Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, we consider a high-order discontinuous Galerkin method while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, we estimate the material porosity and tortuosity while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirms the feasibility and accuracy of this approach

DeepTx: Deep Learning Beamforming with Channel Prediction

Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance.Comment: 27 pages, this work has been submitted to the IEEE for possible publication; v2: Fixed typo in author name, v3: a revisio

HybridDeepRx: Deep Learning Receiver for High-EVM Signals

In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural network receiver is devised, containing layers in both time- and frequency domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Extensive set of numerical results is provided, in the context of 5G NR uplink incorporating also measured terminal power amplifier characteristics. The obtained results show that the proposed receiver system is able to clearly outperform classical linear receivers as well as existing ML receiver approaches, especially when the EVM is high in comparison with modulation order. The proposed ML receiver can thus facilitate pushing the terminal power amplifier (PA) systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage.Comment: To be presented in the 2021 IEEE International Symposium on Personal, Indoor and Mobile Radio Communication

Fatal Outcome in Bacteremia is Characterized by High Plasma Cell Free DNA Concentration and Apoptotic DNA Fragmentation: A Prospective Cohort Study

INTRODUCTION: Recent studies have shown that apoptosis plays a critical role in the pathogenesis of sepsis. High plasma cell free DNA (cf-DNA) concentrations have been shown to be associated with sepsis outcome. The origin of cf-DNA is unclear. METHODS: Total plasma cf-DNA was quantified directly in plasma and the amplifiable cf-DNA assessed using quantitative PCR in 132 patients with bacteremia caused by Staphylococcus aureus, Streptococcus pneumoniae, ß-hemolytic streptococcae or Escherichia coli. The quality of cf-DNA was analyzed with a DNA Chip assay performed on 8 survivors and 8 nonsurvivors. Values were measured on days 1-4 after positive blood culture, on day 5-17 and on recovery. RESULTS: The maximum cf-DNA values on days 1-4 (n = 132) were markedly higher in nonsurvivors compared to survivors (2.03 vs 1.26 ug/ml, p<0.001) and the AUCROC in the prediction of case fatality was 0.81 (95% CI 0.69-0.94). cf-DNA at a cut-off level of 1.52 ug/ml showed 83% sensitivity and 79% specificity for fatal disease. High cf-DNA (>1.52 ug/ml) remained an independent risk factor for case fatality in a logistic regression model. Qualitative analysis of cf-DNA showed that cf-DNA displayed a predominating low-molecular-weight cf-DNA band (150-200 bp) in nonsurvivors, corresponding to the size of the apoptotic nucleosomal DNA. cf-DNA concentration showed a significant positive correlation with visually graded apoptotic band intensity (R = 0.822, p<0.001). CONCLUSIONS: Plasma cf-DNA concentration proved to be a specific independent prognostic biomarker in bacteremia. cf-DNA displayed a predominating low-molecular-weight cf-DNA band in nonsurvivors corresponding to the size of apoptotic nucleosomal DNA

Nigrostriatal 6-hydroxydopamine lesions increase alpha-synuclein levels and permeability in rat colon

Increasing evidence suggests that the gut-brain axis plays a crucial role in Parkinson's disease (PD). The abnormal accumulation of aggregated alpha-synuclein (aSyn) in the brain is a key pathological feature of PD. Intracerebral 6-hydroxydopamine (6-OHDA) is a widely used dopaminergic lesion model of PD. It exerts no aSyn pathology in the brain, but changes in the gut have not been assessed. Here, 6-OHDA was administered unilaterally either to the rat medial forebrain bundle (MFB) or striatum. Increased levels of glial fibrillary acidic protein in the ileum and colon were detected at 5 weeks postlesion. 6-OHDA decreased the Zonula occludens protein 1 barrier integrity score, suggesting increased colonic permeability. The total aSyn and Ser129 phosphorylated aSyn levels were elevated in the colon after the MFB lesion. Both lesions generally increased the total aSyn, pS129 aSyn, and ionized calcium-binding adapter molecule 1 (Iba1) levels in the lesioned striatum. In conclusion, 6-OHDA-induced nigrostriatal dopaminergic damage leads to increased aSyn levels and glial cell activation particularly in the colon, suggesting that the gut-brain axis interactions in PD are bidirectional and the detrimental process may start in the brain

Pentraxin 3 (PTX3) Is Associated with Severe Sepsis and Fatal Disease in Emergency Room Patients with Suspected Infection: A Prospective Cohort Study

Background Early diagnostic and prognostic stratification of patients with suspected infection is a difficult clinical challenge. We studied plasma pentraxin 3 (PTX3) upon admission to the emergency department in patients with suspected infection. Methods The study comprised 537 emergency room patients with suspected infection: 59 with no systemic inflammatory response syndrome (SIRS) and without bacterial infection (group 1), 67 with bacterial infection without SIRS (group 2), 54 with SIRS without bacterial infection (group 3), 308 with sepsis (SIRS and bacterial infection) without organ failure (group 4) and 49 with severe sepsis (group 5). Plasma PTX3 was measured on admission using a commercial solid-phase enzyme-linked immunosorbent assay (ELISA). Results The median PTX3 levels in groups 1–5 were 2.6 ng/ml, 4.4 ng/ml, 5.0 ng/ml, 6.1 ng/ml and 16.7 ng/ml, respectively (p<0.001). The median PTX3 concentration was higher in severe sepsis patients compared to others (16.7 vs. 4.9 ng/ml, p<0.001) and in non-survivors (day 28 case fatality) compared to survivors (14.1 vs. 5.1 ng/ml, p<0.001). A high PTX3 level predicted the need for ICU stay (p<0.001) and hypotension (p<0.001). AUCROC in the prediction of severe sepsis was 0.73 (95% CI 0.66–0.81, p<0.001) and 0.69 in case fatality (95% CI 0.58–0.79, p<0.001). PTX3 at a cut-off level for 14.1 ng/ml (optimal cut-off value for severe sepsis) showed 63% sensitivity and 80% specificity. At a cut-off level 7.7 ng/ml (optimal cut-off value for case fatality) showed 70% sensitivity and 63% specificity in predicting case fatality on day 28.In multivariate models, high PTX3 remained an independent predictor of severe sepsis and case fatality after adjusting for potential confounders. Conclusions A high PTX3 level on hospital admission predicts severe sepsis and case fatality in patients with suspected infection.Public Library of Science open acces