Spontaneous intracranial hypotension : two steroid-responsive cases

Purpose: Spontaneous intracranial hypotension (SIH) is characterised by orthostatic headache, low cerebrospinal fluid pressure and diffuse pachymeningeal enhancement after intravenous gadolinium contrast administration. Magnetic resonance imaging (MRI) often plays a crucial role for correct diagnosis. Case description: We described two similar cases of SIH, whose clinical and imaging features are typical for this pathology. At MRI brain scan, both patients showed diffuse and intense pachymeningeal enhancement and moderate venous distension and epidural vein engorgement. The two patients were treated with bed rest and oral steroid therapy, with complete and long-lasting symptomatic relief. Conclusions: Orthostatic nature of headache is the most indicative clinical feature suggesting SIH; contrast-enhanced MRI provides definite imaging diagnostic findings. Conservative treatment coupled to steroid therapy is often sufficient to obtain complete disappearance of symptoms

Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments

Predictive Maintenance (PdM) is the newest strategy for maintenance management in industrial contexts. It aims to predict the occurrence of a failure to minimize unexpected downtimes and maximize the useful life of components. In data-driven approaches, PdM makes use of Machine Learning (ML) algorithms to extract relevant features from signals, identify and classify possible faults (diagnostics), and predict the components’ remaining useful life (prognostics). The major challenge lies in the high complexity of industrial plants, where both operational conditions change over time and a large number of unknown modes occur. A solution to this problem is offered by novelty detection, where a representation of the machinery normal operating state is learned and compared with online measurements to identify new operating conditions. In this paper, a systematic study of autoencoder-based methods for novelty detection is conducted. We introduce an architecture template, which includes a classification layer to detect and separate the operative conditions, and a localizer for identifying the most influencing signals. Four implementations, with different deep learning models, are described and used to evaluate the approach on data collected from a test rig. The evaluation shows the effectiveness of the architecture and that the autoencoders outperform the current baselines

Exploring Marine Environments for the Identification of Extremophiles and Their Enzymes for Sustainable and Green Bioprocesses

Sea environments harbor a wide variety of life forms that have adapted to live in hard and sometimes extreme conditions. Among the marine living organisms, extremophiles represent a group of microorganisms that attract increasing interest in relation to their ability to produce an array of molecules that enable them to thrive in almost every marine environment. Extremophiles can be found in virtually every extreme environment on Earth, since they can tolerate very harsh environmental conditions in terms of temperature, pH, pressure, radiation, etc. Marine extremophiles are the focus of growing interest in relation to their ability to produce biotechnologically useful enzymes, the so-called extremozymes. Thanks to their resistance to temperature, pH, salt, and pollutants, marine extremozymes are promising biocatalysts for new and sustainable industrial processes, thus representing an opportunity for several biotechnological applications. Since the marine microbioma, i.e., the complex of microorganisms living in sea environments, is still largely unexplored finding new species is a central issue for green biotechnology. Here we described the main marine environments where extremophiles can be found, some existing or potential biotechnological applications of marine extremozymes for biofuels production and bioremediation, and some possible approaches for the search of new biotechnologically useful species from marine environments

The SEE-GeoForm WebGIS: a tool for seismic data and hazard analysis

The SEE-GeoForm project (Site Effects Evaluation - Geological Form) is born to share and make easily accessible via Internet seismic hazard data for Italy at different scales and for different administrative units (regions, provinces, municipalities), from existing database or new dataset carried out in this project. Using a WebGIS (http://www.seegeoform.it) a tool to archive, display and elaborate information has been developed. In particular, the website allows the user to query the basic and local seismic hazard values for single municipalities or to calculate those for any single point only by clicking on the maps. In order to make the WebGIS more flexible, the system has been fully implemented using open source technologies, based on the guidelines expressed by the Open Geospatial Consortium (OGC); in this way, it has been possible to develop some thematic modules for data elaborations and queries as integrated web services such as WMS, following all of the internationally-acknowledged best-practices in this field. The WebGIS has three frames: the data panel, the display area, the map layers directories. The data panel has several modules concerning respectively: basic and local hazard data for all Italian municipalities calculated by National Institute of Geophisics and Volcanology (INGV) or from other studies, such as horizontal peak ground acceleration values for different return periods (considering the exceedance probability in 50 years), and soil classes with the corresponding lithostratigraphic amplification factors according to the EuroCode8; a regular grid of 16.810 points, with a step equal to 0.05°, used by INGV for the seismic hazard elaborations (http://zonesismiche.mi.ingv.it/): values that are necessary to draw the site-dependent response spectra, according to the Italian seismic code, are linked to each point; the calculation on user demand of basic seismic hazard parameters for a site selected by clicking on geographical layers; composite seismogenic sources from DISS (Database of Individual Seismogenic Sources, vers. 3.1.1.: http://diss.rm.ingv.it/diss/), with their relative parameters (maximum moment magnitude, strike, dip, etc.). Finally, there are two modules regarding litoseismic classes and subsoil categories: the first one is linked to a map obtained by reclassifying the 46 litothypes of the Lithological Map of Italy at 100000-scale by Geological Survey of Italy (National Institute for Environmental Protection and Research - ISPRA) into 12 litoseismic classes, considered homogeneous regarding to their seismic behavior; while the second one permits to know the subsoil category, according with Italian seismic provisions (Norme Tecniche per le Costruzioni – NTC 2008), for a single point by clicking on the map. This has been possible by elaborating a subsoil categories map at 100000-scale derived from the litoseismic map at the same scale, by blending different litoseismic classes into 5 categories. Datasets have been built starting from 2007 within the ReLUIS Project (http://www.reluis.it) and are being improved within the 2009-2012 EUCENTRE project (http://www.eucentre.it), that partially financed the WebGIS development, as a result of the strong collaboration between researchers from INGV and ISPRA. The SEE-GeoForm web-tool aims to become the focal point to display in a simple way many databases containing information on seismic hazard of Italian territory, allowing user-friendly elaborations for researchers and professionals

Digital interaction: where are we going?

In the framework of the AVI 2018 Conference, the interuniversity center ECONA has organized a thematic workshop on "Digital Interaction: where are we going?". Six contributions from the ECONA members investigate different perspectives around this thematic