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Distinguishing between bull Y- and X-bearing sperm populations is advantageous for techniques with sexed bull semen. The aim of this study was to produce a single-chain fragment variable (scFv) antibody against plasma membrane epitopes on bull Y-bearing sperm to distinguish between Y- and X- sperm. Variable heavy (VH) and variable light (VL) region genes generated from a hybridoma cell secreting a specific Y-bearing sperm monoclonal antibody (mAb-1F9) were cloned and expressed. The expected sizes of the DNA bands were ~350 bp for the VH gene and ~318 bp for the VL gene. The VH and VL genes were generated and used to construct an scFv gene (~650 bp) and express the corresponding soluble scFv antibody. Compared with the parent mAb-1F9, the scFv antibodies presented a high affinity for Y-bearing sperm and low cross-reactivity with X-bearing sperm. An immunofluorescence analysis confirmed that the scFv antibodies and mAb-1F9 recognize epitopes on the Y-bearing sperm surface. The fluorescence signal was strong on the plasma membrane of Y-bearing sperm but very weak for X-bearing sperm. This study helps the application and production of engineered scFv antibodies specific to Y-bearing sperm to distinguish between Y- and X-bearing sperm populations for techniques involving sexed bull semen
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Raw data of HIGD2A-BioID2 experiment. Total spectrum count for 3 independent experiments for HIGD2A-BioID2 and 3 independent experiments for the control BioID targeted to the mitochondria (MTS-BioID2).
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Data used for plotting figures in the main context. -- First commit: Nov 26th, 2019 for review. -- Second commit: Apt 1st, 2020 for publication
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1H-NMR spectroscopy data from 24-hr urine samples from Diets 1 and 4, Dietary Metabotype Score (DMS), blood glucose measurement and urinary calorific value. Data contains quantified values for identified metabolites (in mmol/ml) of 24-hr urine samples for 19 volunteers for the two reference diets in Excel format. For each sample, the DMS, area-under-the-curve (AUC) glucose and calorific value (in J/g) are also given. This data accompanies Garcia-Perez et al. (2020) "Dietary metabotype modelling predicts individual responses to dietary interventions: A feasibility study" Nature Food (submitted, ref.: NATFOOD-19060125C)
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Amyotrophic lateral sclerosis is a rapidly progressive neurodegeneration disease,with a hall mark of neuronal inclusions, neuron loss and gliosis. The pathogenesis of ALS remains unclear. And the only two drugs riluzole and edaravone exhibit limited efficacy. We then explored the riluzole treatment in TDP-43 transgenic rats trying to uncover the pathological mechanisms of neuron loss in ALS.
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We conducted a longitudinal study with a randomized control group design over a period of 14 days with 120 participants to investigate whether 3 different app-based interventions (cognitive-behavioural, meditation, informational) can enhance the general well-being (stress, engagement and satisfaction), ICT-specific well-being (technostress creators, digitalisation anxiety, IT resilience) and recovery (detachment) of participants compared to the control group with no intervention. All indicators were measured by using scales with several items in the initial questionnaire (prior to the intervention period) and end questionnaire (after the intervention period). Additionally, stress, satisfaction and detachment were measured by single items in the app-interventions which took place every two days directly after the interventions. The meditation intervention significantly increased general well-being (satisfaction, measured in the app) and recovery (detachment, measured in the questionnaires) compared to the control group but did not improve general stress and ICT-related stress. The cognitive-behavioural intervention significantly increased general well-being (less stress, measured in the app). Contrary to our hypotheses, the informational intervention even increased the general stress level (measured in the questionnaire). None of the interventions changed the level of ICT-related stress.
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The Excel File and Mat File contain the raw and transformed data that we used in our paper 'Financial Wealth, Investment and Sentiment in a Bayesian DSGE Model'.
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Data and code files for manuscript submitted to the Journal of Theoretical Biology.
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Hardware design for build a Step Width System Capture
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Open data and R analysis scripts for the paper as submitted for publication: "Poppelaars, E. S., Klackl, J., Scheepers, DT, Mühlberger, C., & Jonas, E. (2019). Reflecting on existential threats elicits self-reported negative affect but no physiological arousal." A dataset of 171 undergraduate students were randomly allocated to one of four existential threat conditions: mortality salience, freedom restriction, uncontrollability, and uncertainty; or to the non-existential threat condition: social-evaluative threat; or to a control condition (TV salience). Three facets of arousal were measured: positive and negative affect before and after reflection, subjective arousal during baseline and reflection, and physiological activation during baseline and reflection (electrodermal, cardiovascular, and respiratory), as well as personality traits (e.g. trait avoidance and approach, self-esteem). Description of files: - File 'README.txt' contains the description of the files (metadata). - File '20191024_IJMData_brief.sav' contains the raw data. - Files 'EXI.outl.del.RData' contains the complete dataset with missing values, with extra variables calculated, and with outliers deleted. - File 'Codebook_EXI.outl.del.csv' contains a description of all variables in the 'EXI.outl.del.RData' file (metadata). - Files 'EXI.outl.del.imp.RData' and 'EXI.outl.del.imp.extra.RData' contain multiple imputed datasets (without missing values) that can be used to reproduce results from the paper. - File '01_CalculationOfData.R' is an R analysis script that imports the raw data, calculates new variables, and imputes missing data via multiple imputation using the 'predictorMatrixAdj.xlsx' file. - File '02_AnalysisOfImputedData.R' is an R analysis script that calculates descriptive statistics, creates plots, and tests hypotheses using t-tests, Bayesian statistics, and multiple lineair regressions. Also uses the custom functions: 'BF.evidence.R', 'cohen.d.magnitude.R' and 'p.value.sig.R'.
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