Land degradation assessment for sustainable soil management

Desertification is a complex phenomenon defined as the extreme degree of land degradation induced by human activities and climatic conditions. Climate change is accelerating and widening these areas. Previews analysis and studies assessed the vulnerability to desertification in Italy at national and regional level through a methodological approach based on integrating climate, soil, vege-tation, and socio-economic data (ESA). The studies carried out by ISPRA aim to provide an update of the of land degradation assessment in Italy, based on Trends.Earth methodology and of the three UN-SDGs sub-indicators on Target 15.3.1 (land use/land cover, land productivity and soil organic carbon above and below ground status and trends), together with additional dimensions of land degradation considered crucial for national land characters. Final assessment of the percentage of degraded land is around 36% of national area. This exercise demonstrates the importance to con-sider a larger number of data and include information on other fac-tors, such as climate, physical, chemical data. This integrated approach to the assessment of land degradation will allow to describe also of the loss of related ecosystem services

A finite element analysis study from 3D CT to predict transcatheter heart valve thrombosis

Background: Transcatheter aortic valve replacement has proved its safety and effectiveness in intermediate- to high-risk and inoperable patients with severe aortic stenosis. However, despite current guideline recommendations, the use of transcatheter aortic valve replacement (TAVR) to treat severe aortic valve stenosis caused by degenerative leaflet thickening and calcification has not been widely adopted in low-risk patients. This reluctance among both cardiac surgeons and cardiologists could be due to concerns regarding clinical and subclinical valve thrombosis. Stent performance alongside increased aortic root and leaflet stresses in surgical bioprostheses has been correlated with complications such as thrombosis, migration and structural valve degeneration. Materials and Methods: Self-expandable catheter-based aortic valve replacement (Medtronic, Minneapolis, MN, USA), which was received by patients who developed transcatheter heart valve thrombosis, was investigated using high-resolution biomodelling from computed tomography scanning. Calcific blocks were extracted from a 250 CT multi-slice image for precise three-dimensional geometry image reconstruction of the root and leaflets. Results: Distortion of the stent was observed with incomplete cranial and caudal expansion of the device. The incomplete deployment of the stent was evident in the presence of uncrushed refractory bulky calcifications. This resulted in incomplete alignment of the device within the aortic root and potential dislodgment. Conclusion: A Finite Element Analysis (FEA) investigation can anticipate the presence of calcified refractory blocks, the deformation of the prosthetic stent and the development of paravalvular orifice, and it may prevent subclinical and clinical TAVR thrombosis. Here we clearly demonstrate that using exact geometry from high-resolution CT scans in association with FEA allows detection of persistent bulky calcifications that may contribute to thrombus formation after TAVR procedure

Time domain maximum likelihood parameter estimation in LISA Pathfinder Data Analysis

LISA is the upcoming space-based Gravitational Wave telescope. LISA Pathfinder, to be launched in the coming years, will prove and verify the detection principle of the fundamental Doppler link of LISA on a flight hardware identical in design to that of LISA. LISA Pathfinder will collect a picture of all noise disturbances possibly affecting LISA, achieving the unprecedented pureness of geodesic motion necessary for the detection of gravitational waves. The first steps of both missions will crucially depend on a very precise calibration of the key system parameters. Moreover, robust parameters estimation is of fundamental importance in the correct assessment of the residual force noise, an essential part of the data processing for LISA. In this paper we present a maximum likelihood parameter estimation technique in time domain being devised for this calibration and show its proficiency on simulated data and validation through Monte Carlo realizations of independent noise runs. We discuss its robustness to non-standard scenarios possibly arising during the real-life mission, as well as its independence to the initial guess and non-gaussianities. Furthermore, we apply the same technique to data produced in mission-like fashion during operational exercises with a realistic simulator provided by ESA.Comment: 16 pages (two columns), 15 figures, 5 tables, submitted to Phys. Rev.

Complete Healing of a Giant Wart in a Severely Immune-Compromised Patient with HIV Infection Treated with Acupuncture

Giant warts are infrequent dermatological viral infections caused by Papillomavirus (HPV) in immune-compromised patients. Treatment may often be difficult and unsatisfactory, either by surgery or cytotoxic agents, because of poor immune control of viral activity in such hosts. Here we report on the case of a patient with advanced and persistent immune suppression caused by HIV disease, who developed a monstrous wart covering the entirety of the radial district of his right hand. He was completely healed after a long treatment with traditional Chinese acupuncture, in spite of minimal immune recovery induced by efficacious antiretroviral therapy. To the best of our knowledge, therefore, the present report may be the first direct clinical evidence that acupuncture may be effective in the treatment of cutaneous warts also in HIV-infected patients

Parameter estimation in LISA Pathfinder operational exercises

The LISA Pathfinder data analysis team has been developing in the last years the infrastructure and methods required to run the mission during flight operations. These are gathered in the LTPDA toolbox, an object oriented MATLAB toolbox that allows all the data analysis functionalities for the mission, while storing the history of all operations performed to the data, thus easing traceability and reproducibility of the analysis. The parameter estimation methods in the toolbox have been applied recently to data sets generated with the OSE (Off-line Simulations Environment), a detailed LISA Pathfinder non-linear simulator that will serve as a reference simulator during mission operations. These operational exercises aim at testing the on-orbit experiments in a realistic environment in terms of software and time constraints. These simulations, so called operational exercises, are the last verification step before translating these experiments into tele-command sequences for the spacecraft, producing therefore very relevant datasets to test our data analysis methods. In this contribution we report the results obtained with three different parameter estimation methods during one of these operational exercises.Comment: 10 pages, 3 figures, prepared for the Proceedings of the 9th Edoardo Amaldi Conference on Gravitational Waves, JPC

Il consumo di suolo in Italia - Edizione 2015

Nel nostro Paese si continua a consumare suolo e la seconda edizione del Rapporto ISPRA fornisce un quadro completo sull’avanzata della copertura artificiale del nostro territorio. Il Rapporto sul consumo di suolo in Italia 2015 integra nuove informazioni, aggiorna le precedenti stime sulla base di dati a maggiore risoluzione e completa il quadro nazionale con specifici indicatori per regioni, province e comuni. Sono, inoltre, approfonditi alcuni aspetti che caratterizzano le dinamiche di espansione urbana e di trasformazione del paesaggio a scala nazionale e locale con riferimento alla fascia costiera, alle aree montane, ai corpi idrici, alle aree protette, alle aree a pericolosità idraulica, all’uso del suolo, alle forme e alle densità di urbanizzazione, ai fenomeni dello sprawl urbano, della frammentazione, della dispersione e della diffusione insediativa

Bayesian Model Selection for LISA Pathfinder

The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the eLISA concept. The data analysis team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment on-board LPF. These models are used for simulations, but more importantly, they will be used for parameter estimation purposes during flight operations. One of the tasks of the data analysis team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching this problem is to recover the essential parameters of a LTP model fitting the data. Thus, we want to define the simplest model that efficiently explains the observations. To do so, adopting a Bayesian framework, one has to estimate the so-called Bayes Factor between two competing models. In our analysis, we use three main different methods to estimate it: The Reversible Jump Markov Chain Monte Carlo method, the Schwarz criterion, and the Laplace approximation. They are applied to simulated LPF experiments where the most probable LTP model that explains the observations is recovered. The same type of analysis presented in this paper is expected to be followed during flight operations. Moreover, the correlation of the output of the aforementioned methods with the design of the experiment is explored

An Introduction to EEG Source Analysis with an illustration of a study on Error-Related Potentials

International audienceOver the last twenty years blind source separation (BSS) has become a fundamental signal processing tool in the study of human electroencephalography (EEG), other biological data, as well as in many other signal processing domains such as speech, images, geophysics and wireless communication (Comon and Jutten, 2010). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG, increasing the sensitivity and specificity of the signal received from the electrodes on the scalp. This chapter begins with a short review of brain volume conduction theory, demonstrating that BSS modeling is grounded on current physiological knowledge. We then illustrate a general BSS scheme requiring the estimation of second-order statistics (SOS) only. A simple and efficient implementation based on the approximate joint diagonalization of covariance matrices (AJDC) is described. The method operates in the same way in the time or frequency domain (or both at the same time) and is capable of modeling explicitly physiological and experimental source of variations with remarkable flexibility. Finally, we provide a specific example illustrating the analysis of a new experimental study on error-related potentials

Classification of P300 component using a riemannian ensemble approach

We present a framework for P300 ERP classification on the 2019 IFMBE competition dataset using a combination of a Riemannian geometry and ensemble learning. Covariance matrices and ERP prototypes are extracted after the EEG is passed through a filter bank and an ensemble of LDA classifiers is trained on subsets of channels, trials, and frequencies. The model selects a final class based on maximum probability of evidence from all ensembles. Our pipeline achieves an average classification accuracy of 81.2% on the test set