Development of a biosensor for urea assay based on amidase inhibition, using an ion-selective electrode

A biosensor for urea has been developed based on the observation that urea is a powerful active-site inhibitor of amidase, which catalyzes the hydrolysis of amides such as acetamide to produce ammonia and the corresponding organic acid. Cell-free extract from Pseudomonas aeruginosa was the source of amidase (acylamide hydrolase, EC 3.5.1.4) which was immobilized on a polyethersulfone membrane in the presence of glutaraldehyde; anion-selective electrode for ammonium ions was used for biosensor development. Analysis of variance was used for optimization of the biosensorresponse and showed that 30 mu L of cell-free extract containing 7.47 mg protein mL(-1), 2 mu L of glutaraldehyde (5%, v/v) and 10 mu L of gelatin (15%, w/v) exhibited the highest response. Optimization of other parameters showed that pH 7.2 and 30 min incubation time were optimum for incubation ofmembranes in urea. The biosensor exhibited a linear response in the range of 4.0-10.0 mu M urea, a detection limit of 2.0 mu M for urea, a response timeof 20 s, a sensitivity of 58.245 % per mu M urea and a storage stability of over 4 months. It was successfully used for quantification of urea in samples such as wine and milk; recovery experiments were carried out which revealed an average substrate recovery of 94.9%. The urea analogs hydroxyurea, methylurea and thiourea inhibited amidase activity by about 90%, 10% and 0%, respectively, compared with urea inhibition

Mifepristone Prevents Stress-Induced Apoptosis in Newborn Neurons and Increases AMPA Receptor Expression in the Dentate Gyrus of C57/BL6 Mice

Chronic stress produces sustained elevation of corticosteroid levels, which is why it is considered one of the most potent negative regulators of adult hippocampal neurogenesis (AHN). Several mood disorders are accompanied by elevated glucocorticoid levels and have been linked to alterations in AHN, such as major depression (MD). Nevertheless, the mechanism by which acute stress affects the maturation of neural precursors in the dentate gyrus is poorly understood. We analyzed the survival and differentiation of 1 to 8 week-old cells in the dentate gyrus of female C57/BL6 mice following exposure to an acute stressor (the Porsolt or forced swimming test). Furthermore, we evaluated the effects of the glucocorticoid receptor (GR) antagonist mifepristone on the cell death induced by the Porsolt test. Forced swimming induced selective apoptotic cell death in 1 week-old cells, an effect that was abolished by pretreatment with mifepristone. Independent of its antagonism of GR, mifepristone also induced an increase in the percentage of 1 week-old cells that were AMPA+. We propose that the induction of AMPA receptor expression in immature cells may mediate the neuroprotective effects of mifepristone, in line with the proposed antidepressant effects of AMPA receptor potentiators

The Communication of Corporate-NGO Partnerships: Analysis of Sainsbury’s Collaboration with Comic Relief.

This study focuses on CSR communication using the example of Corporate-NGO partnership between British supermarket chain Sainsbury’s and Comic Relief. Questionnaires were distributed to 40 participants asking them about their consumer behaviour and opinion on partnerships. Using thematic analysis, two main themes have been identified in the data set: some consumers are sceptical towards cross sector partnerships because they assume selfish reasons behind the collaboration and view them as corporate PR tool. On the other hand, the majority of consumers evaluate Corporate-NGO Partnerships as appropriate and a gain for society at large. The analysis showed that Sainsbury’s customers know about the partnership with Comic Relief while non-customers lack awareness, and that the most successful means of communication of partnerships is the supermarket promotion

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation