Investigation of the Liverpool Bay mixing front using POLCOMS

Liverpool Bay, northwest UK, is a region of freshwater influence and hypertidal conditions. The river inflow from the 3 large estuary systems (Dee, Mersey and Ribble) forms a coastal front that moves < 10 km in response to semi-diurnal tidal straining and < 35 km due to the spring-neap cycle. The time variability of the density gradients in this coastal region are mainly controlled by salinity. Coastal observations are used in this study to improve the numerical simulation of the exchange process occurring at this front through improved spatial structure and temporal variability. A decade of Conductivity Temperature Depth (CTD) sensor observations were collected during cruises across a nearshore grid of monitoring stations. These data are used in addition to fixed mooring data that are near-continuous in time to validate numerical simulations using the 1-way nested Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS) at ~1.8km and 180m horizontal resolution. A downscaled simulation is used to investigate the influence of model resolution, inclusion of wetting and drying, diffusivity, turbulence advection and the influence of model boundary and initial conditions for select cruise periods in 2008. This year is chosen as a typical year with periods of calm and stormy conditions with variable river influence to investigate the seasonal frontal structure. A method to validate the spatial structure of the front is presented demonstrating the importance of a fine-resolution grid and improved physics to capture the details

Parameterization of intrawave ripple-averaged sediment pickup above steep ripples

Near-bed sediment pickup is critical for predictions of intrawave suspension and in turn net sediment transport in coastal models. In the present study, numerical results from a two-dimensional Reynolds-averaged Navier-Stokes model are used to assess the functional relationship of intrawave ripple-averaged sediment pickup above steep ripples. The numerical model provides intrawave time histories of ripple-averaged near-bed velocities and turbulence, which are qualitatively interrogated to determine pickup functional relationships. Several specific sediment pickup formulations are implemented within the numerical model: expressions relating pickup to near-bed velocity or near-bed turbulent kinetic energy via the bed shear stress; and expressions relating pickup to near-bed shear production of turbulent kinetic energy. These are then tested via model-data comparisons of near-bed suspended sediment concentration. The results show that the traditional functions relating sediment pickup to near-bed velocity cannot lead to reasonable intrawave suspension predictions above vortex ripples under a ripple-averaged framework. Instead, relating sediment pickup to near-bed turbulence quantities, such as turbulent kinetic energy or shear production of turbulent kinetic energy, significantly improves the numerical predictions for these conditions. This article is protected by copyright. All rights reserved

Towards Electrical, Integrated Implementations of SIMPL Systems

International audienceThis paper discusses strategies for the electrical, integrated implementation of a novel security tool termed SIMPL system, which was introduced in [1]. SIMPL systems are a public key version of Physical Unclonable Functions (PUFs). Like a PUF, each SIMPL system S is physically unique and non-reproducible, and implements an individual function FS. In opposition to a PUF, every SIMPL system S possesses a publicly known numerical description D(S), which allows its digital simulation and prediction. However, any such simulation must work at a detectably lower speed than the real-time behavior of S. As argued in [1], SIMPL systems have practicality and security advantages over PUFs, Certificates of Authenticity (COAs), Physically Obfuscated Keys (POKs), and also over standard mathematical cryptotechniques. This manuscript focuses on electrical, integrated realizations of SIMPL systems, and proposes two potential candidates: SIMPL systems derived from special SRAM-architectures (so-called "skew designs" of SRAM cells), and implementations based on analog computing arrays called Cellular Non-Linear Networks (CNNs)

Differentially expressed plasmatic microRNAs in Brazilian patients with Coronavirus disease 2019 (COVID-19): preliminary results

Background: Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is known that host microRNAs (miRNAs) can be modulated to favor viral infection or to protect the host. Herein, we report preliminary results of a study aiming at identifying differentially expressed plasmatic miRNAs in Brazilian patients with COVID-19. Methods and results: miRNAs were extracted from the plasma of eight patients with COVID-19 (four patients with mild COVID-19 and four patients with severe/critical COVID-19) and four healthy controls. Patients and controls were matched for sex and age. miRNA expression levels were detected using high-throughput sequencing. Differential miRNA expression and enrichment analyses were further evaluated. A total of 18 miRNAs were differentially expressed between patients with COVID-19 and controls. miR-4433b-5p, miR-6780b-3p, miR-6883-3p, miR-320b, miR-7111-3p, miR-4755-3p, miR-320c, and miR-6511a-3p were the most important miRNAs significantly involved in the PI3K/AKT, Wnt/β-catenin, and STAT3 signaling pathways. Moreover, 42 miRNAs were differentially expressed between severe/critical and mild patients with COVID-19. miR-451a, miR-101-3p, miR-185-5p, miR-30d-5p, miR-25-3p, miR-342-3p, miR-30e-5p, miR-150-5p, miR-15b-5p, and miR-29c-3p were the most important miRNAs significantly involved in the Wnt/β-catenin, NF-κβ, and STAT3 signaling pathways. Conclusions: If validated by quantitative real-time reverse transcriptase-polymerase chain reaction (RT-PCR) in a larger number of participants, the miRNAs identified in this study might be used as possible biomarkers for the diagnosis and severity of COVID-19