RF Power Amplifier Linearization in Professional Mobile Radio Communications Using Artificial Neural Networks

This paper is focused on the linearization of the radio frequency power amplifier of a professional digital handheld by means of an artificial neural network. The simplicity of the neural network that is used, together with the fact that a feedback path is unnecessary, makes this solution ideal to reduce both the cost of a handheld and its hardware complexity, while fully maintaining its performance. A compensation system is also needed to keep the linearization characteristics of the neural network stable against frequency, temperature, and voltage variations. The whole solution that comprises both the neural network and the compensation system has been implemented in the digital signal processor of a real handheld and afterward fully tested. It has proved to be satisfactory to meet the telecommunication standard requirements in all frequency, temperature, and voltage ranges under consideration while efficient to lower the computational cost of the handheld and to make its internal hardware simpler in comparison with other traditional linearization techniques. The results obtained demonstrate that a neural network can be used to linearize the power amplifiers that are used in transmitters of telecommunication equipment, leading to a significant reduction of both their hardware cost and complexity

High-Linearity Self-Biased CMOS Current Buffer

A highly linear fully self-biased class AB current buffer designed in a standard 0.18 mu m CMOS process with 1.8 V power supply is presented in this paper. It is a simple structure that, with a static power consumption of 48 mu W, features an input resistance as low as 89 Omega, high accuracy in the input-output current ratio and total harmonic distortion (THD) figures lower than -60 dB at 30 mu A amplitude signal and 1 kHz frequency. Robustness was proved through Monte Carlo and corner simulations, and finally validated through experimental measurements, showing that the proposed configuration is a suitable choice for high performance low voltage low power applications

Differences between familial and sporadic dilated cardiomyopathy: ESC EORP Cardiomyopathy & Myocarditis registry

Aims: Dilated cardiomyopathy (DCM) is a complex disease where genetics interplay with extrinsic factors. This study aims to compare the phenotype, management, and outcome of familial DCM (FDCM) and non-familial (sporadic) DCM (SDCM) across Europe. Methods and results: Patients with DCM that were enrolled in the prospective ESC EORP Cardiomyopathy & Myocarditis Registry were included. Baseline characteristics, genetic testing, genetic yield, and outcome were analysed comparing FDCM and SDCM; 1260 adult patients were studied (238 FDCM, 707 SDCM, and 315 not disclosed). Patients with FDCM were younger (P\ua0<\ua00.01), had less severe disease phenotype at presentation (P\ua0<\ua00.02), more favourable baseline cardiovascular risk profiles (P\ua0 64\ua00.007), and less medication use (P\ua0 64\ua00.042). Outcome at 1\ua0year was similar and predicted by NYHA class (HR 0.45; 95% CI [0.25\u20130.81]) and LVEF per % decrease (HR 1.05; 95% CI [1.02\u20131.08]. Throughout Europe, patients with FDCM received more genetic testing (47% vs. 8%, P\ua0<\ua00.01) and had higher genetic yield (55% vs. 22%, P\ua0<\ua00.01). Conclusions: We observed that FDCM and SDCM have significant differences at baseline but similar short-term prognosis. Whether modification of associated cardiovascular risk factors provide opportunities for treatment remains to be investigated. Our results also show a prevalent role of genetics in FDCM and a non-marginal yield in SDCM although genetic testing is largely neglected in SDCM. Limited genetic testing and heterogeneity in panels provides a scaffold for improvement of guideline adherence

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease

Permanent Genetic Resources added to Molecular Ecology Resources Database 1 October 2012-30 November 2012

This article documents the addition of 153 microsatellite marker loci to the Molecular Ecology Resources Database. Loci were developed for the following species: Brassica oleracea, Brycon amazonicus, Dimorphandra wilsonii, Eupallasella percnurus, Helleborus foetidus, Ipomoea purpurea, Phrynops geoffroanus, Prochilodus argenteus, Pyura sp., Sylvia atricapilla, Teratosphaeria suttonii, Trialeurodes vaporariorum and Trypanosoma brucei. These loci were cross-tested on the following species: Dimorphandra coccicinea, Dimorphandra cuprea, Dimorphandra gardneriana, Dimorphandra jorgei, Dimorphandra macrostachya, Dimorphandra mollis, Dimorphandra parviflora and Dimorphandra pennigera