The binaural MF SSR was proven to be a valid technique for the estimation of an objective audiogram, in a large sample of hearing-impaired children and normal-hearing subjects. With this method, frequency-specific thresholds at 0.5, 1, 2, and 4 kHz could be determined in all subjects (and both ears) with no appreciable loss in accuracy and a considerable reduction in testing time (average recording time = 21 minutes) when compared with other frequency-specific techniques.

In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turnaround time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers. However, these studies lack clinical control and rely on Internet users confirming their test results in a web questionnaire (crowdsourcing) thus rendering their analysis inadequate. We seek to evaluate the detection performance of a primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 Covid-19 negative) under quantitative RT-PCR (qRT-PCR) from certified laboratories. All collected samples were clinically labelled, i.e. Covid-19 positive or negative, according to the results in addition to the disease severity based on the qRT-PCR threshold cycle (Ct) and lymphocytes count from the patients. Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) for cough sound detection with subsequent classification based on a tensor of audio sonographs and deep artificial neural network classifier with convolutional layers called 'DeepCough'. Two different versions of DeepCough based on the number of tensor dimensions, i.e. DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform prototype web-app 'CoughDetect'.Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of 98.80% ± 0.83%, sensitivity of 96.43% ± 1.85%, and specificity of 96.20% ± 1.74% and average AUC of 81.08% ± 5.05% for the recognition of three severity levels. Our proposed web tool as a pointof-need primary diagnostic test for Covid-19 facilitates the rapid detection of the infection. We believe it has the potential to significantly hamper the Covid-19 pandemic across the world.

Gold/Palladium nanoparticles were fabricated by inert-gas condensation on a sputtering reactor. With this method, by controlling both the atmosphere on the condensation chamber and the magnetron power, it was possible to produce nanoparticles with a high degree of monodispersity in size. The structure and size of the Au/Pd nanoparticles were determined by mass spectroscopy, and confirmed by atomic force microscopy and electron transmission microscopy measurements. The chemical composition was analyzed by X-ray microanalysis. From these measurements we confirmed that with the sputtering technique we are able to produce particles of 1, 3, and 5 nm on size, depending on the choice of the synthesis conditions. From TEM measurements made both in the regular HREM, as well as in STEM-HAADF mode, we found that the particles are icosahedral in shape, and the micrographs show no evidence of a core-shell structure, in contrast to what is observed in the case of nanoparticles prepared by chemical synthesis.

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