A Boundary Element Method for Motions and Added Resistance of Ships in Waves

The accurate prediction of ship resistance in waves is nowadays of increased importance since it greatly influences ship performance regarding sustainable service speed and fuel consumption in seaways. Added resistance is considered as the longitudinal component of the second order mean force acting on a ship in waves and can be calculated from the first order ship motions by integrating the corresponding second-order pressure on the body surface. The purpose of this paper is to present a methodology for the prediction of motions and added resistance by a three dimensional Rankine panel method and to discuss and validate its results by comparing them with experimental data. The prediction in the short wave range, where forces due to wave reflection dominate, has been made applying semi-empirical corrections proposed by Kuroda. Experimental data for the heave, pitch, and added resistance of an ITTC benchmark KRISO container ship have been compared with numerical ones, and the applicability of the proposed method is discussed

Phi-Net: Deep Residual Learning for InSAR Parameters Estimation

Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and the enhancement of signals disrupted by the noise of different natures. In this article, we address the problem of the estimation of the interferometric parameters from synthetic aperture radar (SAR) data. In particular, we combine convolutional neural networks together with the concept of residual learning to define a novel architecture, named Phi-Net, for the joint estimation of the interferometric phase and coherence. Phi-Net is trained using synthetic data obtained by an innovative strategy based on the theoretical modeling of the physics behind the SAR acquisition principle. This strategy allows the network to generalize the estimation problem with respect to: 1) different noise levels; 2) the nature of the imaged target on the ground; and 3) the acquisition geometry. We then analyze the Phi-Net performance on an independent data set of synthesized interferometric data, as well as on real InSAR data from the TanDEM-X and Sentinel-1 missions. The proposed architecture provides better results with respect to state-of-the-art InSAR algorithms on both synthetic and real test data. Finally, we perform an application-oriented study on the retrieval of the topographic information, which shows that Phi-Net is a strong candidate for the generation of high-quality digital elevation models at a resolution close to the one of the original single-look complex data

Study protocol on advance care planning in multiple sclerosis (ConCure-SM): intervention construction and multicentre feasibility trial

Multiple sclerosis (MS) is the most common cause of progressive neurological disability in young adults. The use of advance care planning (ACP) for people with progressive MS (pwPMS) remains limited. The ConCure-SM project aims to assess the effectiveness of a structured ACP intervention for pwPMS. The intervention consists of a training programme on ACP for healthcare professionals caring for pwPMS, and a booklet to be used during the ACP conversation. Herein, we describe the first two project phases

Evaluation of virological response and resistance profile in HIV-1 infected patients starting a first-line integrase inhibitor-based regimen in clinical settings

Background: Virological response and resistance profile were evaluated in drug-naïve patients starting their first-line integrase inhibitors (INIs)-based regimen in a clinical setting. Study design: Virological success (VS) and virological rebound (VR) after therapy start were assessed by survival analyses. Drug-resistance was evaluated at baseline and at virological failure. Results: Among 798 patients analysed, 38.6 %, 27.1 % and 34.3 % received raltegravir, elvitegravir and dolutegravir, respectively. Baseline resistance to NRTIs, NNRTIs, PIs and INIs was: 3.9 %, 13.9 %, 1.6 % and 0.5 %, respectively. Overall, by 12 months of treatment, the probability of VS was 95 %, while the probability of VR by 36 months after VS was 13.1 %. No significant differences in the virological response were found according to the INI used. The higher pre-therapy viremia strata was (<100,000 vs. 100,000-500,000 vs. > 500,000 copies/mL), lower was the probability of VS (96.0 % vs. 95.2 % vs. 91.1 %, respectively, P < 0.001), and higher the probability of VR (10.2 % vs. 15.8 % vs. 16.6 %, respectively, P = 0.010). CD4 cell count <200 cell/mm3 was associated with the lowest probability of VS (91.5 %, P < 0.001) and the highest probability of VR (20.7 %, P = 0.008) compared to higher CD4 levels. Multivariable Cox-regression confirmed the negative role of high pre-therapy viremia and low CD4 cell count on VS, but not on VR. Forty-three (5.3 %) patients experienced VF (raltegravir: 30; elvitegravir: 9; dolutegravir: 4). Patients failing dolutegravir did not harbor any resistance mutation either in integrase or reverse transcriptase. Conclusions: Our findings confirm that patients receiving an INI-based first-line regimen achieve and maintain very high rates of VS in clinical practice

Translating land cover/land use classifications to habitat taxonomies for landscape monitoring: A Mediterranean assessment

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth observation (EO) data offer a potential solution, through direct or indirect mapping of species or habitats. Most national and international programs rely on the use of land cover (LC) and/or land use (LU) classification systems. Yet, these are not as clearly relatable to biodiversity in comparison to habitat classifications, and provide less scope for monitoring. While a conversion from LC/LU classification to habitat classification can be of great utility, differences in definitions and criteria have so far limited the establishment of a unified approach for such translation between these two classification systems. Focusing on five Mediterranean NATURA 2000 sites, this paper considers the scope for three of the most commonly used global LC/LU taxonomies—CORINE Land Cover, the Food and Agricultural Organisation (FAO) land cover classification system (LCCS) and the International Geosphere-Biosphere Programme to be translated to habitat taxonomies. Through both quantitative and expert knowledge based qualitative analysis of selected taxonomies, FAO-LCCS turns out to be the best candidate to cope with the complexity of habitat description and provides a framework for EO and in situ data integration for habitat mapping, reducing uncertainties and class overlaps and bridging the gap between LC/LU and habitats domains for landscape monitoring—a major issue for conservation. This study also highlights the need to modify the FAO-LCCS hierarchical class description process to permit the addition of attributes based on class-specific expert knowledge to select multi-temporal (seasonal) EO data and improve classification. An application of LC/LU to habitat mapping is provided for a coastal Natura 2000 site with high classification accuracy as a result

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

Abstract The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.Peer reviewe