QUBIC: the Q&U Bolometric Interferometer for Cosmology. A novel way to look at the polarized Cosmic Microwave Background

In this paper we describe QUBIC, an experiment that takes up the challenge posed by the detection of primordial gravitational waves with a novel approach, that combines the sensitivity of state-of-the art bolometric detectors with the systematic effects control typical of interferometers. The so-called ``self-calibration'' is a technique deeply rooted in the interferometric nature of the instrument and allows us to clean the measured data from instrumental effects. The first module of QUBIC is a dual band instrument (150 GHz and 220 GHz) that will be deployed in Argentina during Fall 2018

Simulations and performance of the QUBIC optical beam combiner

QUBIC, the Q & U Bolometric Interferometer for Cosmology, is a novel ground-based instrument that aims to measure the extremely faint B-mode polarisation anisotropy of the cosmic microwave background at intermediate angular scales (multipoles of l = 30 – 200). Primordial B-modes are a key prediction of Inflation as they can only be produced by gravitational waves in the very early universe. To achieve this goal, QUBIC will use bolometric interferometry, a technique that combines the sensitivity of an imager with the immunity to systematic effects of an interferometer. It will directly observe the sky through an array of back-to-back entry horns whose beams will be superimposed using a cooled quasioptical beam combiner. Images of the resulting interference fringes will be formed on two focal planes, each tiled with transition-edge sensors, cooled down to 320 mK. A dichroic filter placed between the optical combiner and the focal planes will select two frequency bands (centred at 150 GHz and 220 GHz), one frequency per focal plane. Polarization modulation will be achieved using a cold stepped half-wave plate (HWP) and polariser in front of the sky-facing horns. The full QUBIC instrument is described elsewhere; in this paper we will concentrate in particular on simulations of the optical combiner (an off-axis Gregorian imager) and the feedhorn array. We model the optical performance of both the QUBIC full module and a scaled-down technological demonstrator which will be used to validate the full instrument design. Optical modelling is carried out using full vector physical optics with a combination of commercial and in-house software. In the high-frequency channel we must be careful to consider the higher-order modes that can be transmitted by the horn array. The instrument window function is used as a measure of performance and we investigate the effect of, for example, alignment and manufacturing tolerances, truncation by optical components and off-axis aberrations. We also report on laboratory tests carried on the QUBIC technological demonstrator in advance of deployment to the observing site in Argentina

Convolutional Neural Networks for Water segmentation using Sentinel-2 Red, Green, Blue (RGB) composites and derived Spectral Indices

Near-real time water segmentation with medium resolution satellite imagery plays a critical role in water management. Automated water segmentation of satellite imagery has traditionally been achieved using spectral indices. Spectral water segmentation is limited by environmental factors and requires human expertise to be applied effectively. In recent years, the use of convolutional neural networks (CNN’s) for water segmentation has been successful when used on high-resolution satellite imagery, but to a lesser extent for medium resolution imagery. Existing studies have been limited to geographically localized datasets and reported metrics have been benchmarked against a limited range of spectral indices. This study seeks to determine if a single CNN based on Red, Green, Blue (RGB) image classification can effectively segment water on a global scale and outperform traditional spectral methods. Additionally, this study evaluates the extent to which smaller datasets (of very complex pattern, e.g harbour megacities) can be used to improve globally applicable CNNs within a specific region. Multispectral imagery from the European Space Agency, Sentinel-2 satellite (10 m spatial resolution) was sourced. Test sites were selected in Florida, New York, and Shanghai to represent a globally diverse range of waterbody typologies. Region-specific spectral water segmentation algorithms were developed on each test site, to represent benchmarks of spectral index performance. DeepLabV3-ResNet101 was trained on 33,311 semantically labelled true-colour samples. The resulting model was retrained on three smaller subsets of the data, specific to New York, Shanghai and Florida. CNN predictions reached a maximum mean intersection over union result of 0.986 and F1-Score of 0.983. At the Shanghai test site, the CNN’s predictions outperformed the spectral benchmark, primarily due to the CNN’s ability to process contextual features at multiple scales. In all test cases, retraining the networks to localized subsets of the dataset improved the localized region’s segmentation predictions. The CNN’s presented are suitable for cloud-based deployment and could contribute to the wider use of satellite imagery for water management

ANTI-STAPHYLOCOCCAL BIOFILM ACTIVITY OF NOVEL SORTASE A (SRTA) INHIBITORS

Pathogenic staphylococci have an extraordinary ability to form biofilms. This characteristic is likely the most important virulence factor of staphylococci in the development of the chronic form of infectious diseases and in biomaterial associated infections (BAI). Staphylococcal biofilms are particularly dangerous because they are more resistant to host immune defence system and have a significantly increased tolerance to conventional antibiotics. There is undoubtedly an urgent need for novel treatments, strategies and anti-staphylococcal biofilm agents. The Sortase A (SrtA) transpeptidase is responsible for covalent anchoring to the cell wall of various surface proteins (FnbpA, FnbpB, ClfA, ClfB, Protein A, etc.) that have a direct role in the pathogenesis and in the first stage of biofilm formation and because of this it can be considered a good target candidate to design agents that could interfere with virulence mechanism including biofilm formation. With the aim to discover new SrtA inhibitors, a library of 50000 low-molecular weight compounds was screened in a high throughput assay by using the standard Dabcyl-QALPETGEE-Edans fluorescence resonance energy transfer (FRET)- peptide substrate for measurement of enzyme activity. A group of the selected 38 most potent compounds and 3 known reference inhibitors were further evaluated in an in vitro biofilm formation assay at a screening concentration of 10\ub5g/ml using three reference staphylococcal strains S.aureus 29213, 6538 and S.epidermidis RP62A. An interesting correlation between inhibition of SrtA and biofilm formation inhibition was observed in many cases especially at a concentration equal or more than IC50 determined as SrtA inhibitors

Positron emission tomography (Pet) and neuroimaging in the personalized approach to neurodegenerative causes of dementia

Generally, dementia should be considered an acquired syndrome, with multiple possible causes, rather than a specific disease in itself. The leading causes of dementia are neurodegenerative and non-neurodegenerative alterations. Nevertheless, the neurodegenerative group of diseases that lead to cognitive impairment and dementia includes multiple possibilities or mixed pathologies with personalized treatment management for each cause, even if Alzheimer's disease is the most common pathology. Therefore, an accurate differential diagnosis is mandatory in order to select the most appropriate therapy approach. The role of personalized assessment in the treatment of dementia is rapidly growing. Neuroimaging is an essential tool for differential diagnosis of multiple causes of dementia and allows a personalized diagnostic and therapeutic protocol based on risk factors that may improve treatment management, especially in early diagnosis during the prodromal stage. The utility of structural and functional imaging could be increased by standardization of acquisition and analysis methods and by the development of algorithms for automated assessment. The aim of this review is to focus on the most commonly used tracers for differential diagnosis in the dementia field. Particularly, we aim to explore F-18 Fluorodeoxyglucose (FDG) and amyloid positron emission tomography (PET) imaging in Alzheimer's disease and in other neurodegenerative causes of dementia

Validating the regional estimates of changes in soil organic carbon by using the data from paired-sites: the case study of Mediterranean arable lands

BACKGROUND: Legacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use showed limitations due to the sampling schemes adopted and improvements may be needed in the analysis methodologies. When SOC changes is estimated with legacy data, the use of soil samples collected in different plots (i.e., non-paired data) may lead to biased results. In the present work, N = 302 georeferenced soil samples were selected from a regional (Sicily, south of Italy) soil database. An operational sampling approach was developed to spot SOC concentration changes from 1994 to 2017 in the same plots at the 0-30 cm soil depth and tested. RESULTS: The measurements were conducted after computing the minimum number of samples needed to have a reliable estimate of SOC variation after 23 years. By applying an effect size based methodology, 30 out of 302 sites were resampled in 2017 to achieve a power of 80%, and an α = 0.05. A Wilcoxon test applied to the variation of SOC from 1994 to 2017 suggested that there was not a statistical difference in SOC concentration after 23 years (Z = - 0.556; 2-tailed asymptotic significance = 0.578). In particular, only 40% of resampled sites showed a higher SOC concentration than in 2017. CONCLUSIONS: This finding contrasts with a previous SOC concentration increase that was found in 2008 (75.8% increase when estimated as differences of 2 models built with non-paired data), when compared to 1994 observed data (Z = - 9.119; 2-tailed asymptotic significance < 0.001). This suggests that the use of legacy data to estimate SOC concentration dynamics requires soil resampling in the same locations to overcome the stochastic model errors. Further experiment is needed to identify the percentage of the sites to resample in order to align two legacy datasets in the same area