Efficacy of somatostatine analogues in survival and quality of life of a frail patient with poorly differentiated biliary neuroendocrine tumour

We describe the case of a 71-year-old woman with poorly differentiated neuroendocrine tumor of third distal biliary duct and osteolytic metastasis. The patient was evaluated as a third stage of Balducci’s criteria for the recognition of frailty. The patient received radiotherapy and octreotide LAR. This treatment allowed a good tumour progression rate (18 months), a good quality of life and a good survival (35 months). The case report describes the role of octreotide in the therapy of neuroendocrine tumours, and underlines the importance of a multidisciplinary management of cancer in frail patients

Architecture and performance of the KM3NeT front-end firmware

The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine

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

Lexicography and language learning of Swahili L2 at UNIOR: the Swahili-Italian online dictionary project

The present essay outlines the progress of a lexicography project, namely a Swahili-Italian online dictionary, which was developed at the University of Naples “L’Orientale” (henceforth UNIOR) and conceived as a useful digital tool for Italian L1 learners of Swahili in a context of renewal of the teaching/learning of Swahili at the UNIOR, encouraged by web-based distance tools and platforms. As will be explained in detail, the software of the online lexical database, first developed in the period between 2003 and 2009, is in the process of being updated with some technical and lexicographical improvements, aiming for a resource that will be increasingly user-friendly to Swahili language students, and in general to Italian-speaking learners

Overexpression of Fgfr2c causes craniofacial bone hypoplasia and ameliorates craniosynostosis in the Crouzon mouse

FGFR2c regulates many aspects of craniofacial and skeletal development. Mutations in the FGFR2 gene are causative of multiple forms of syndromic craniosynostosis, including Crouzon syndrome. Paradoxically, mouse studies have shown that the activation (Fgfr2cC342Y; a mouse model for human Crouzon syndrome), as well as the removal (Fgfr2cnull), of the FGFR2c isoform can drive suture abolishment. This study aims to address the downstream effects of pathogenic FGFR2c signalling by studying the effects of Fgfr2c overexpression. Conditional overexpression of Fgfr2c (R26RFgfr2c;βact) results in craniofacial hypoplasia as well as microtia and cleft palate. Contrary to Fgfr2cnull and Fgfr2cC342Y, Fgfr2c overexpression is insufficient to drive onset of craniosynostosis. Examination of the MAPK/ERK pathway in the embryonic sutures of Fgfr2cC342Y and R26RFgfr2c;βact mice reveals that both mutants have increased pERK expression. The contrasting phenotypes between Fgfr2cC342Y and R26RFgfr2c;βact mice prompted us to assess the impact of the Fgfr2c overexpression allele on the Crouzon mouse (Fgfr2cC342Y), in particular its effects on the coronal suture. Our results demonstrate that Fgfr2c overexpression is sufficient to partially rescue craniosynostosis through increased proliferation and reduced osteogenic activity in E18.5 Fgfr2cC342Y embryos. This study demonstrates the intricate balance of FGF signalling required for correct calvarial bone and suture morphogenesis, and that increasing the expression of the wild-type FGFR2c isoform could be a way to prevent or delay craniosynostosis progression