin reduction and T-lymphocyte activation in blood and adipose tissue in men with overweight and obesity"
Full text linkRaw data composed of adipose tissue ex vivo secretion, adipose tissue macrophage abundances, adipose tissue T cell counts and proportions, T cell activation marker expression and leptin and insulin receptor expression in adipose tissue, peripheral blood T cell counts and proportions, T cell activation marker expression and leptin and insulin receptor expression in peripheral blood, peripheral blood monocyte proportions and expression of leptin and insulin receptors, adipose tissue metabolic gene expression, serum metabolic and inflammatory protein concentrations, basic participant anthropometrics, dietary energy intake and daily expenditure, DEXA-derived body composition, OGTT results, and energy intake prescriptions during a 3 day 50 % calorie restriction protocol in 12 overweight and obese males.The methodology can be found in the associated paper, below is an overview of these same methods.
MATERIALS AND METHODS
Experimental design
Twelve abdominally overweight/ obese men aged between 35-55 years were recruited from the local community following ethical approval from the South West, Frenchay NHS Research Ethics Committee (REC Reference: 12/SW/0324). Each participant gave written informed consent. Only overweight and obese participants with waist circumference >94 cm (Lean et al., 1995) were recruited since these individuals showed greatest activation of T-lymphocytes within adipose tissue. After a 1-week period of monitoring energy intake and expenditure to confirm ‘energy balance’, participants reduced their caloric intake to 50 % of their normal intake for 3 consecutive days. Participants attended the Physiology Laboratory at the University of Bath before and after this intervention for analysis of immune cell activation and markers of inflammation and metabolism in blood and adipose tissue. An oral glucose tolerance test was also performed to assess glycaemic control before and after the 3-day calorie restriction intervention. Individuals were excluded from participation if they smoked, had personal history of cardiovascular disease, metabolic disease or dyslipidaemia, or were taking medications that may influence lipid or carbohydrate metabolism or immune system function. It was also required that participants had been weight stable for more than 3 months (no change in weight +/-3 %) (Stevens et al., 2006). This study is registered at Clinicaltrials.gov (Reference: NCT02473835).
Sample size determination
There are no data regarding changes in T-lymphocyte activation in human adipose tissue in response to calorie restriction. However, significant differences in T-lymphocyte activation have been observed between lean and obese individuals (Travers et al., 2015). A short period of calorie restriction can reduce serum leptin values by around 40 % (Mars et al., 2006), which is sufficient to reduce typical values for a person with obesity to those of a lean person. Thus, a similar reduction in T-lymphocyte activation in response to calorie restriction might reasonably be anticipated. Previous data indicate that the CD69 mean fluorescence intensity (MFI) for lean CD4+CD69+ cells is 288 (+/45 SD) and obese is 411 (+/31 SD) with an effect size of 3.08 (G-Power) (Travers et al., 2015). To account for potentially greater variability in other activation markers, we recruited 12 participants.
Monitoring of energy balance
Participants were weighed before and after a 1-week period of ‘energy balance monitoring’ to ensure weight stability using a digital balance (Tanita Corp.; Amsterdam, Netherlands). Participants were fitted with a combined heart rate and accelerometry monitor (Actiheart™; Cambridgeshire, UK) to determine habitual total energy expenditure (TEE) (Thompson et al., 2006). TEE was adjusted for measured resting metabolic rate which was measured by indirect calorimetry (Frayn, 1983). A weighed food and fluid intake record was used during this period to estimate participants’ energy intake with dietary analysis performed using COMP-EAT Pro software (v.5.8.0, Nutrition Systems; UK). Participants were asked not to make any conscious changes to their normal lifestyle habits/routines during this period and, to avoid influencing their habitual routines, were not told that the activity monitoring and food records would be used to directly influence the diet prescribed/given during the 3-day calorie reduction period. This analysis was then used to confirm that participants were in a state of energy balance and to write a diet prescription for the 3-day intervention. The aim was to ensure that participants received 50 % of their 'normal' calorie requirements (i.e. average of energy intake and energy expenditure) using foods they would normally consume. Since there are errors associated with estimations of total energy expenditure (Thompson et al., 2006) and recording dietary intake (in particular underreporting) (Poslusna et al., 2009), we stipulated a priori that total energy intake and energy expenditure values had to be within 25 % of each other during the energy balance assessment and, furthermore, that the prescribed calorie intake had to be within 40–60 % of both the total energy expenditure and dietary intake values. If either of these requirements were not met, participants were asked to repeat the monitoring phase.
Calorie restriction protocol
To determine the exact calorie intake required to achieve a calorie restriction of 50 % normal energy requirements for the 3-days, an average of energy expenditure and dietary intake from the monitoring period was taken (provided that the above criteria were met). This value was then divided in half to give the ‘target’ calorie value for each of the 3 days in the diet. Subsequently, three separate days from the participant’s one-week diet record were selected and the weight of each item adjusted to meet this daily target kcal value whilst maintaining the overall relative proportions so that participants’ typical diet composition remained unaltered. During the 3-day intervention period, participants were asked to record the timing of each food/fluid intake within the prescribed food diary and to confirm that they had consumed the correct amount of food to help improve compliance with diet instructions. Participants were asked not to make any conscious changes to habitual physical activity during the calorie restriction period.
Sample collection days
Participants were asked not to perform any strenuous physical activity for 48 hr and to refrain from consuming caffeine/alcohol for 24 hr before both trial days (i.e., pre- and post-intervention). Trial days were scheduled so participants had been free from any self-reported illness for a minimum of 2 weeks in order to reduce immune system disturbance. On both main trial days, participants arrived at the Physiology Resting Laboratory in the morning following a 10 hr fast (approximately 8 am) and after consuming 1 pint of water upon waking. Participants arrived in the laboratory at the same time on both trial days. Measurements of height, waist circumferences and body mass (post- void using a digital balance; TANITA corp.) were determined on both trial days. Participants’ body composition were characterised at baseline using dual energy X-ray absorptiometry (DEXA; Discovery, Hologic; Bedford, UK) and estimates of total and central fat mass [L1-L4; (Glickman et al., 2004)] and FMI (Kelly et al., 2009) determined.
Blood and adipose sampling
A cannula was inserted into an antecubital forearm vein and blood sample(s) taken for isolation of peripheral blood mononuclear cells (PBMCs) by density gradient separation (Lympholyte; Cedarlane Laboratories Ltd.; Ontario, Canada) and analysis of plasma and serum metabolic/inflammatory markers (Travers et al., 2015). Subcutaneous adipose tissue samples (~1 g) were obtained under local anaesthetic (1 % lidocaine) approximately 5 cm lateral to the umbilicus using a ‘needle aspiration’ technique (Walhin et al., 2013). Approximately 100 mg whole adipose tissue was transferred to an RNase/DNase free sterile centrifuge tube, homogenised in Trizol reagent (Invitrogen; MA, USA) and frozen on dry ice for later RNA isolation. The remainder was used for adipose tissue culture [explants cultured for 3 hr at a concentration of 100 mg/ mL (Fain et al., 2004)], and preparation of the stromavascular fraction (SVF), both described previously (Travers et al., 2015, Trim et al., 2021). Due to the limited size of some adipose tissue samples, priority was given to preparing tissue for analysis of SVF to address the main aim of this study (n = 12). Lowest priority was given to gene expression analysis. Paired samples (pre- and post-calorie restriction) were available for 9 participants for explant secretion analysis and 7 participants for gene expression analysis.
Oral glucose tolerance test
Participants were asked to consume a glucose drink consisting of 75 g anhydrous glucose (maltodextrin) solution (Polycal, Nutricia; Wiltshire, UK) and cannula blood samples were taken every 15 mins for the following 2 hr for measurement of plasma glucose and serum insulin concentrations.
Analysis of SVF and PBMCs by flow cytometry
Flow cytometry (using the FACSverse, BD; NJ, USA) was used to identify CD4+/CD8+ T-lymphocytes (CD45+CD3+ cells) in SVF and PBMCs together with respective levels of activation. Due to the limited size of the SVF samples for analysis, cells were labelled using a single antibody cocktail comprising; CD4-FITC, CD8-PE-Cy7, CD69-APC, CD25-APC-Cy7, CD3-V450 and CD45-V500 (BD; USA). PBMCs were labelled using; CD45-V500, CD3-V450, CD295-FITC, CD220-PE, CD4-PerCP, CD8-PE-Cy7, CD69-APC and CD25-APC-Cy7 (BD; USA).
RT-PCR
Total RNA was extracted from whole adipose tissue, quantified and 1 μg reverse transcribed to cDNA as described previously (Travers et al., 2015). Real-time PCR was performed using a StepOneTM (Applied Biosystems; MA, USA) with pre-designed primers and probes obtained from Applied Biosystems for measurement of LEPTIN (Hs00174877_m1), ADIPONECTIN (Hs00605917_m1), GLUT4 (Hs00168966_m1), IRS2 (Hs00275843_s1), HSL (Hs00193510_m1), LPL (Hs01012567_m1), PPARγ (Hs01115513_m1), MCP1 (Hs00234140_m1), IL6 (Hs00985639_m1), IL8 (Hs99999034_m1), IL1Ra (Hs00893626_m1), and IL18 (Hs00155517_m1) expression. Peptidylpropyl isomerase A (PPIA) was used as an endogenous control (Neville et al., 2011). Results were analysed using the comparative Ct method and expression normalized to an internal calibrator specific to each gene using the formula 2- ∆∆CT; where ∆∆CT is [(CT gene of interest – CT PPIA) – lowest ∆CT for gene of interest] and statistical analysis performed on LN-transformed values (Livak and Schmittgen, 2001).
Biochemical analysis
Plasma glucose, serum total cholesterol, HDL cholesterol, triglycerides and CRP concentrations and ALT activity were measured using commercially available assay kits and analyser (Daytona Rx, Randox; Crumlin, UK). ELISA was used for the measurement of serum Insulin concentrations (Mercodia; Uppsala, Sweden), and both serum and adipose tissue Leptin and Adiponectin secretion (R&D systems; MN, USA). A fluorescent bead multiplex system (Luminex, BIO-RAD; CA, USA) was used for the measurement of serum and adipose tissue secretion of TNF, IL-10, IL-8, MCP-1, IL-1a, IL-1Ra, IL-18, IL-6, G-CSF, IP-10, MIP-1β, and M-CSF. Serum IL-6, G-CSF, M-CSF, IL-1a, and IL1Ra were detectable in fewer than 3 individuals so results were not included in statistical analysis. TNF and IL-10 were not detectable in either serum or adipose culture supernatant.
Statistical analysis
Estimates of glucose control were calculated using homeostasis model assessment for insulin resistance [HOMA-IR; (Matthews et al., 1985)] and insulin sensitivity index [ISI comp/ Matsuda index; (Matsuda and DeFronzo, 1999)]. LDL cholesterol was calculated using the Friedewald equation (Friedewald et al., 1972) Comparisons were made between pre- and post- calorie restriction values using two-tailed, paired t-tests, or non-parametric equivalents where data were non-normally distributed (Shapiro-Wilks p>0.05). Correlation analysis was performed using Pearson’s r. Statistical analysis was performed using GraphPad Prism 9.1.2 (GraphPad Software, LLC.; FL, USA). Effect sizes were calculated using Cohen’s d. p≤0.05 was considered to be statistically significant.
Citations:
FAIN, J. N., MADAN, A. K., HILER, M. L., CHEEMA, P. & BAHOUTH, S. W. 2004. Comparison of the release of adipokines by adipose tissue, adipose tissue matrix, and adipocytes from visceral and subcutaneous abdominal adipose tissues of obese humans. Endocrinology, 145, 2273-82.
FRAYN, K. N. 1983. Calculation of substrate oxidation rates in vivo from gaseous exchange. J Appl Physiol Respir Environ Exerc Physiol, 55, 628-34.
FRIEDEWALD, W. T., LEVY, R. I. & FREDRICKSON, D. S. 1972. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem, 18, 499-502.
GLICKMAN, S. G., MARN, C. S., SUPIANO, M. A. & DENGEL, D. R. 2004. Validity and reliability of dual-energy X-ray absorptiometry for the assessment of abdominal adiposity. J Appl Physiol (1985), 97, 509-14.
KELLY, T. L., WILSON, K. E. & HEYMSFIELD, S. B. 2009. Dual energy X-Ray absorptiometry body composition reference values from NHANES. PLoS One, 4, e7038.
LEAN, M. E., HAN, T. S. & MORRISON, C. E. 1995. Waist circumference as a measure for indicating need for weight management. BMJ, 311, 158-61.
LIVAK, K. J. & SCHMITTGEN, T. D. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods, 25, 402-8.
MARS, M., DE GRAAF, C., DE GROOT, C. P., VAN ROSSUM, C. T. & KOK, F. J. 2006. Fasting leptin and appetite responses induced by a 4-day 65%-energy-restricted diet. Int J Obes (Lond), 30, 122-8.
MATSUDA, M. & DEFRONZO, R. A. 1999. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care, 22, 1462-70.
MATTHEWS, D. R., HOSKER, J. P., RUDENSKI, A. S., NAYLOR, B. A., TREACHER, D. F. & TURNER, R. C. 1985. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia, 28, 412-9.
NEVILLE, M. J., COLLINS, J. M., GLOYN, A. L., MCCARTHY, M. I. & KARPE, F. 2011. Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization. Obesity (Silver Spring), 19, 888-92.
POSLUSNA, K., RUPRICH, J., DE VRIES, J. H., JAKUBIKOVA, M. & VAN'T VEER, P. 2009. Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice. Br J Nutr, 101 Suppl 2, S73-85.
STEVENS, J., TRUESDALE, K. P., MCCLAIN, J. E. & CAI, J. 2006. The definition of weight maintenance. Int J Obes (Lond), 30, 391-9.
THOMPSON, D., BATTERHAM, A. M., BOCK, S., ROBSON, C. & STOKES, K. 2006. Assessment of low-to-moderate intensity physical activity thermogenesis in young adults using synchronized heart rate and accelerometry with branched-equation modeling. J Nutr, 136, 1037-42.
TRAVERS, R. L., MOTTA, A. C., BETTS, J. A., BOULOUMIE, A. & THOMPSON, D. 2015. The impact of adiposity on adipose tissue-resident lymphocyte activation in humans. Int J Obes (Lond), 39, 762-9.
TRIM, W. V., WALHIN, J.-P., KOUMANOV, F., BOULOUMIÉ, A., LINDSAY, M. A., CHEN, Y.-C., TRAVERS, R. L., TURNER, J. E. & THOMPSON, D. 2021. Divergent immunometabolic changes in adipose tissue and skeletal muscle with ageing in healthy humans. The Journal of Physiology.
WALHIN, J. P., RICHARDSON, J. D., BETTS, J. A. & THOMPSON, D. 2013. Exercise counteracts the effects of short-term overfeeding and reduced physical activity independent of energy imbalance in healthy young men. J Physiol, 591, 6231-43.N/AGraphPad Prism 9.1.2 (GraphPad Software, LLC.; FL, USA)All data are laid out in columns, with participants in rows
Dataset for "Resonance-Induced Anomalies in Temperature-Dependent Raman Scattering of PdSe2"
No full textThe dataset contains the inputs necessary to reproduce the theoretical calculations presented in the associated paper, the abstract of which is as follows:
We report a comprehensive Raman study of the phonon behaviour in bulk and trilayer PdSe2 in the temperature range 5 K-300 K. In the bulk, a remarkable change in the Raman spectrum was observed at 120 K: a significant enhancement of the out-of-plane phonon A1g mode, accompanied by a suppression of the in-plane A2g and B21g modes. This intriguing behavior is attributed to a temperature-dependent resonant excitation effect. Our findings are corroborated by density functional theory (DFT) calculations which confirm an anisotropic electron-phonon coupling related to the relevant optical transitions. Furthermore, nonlinear frequency shifts were identified in all modes, indicating the decay of an optical phonon into multiple optical-acoustic phonons. The study of the Raman emission reported here, complemented by linear optical spectroscopies, bring out a new and unexpected scenario for the vibrational properties of PdSe2 that holds substantial promise for advanced thermoelectric and optical device applications.The input files are intended for use with VASP and Quantum Espresso open-source density functional theory codes with pseudopotentials generated by the "atomic" code included with QE and contained in PSlibrary or included within VASP.
The Phonopy code was used to generate atomic displacements corresponding to the phonon eigenmodes.The VASP subfolder contains the inputs needed to generate the electronic band structure of PdSe2 and the phonons at the Gamma point. The Phonopy code was used in interactive mode to generate force constants, phonon eignemodes, and displaced structures which were then used to calculate modulated electronic band structures. The QE subfolder "Raman tensor" contains the inputs needed to calculate the phonon frequencies and displacements in Quantum Espresso along with the Raman tensors for PdSe2. The QE subfolder "EPW" contains the inputs required to calcualte the electron-phonon coupling as a function of phonon mode and wavevector via the EPW code
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