Seismic footprints of shallow dyke propagation at Etna, Italy

One of the key issues in forecasting volcanic eruptions is to detect signals that can track the propagation of dykes towards the surface. Continuous monitoring of active volcanoes helps significantly in achieving this goal. The seismic data presented here are unique, as they document surface faulting processes close (tens to a few hundred meters) to their source, namely the dyke tip. They originated nearby - and under - a seismic station that was subsequently destroyed by lava flows during eruptive activity at Etna volcano, Italy, in 2013. On February 20, a ~600 m-long and ~120 m wide NW-SE fracture field opened at an altitude between 2750 and 2900 m. The consequent rock dislocation caused the station to tilt and offset the seismic signal temporarily. Data acquisition continued until the arrival of the lava flow that led to the breakdown of the transmission system. Shallow ground fracturing and repeated low-frequency oscillations occurred during two stages in which the seismic signal underwent a maximum offset ~2.57 E+04 nm/s. Bridging instrumental recordings, fieldwork and conceptual modelling, these data are interpreted as the seismic footprints of a magmatic dyke intrusion that moved at speed ~0.02 m/s (first stage) and 0.46 m/s (second stage)

Molecular Mechanism Involved in the Pathogenesis of Early-Onset Epileptic Encephalopathy

Recent studies have shown that neurologic inflammation may both precipitate and sustain seizures, suggesting that inflammation may be involved not only in epileptogenesis but also in determining the drug-resistant profile. Extensive literature data during these last years have identified a number of inflammatory markers involved in these processes of "neuroimmunoinflammation" in epilepsy, with key roles for pro-inflammatory cytokines such as: IL-6, IL-17 and IL-17 Receptor (IL-17R) axis, Tumor-Necrosis-Factor Alpha (TNF-α) and Transforming-Growth-Factor Beta (TGF-β), all responsible for the induction of processes of blood-brain barrier (BBB) disruption and inflammation of the Central Nervous System (CNS) itself. Nevertheless, many of these inflammatory biomarkers have also been implicated in the pathophysiologic process of other neurological diseases. Future studies will be needed to identify the disease-specific biomarkers in order to distinguish epilepsies from other neurological diseases, as well as recognize different epileptic semiology. In this context, biological markers of BBB disruption, as well as those reflecting its integrity, can be useful tools to determine the pathological process of a variety of neurological diseases. However; how these molecules may help in the diagnosis and prognostication of epileptic disorders remains yet to be determined. Herein, authors present an extensive literature review on the involvement of both, systemic and neuronal immune systems, in the early onset of epileptic encephalopathy

Rockfall Episodes from Visual and Seismic Data Analysis at Stromboli Volcano, Italy

On 30 December, 2002, huge subaerial and submarine landslides (Bonaccorso et al., 2003) occurred at Stromboli volcano, Italy, two days after a renewal of the effusive activity. As a consequence of the landslides and concurrent tsunami waves, which threatened the safety of the inhabitants of the island (Pino et al., 2004), the attention of the scientific community has been drawn on sliding processes affecting the instable flanks of the Sciara del Fuoco in the western part of the volcano (e.g., Maiolino et al., 2004). We analyze rockfall episodes which have been continuing to occur despite the end of the lava effusion in July 2003. Particularly, we propose a comparative analysis of visual and seismic data recorded in 2004. Our data set encompasses records of the seismic network, along with concurrent visual images of permanent video cameras - in continuous acquisition - run by INGV, pointing from a site at 400 m above see level to the summit part of the volcano. Excluding night-time hours and days with bad weather conditions and/or when the vapor emission hindered the view, we find that only a few seismic traces refer to rockfall episodes which are not visible on the field. This finding allows us to explore the characteristics of the rockfalls in a new perspective, integrating visual and seismic data. Additionally, this comparative analysis sheds light on the sliding process, considering the material involved and possible cause-and-effect relationships with seismic-shaking and eruptive activity

Application of a multi­station alert method for short­-term forecasting of eruptions at Etna, Italy

From 11 January to 15 November 2011, 18 paroxysmal eruptions occurred at Etna, Italy. These events belong to a long sequence of eruptive episodes, which marked the prevalent explosive style of the volcano since the early 2000s. Applying “KKAnalysis”, a software for pattern classification that combines Self­Organizing Maps and fuzzy clustering, to the background seismic radiation (so-called volcanic tremor), we were able to detect critical changes in the spectral characteristics (amplitude and frequency content) at a very early stage of the volcano unrest. The online implementation for surveillance purposes of KKAnalysis provided automatic alert of the impending eruptive events from hours to a few days in advance. In its original version, the classifier analyzed the data stream continuously recorded at a single seismic station. By using offline a modified version of KKAnalysis, here we apply the software to the seismic signal recorded at 11 broadband stations in 2011. The seismic sensors were located at various distances (from 1 to 8 km) from the active craters. The continuous records and the optimal geometry of the seismic network offer us the possibility to track the spectral variations in time and space. We show the new results of pattern classification and propose a revised, more powerful multi­station alert method that now provides short­ term forecasting also in the form of animated maps that flag the detection of changes at each station. This allows us to observe how the unrest develops in various sectors of the volcano. We discuss the performance of the method and the robustness of the eruption forecasts in the context of the complex dynamics of a volcanic system such as Etna

Activity Regimes on Mt Etna inferred from Automatic Unsupervised

Mt Etna is among the best monitored basaltic volcano worldwide. High-quality, multidisciplinary data set are continuously available for around-the-clock surveillance. Seismic data sets cover decades long local recordings, obtained during different regimes of eruptive activity, from Strombolian eruptions to lava fountains and lava flows. Earthquakes swarms have often heralded effusive activity. However, volcanic tremor – the persistently radiated signal by the volcano - has proved to be a key indicator of impending eruptive activity. Changes in the volcano feeder show up in the signature of tremor, its spectral characteristics and source location. We apply a recently developed software for the analysis of volcanic tremor, combining Kohonen Maps along with Cluster and Fuzzy Analysis, in order to identify transitions from pre-eruptive to eruptive activity. Throughout the analysis of the data flow, the software provides an unsupervised classification of the spectral characteristics (i.e., amplitude and frequency content) of the signal, which is interpreted in the context of a specific state of the volcano. We present an application on the eruptive events occurred during the 2007-2009 time period, encompassing 7 episodes of lava fountaining, periodic Strombolian activity at the summit craters, and a lava emission on the upper east flank of the volcano, which started on 13 May 2008 and ended on 6 July 2009. In this time span the source of volcanic tremor was always shallow (less than 3 km), i. e., within the volcano edifice. From the analysis we conclude that the upraise of magma to the surface was fast, taking several hours to a few minutes. We discuss the possible reasons of such variability in the light of the characteristics of the overall seismicity preceding the eruptions in the study period, taking into account field observations and rheology of the ascending magma as well

Pattern classification of volcanic tremor data related to the 2007-2012 Mt. Etna (Italy) eruptive episodes

From March 2007 to April 2012 one of the main craters of Mt. Etna volcano, the South East Crater, was frequently active with spectacular, even though low dangerous, eruptions mainly in form of lava fountains. Thirty-three eruptive episodes occurred at that crater, encompassing thirty-two paroxysmal lava fountains (seven in 2007-2008 and twenty-five in 2011-2012), and a lava emission, started on 13 May 2008 and ended on 6 July 2009, along the upper eastern flank of the volcano. From the seismic point of view, the onset of all these eruptions was heralded by changes in the spectral characteristics of volcanic tremor recorded by digital broadband stations, which permanently monitor the volcanic region. On the basis of the tremor data collected between 2007 and 2009, some of us (Messina and Langer) developed a software which, combining unsupervised classification methods based on Kohonen Maps and the fuzzy cluster analysis, allows to identify transitions from pre-eruptive to eruptive activity through the classification of the tremor characteristics (i.e. amplitude and frequency content). Since 2010 an on-line version of this software is adopted at the Osservatorio Etneo as one of the automatic alerting tools to identify early stages of eruptive events. The software carries out the analysis of the continuous data stream of two key seismic stations, for which reference datasets were elaborated taking into account the tremor data recorded during the eruptive episodes from 2007 to 2009. The numerous paroxysmal eruptions occurred in 2011-2012 and the improved network density, in particular on the summit crater area, after 2009, lead us to extend the application of automatic volcanic tremor classification by using a larger number of stations at different elevation and distance from the summit craters. Datasets have been formed for the new stations, while for the previous key stations, the reference datasets were updated adding new patterns of the tremor signal. We discuss the performances of the classifier for the various stations in terms of timing of the early variations and spatial distribution of the stations

PRRT2 gene variant in a child with dysmorphic features, congenital microcephaly, and severe epileptic seizures: genotype-phenotype correlation?

BACKGROUND: Mutations in Proline-rich Transmembrane Protein 2 (PRRT2) have been primarily associated with individuals presenting with infantile epilepsy, including benign familial infantile epilepsy, benign infantile epilepsy, and benign myoclonus of early infancy, and/or with dyskinetic paroxysms such as paroxysmal kinesigenic dyskinesia, paroxysmal non-kinesigenic dyskinesia, and exercise-induced dyskinesia. However, the clinical manifestations of this disorder vary widely. PRRT2 encodes a protein expressed in the central nervous system that is mainly localized in the pre-synaptic neurons and is involved in the modulation of synaptic neurotransmitter release. The anomalous function of this gene has been proposed to cause dysregulation of neuronal excitability and cerebral disorders. CASE PRESENTATION: We hereby report on a young child followed-up for three years who presents with a spectrum of clinical manifestations such as congenital microcephaly, dysmorphic features, severe intellectual disability, and drug-resistant epileptic encephalopathy in association with a synonymous variant in PRRT2 gene (c.501C > T; p.Thr167Ile) of unknown clinical significance variant (VUS) revealed by diagnostic exome sequencing. CONCLUSION: Several hypotheses have been advanced on the specific role that PRRT2 gene mutations play to cause the clinical features of affected patients. To our knowledge, the severe phenotype seen in this case has never been reported in association with any clinically actionable variant, as the missense substitution detected in PRRT2 gene. Intriguingly, the same mutation was reported in the healthy father: the action of modifying factors in the affected child may be hypothesized. The report of similar observations could extend the spectrum of clinical manifestations linked to this mutation

Total Hemi-overgrowth in Pigmentary Mosaicism of the (Hypomelanosis of) Ito Type: Eight Case Reports.

Pigmentary mosaicism of the (hypomelanosis of) Ito type is an umbrella term, which includes phenotypes characterized by mosaic hypopigmentation in the form of streaks, whorls, patchy, or more bizarre skin configurations (running along the lines of Blaschko): these cutaneous patterns can manifest as an isolated skin disorder (pigmentary mosaicism of the Ito type) or as a complex malformation syndrome in association with extracutaneous anomalies (most often of the musculoskeletal and/or nervous systems) (hypomelanosis of Ito). Affected individuals are anecdotally reported to have also partial or total body hemi-overgrowth (HOG), which often causes moderate to severe complications.We studied the occurrence and features of HOG in the 114 children and adults with mosaic pigmentary disorders of the Ito type diagnosed and followed up (from 2 to 22 years; average follow-up 16 years) at our Institutions.Eight patients (5 M, 3 F; aged 4 to 25 years; median age 16 years) out of the 114 analyzed (7%) fulfilled the criteria for unilateral HOG, with differences in diameter ranging from 0.4 to 4.0 cm (upper limbs) and 1.0 to 9.0 cm (lower limbs). Moreover, among these 8 patients, 5/8 filled in the 75th to 90th percentile for height; 6/8 had associated kyphoscoliosis; and 5/8 showed cognitive delays. No tumour complications were recorded. Overall, 6/8 HOG patients presented with additional (extracutaneous) syndromic manifestations, apart from the HOG (ie, with a clinical phenotype of hypomelanosis of Ito).The present study, which includes children and adults with the longest follow-up so far recorded, confirms the association between pigmentary mosaicism of the Ito type and HOG lowering previous estimates (7% vs 16%) for HOG in the context of mosaic hypopigmentation. A careful examination, looking at subtle to moderate asymmetries and associated complications within the spectrum of these mosaic pigmentary disorders, is recommended

New inferences from spectral seismic energy measurement of a link between regional seismicity and volcanic activity at Mt. Etna, Italy

The existence of a relationship between regional seismicity and changes in volcanic activity has been the subject of several studies in the last years. Generally, activity in basaltic volcanoes such as Villarica (Chile) and Tungurahua (Ecuador) shows very little changes after the occurrence of regional earthquakes. In a few cases volcanic activity has changed before the occurrence of regional earthquakes, such as observed at Teide, Tenerife, in 2004 and 2005 (Tárraga et al., 2006). In this paper we explore the possible link between regional seismicity and changes in volcanic activity at Mt. Etna in 2006 and 2007. On 24 November, 2006 at 4:37:40 GMT an earthquake of magnitude 4.7 stroke the eastern coast of Sicily. The epicenter was localized 50 km SE of the south coast of the island, and at about 160 km from the summit craters of Mt. Etna. The SSEM (Spectral Seismic Energy Measurement) of the seismic signal at stations at 1 km and 6 km from the craters highlights that four hours before this earthquake the energy associated with volcanic tremor increased, reached a maximum, and finally became steady when the earthquake occurred. Conversely, neither before nor after the earthquake, the SSEM of stations located between 80 km and 120 km from the epicentre and outside the volcano edifice showed changes. On 5 September, 2007 at 21:24:13 GMT an earthquake of magnitude 3.2 and 7.9 km depth stroke the Lipari Island, at the north of Sicily. About 38 hours before the earthquake occurrence, there was an episode of lava fountain lasting 20 hours at Etna volcano. The SSEM of the seismic signal recorded during the lava fountain at a station located at 6 km from the craters highlights changes heralding this earthquake ten hours before its occurrence using the FFM method (e.g., Voight, 1988; Ortiz et al., 2003). A change in volcanic activity – with the onset of ash emission and Strombolian explosions – was observed a couple of hours before the occurrence of the regional earthquakes. It can be interpreted as the magmatic response to a change of the distribution of tectonic stress in the edifice before the earthquake. In the light of this hypothesis, we surmise that the magmatic system behaved similar to a dilatometer and promise news lines to forecasting the volcano activity

Complementary Methods for Volcanic Seismic Source Discrimination

ABSTRACT FINAL ID: V53E-2673 TITLE: Complementary Methods for Volcanic Seismic Source Discrimination SESSION TYPE: Poster SESSION TITLE: V53E. Surveillance of Volcanic Unrest: New Developments in Multidisciplinary Monitoring Methods IV Posters AUTHORS (FIRST NAME, LAST NAME): Charlotte A Rowe1, Susanna M R Falsaperla2, Emily Morton3, Horst K Langer2, Boris Behncke2 INSTITUTIONS (ALL): 1. Los Alamos Natl Lab, Los Alamos, NM, United States. 2. Istituto Nazionale di Geofisica e Volcanologia, Catania, Italy. 3. Earth and Environmental Sciences, New Mexico Institute of Mining and Technology, Socorro, NM, United States. Title of Team: ABSTRACT BODY: We explore the success rates of detection and classification algorithms as applied to seismic signals from active volcanoes. The subspace detection method has shown some success in identifying repeating (but not identical) signals from seismic swarm sources, as well as pulling out nonvolcanic long period events within subduction zone tremor. We continue the exploration of this technique as applied to both discrete events and variations within volcanic tremor to determine optimal situations for its use. We will demonstrate both three-dimensional and subband applications both on raw waveforms and derived features such as skewness and kurtosis. The application can be used in both a supervised (select templates and compare) as well as unsupervised (cross-compare all samples and apply clustering to the matrix of comparisons). We compare the method to that of the KKAnalysis tool, which uses a self-organizing map approach to unsupervised clustering for feature vectors derived from the seismic waveforms. We will present a comparison of this method as applied to waveform features, spectral features and time-varying higher-order statistics as well as signal polarization, to elucidate the tools which show the best promise for problematic discrimination tasks