Notulae to the Italian flora of algae, bryophytes, fungi and lichens: 14

In this contribution, new data concerning bryophytes, fungi and lichens of the Italian flora are presented. It includes new records and confirmations for the algal genus Chara, for the bryophyte genera Bryum, Grimmia, Cephaloziella, Hypnum, Nogopterium, Physcomitrium, Polytrichastrum, Rhynchostegiella, Saelania, and Schistostega, the fungal genera Cortinarius, Lentinellus, Omphalina, and Xerophorus, and the lichen genera Acarospora, Agonimia, Candelariella, Cladonia, Graphis, Gyalolechia, Hypogymnia, Lichinella, Megalaria, Nephroma, Ochrolechia, Opegrapha, Peltigera, Placidium, Ramalina, Rhizoplaca, Ropalospora, Strangospora, Toniniopsis, Usnea, and Zahlbrucknerell

Search for Multimessenger Sources of Gravitational Waves and High-energy Neutrinos with Advanced LIGO during Its First Observing Run, ANTARES, and IceCube

Astrophysical sources of gravitational waves, such as binary neutron star and black hole mergers or core-collapse supernovae, can drive relativistic outflows, giving rise to non-thermal high-energy emission. High-energy neutrinos are signatures of such outflows. The detection of gravitational waves and high-energy neutrinos from common sources could help establish the connection between the dynamics of the progenitor and the properties of the outflow. We searched for associated emission of gravitational waves and high-energy neutrinos from astrophysical transients with minimal assumptions using data from Advanced LIGO from its first observing run O1, and data from the Antares and IceCube neutrino observatories from the same time period. We focused on candidate events whose astrophysical origins could not be determined from a single messenger. We found no significant coincident candidate, which we used to constrain the rate density of astrophysical sources dependent on their gravitational-wave and neutrino emission processes

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Gestione di un progetto di sviluppo, introduzione e manutenzione di un sistema informativo per lo Shop Floor Control alla Prysmian Cavi e Sistemi Energia”

Progetto di analisi, sviluppo, manutenzione di un sistema informativo di tracciabilita' della produzione nel settore dell'Energia. Pianificazione di progetto,Analisi Requisiti di sistema, Progettazione del sistema e dei servizi ,Sviluppo e predisposizione Sistema, Realizzazione Documentazione, Formazione Utenza

Effectiveness of oral propranolol in a patient with neurofibromatosis type 1 and infantile hemangiomas

Effectiveness of oral propranolol in a patient with neurofibromatosis type 1 and infantile hemangiomas

Hepatocytes with extensive telomere deprotection and fusion remain viable and regenerate liver mass through endoreduplication

We report that mouse liver cells are highly resistant to extensive telomere dysfunction. In proliferating cells, telomere dysfunction results in chromosome end fusions, a DNA damage signal, and apoptosis or senescence. To determine the consequences of telomere dysfunction in noncycling cells, we used conditional deletion of the telomeric protein TRF2 in hepatocytes. TRF2 loss resulted in telomeric accumulation of γ-H2AX and frequent telomere fusions, indicating telomere deprotection. However, there was no induction of p53 or apoptosis, and liver function appeared unaffected. Furthermore, the loss of TRF2 did not compromise liver regeneration after partial hepatectomy. Remarkably, liver regeneration occurred without cell division involving endoreduplication and cell growth, thereby circumventing the chromosome segregation problems associated with telomere fusions. We conclude that nondividing hepatocytes can maintain and regenerate liver function despite substantial loss of telomere integrity