Unmanned Maritime Systems for Search and Rescue

The development of maritime unmanned tools for search and rescue operations is not a trivial task. A great part of maritime unmanned systems developed did not target such application, being more focused on environmental monitoring, surveillance or defence. In opposition to these applications, search and rescue operations need to take into account relevant issues such as the presence of people or other vessels on the water. Building upon user requirements and overall integrated components for assisted rescue and unmanned search operations (ICARUS) system architecture, this chapter addresses the development of unmanned maritime systems. It starts with an overview of the approach where a two‐tier solution was adopted to address safety issues and then proceeds to detail each of the developed technologies

Final results of the second prospective AIEOP protocol for pediatric intracranial ependymoma

BACKGROUND: This prospective study stratified patients by surgical resection (complete = NED vs incomplete = ED) and centrally reviewed histology (World Health Organization [WHO] grade II vs III). METHODS: WHO grade II/NED patients received focal radiotherapy (RT) up to 59.4 Gy with 1.8 Gy/day. Grade III/NED received 4 courses of VEC (vincristine, etoposide, cyclophosphamide) after RT. ED patients received 1-4 VEC courses, second-look surgery, and 59.4 Gy followed by an 8-Gy boost in 2 fractions on still measurable residue. NED children aged 1-3 years with grade II tumors could receive 6 VEC courses alone. RESULTS: From January 2002 to December 2014, one hundred sixty consecutive children entered the protocol (median age, 4.9 y; males, 100). Follow-up was a median of 67 months. An infratentorial origin was identified in 110 cases. After surgery, 110 patients were NED, and 84 had grade III disease. Multiple resections were performed in 46/160 children (28.8%). A boost was given to 24/40 ED patients achieving progression-free survival (PFS) and overall survival (OS) rates of 58.1% and 68.7%, respectively, in this poor prognosis subgroup. For the whole series, 5-year PFS and OS rates were 65.4% and 81.1%, with no toxic deaths. On multivariable analysis, NED status and grade II were favorable for OS, and for PFS grade II remained favorable. CONCLUSIONS: In a multicenter collaboration, this trial accrued the highest number of patients published so far, and results are comparable to the best single-institution series. The RT boost, when feasible, seemed effective in improving prognosis. Even after multiple procedures, complete resection confirmed its prognostic strength, along with tumor grade. Biological parameters emerging in this series will be the object of future correlatives and reports

Pediatric diffuse midline glioma H3K27- altered: A complex clinical and biological landscape behind a neatly defined tumor type

The 2021 World Health Organization Classification of Tumors of the Central Nervous System, Fifth Edition (WHO-CNS5), has strengthened the concept of tumor grade as a combination of histologic features and molecular alterations. The WHO-CNS5 tumor type “Diffuse midline glioma, H3K27-altered,” classified within the family of “Pediatric-type diffuse high-grade gliomas,” incarnates an ideally perfect integrated diagnosis in which location, histology, and genetics clearly define a specific tumor entity. It tries to evenly characterize a group of neoplasms that occur primarily in children and midline structures and that have a dismal prognosis. Such a well-defined pathological categorization has strongly influenced the pediatric oncology community, leading to the uniform treatment of most cases of H3K27-altered diffuse midline gliomas (DMG), based on the simplification that the mutation overrides the histological, radiological, and clinical characteristics of such tumors. Indeed, multiple studies have described pediatric H3K27-altered DMG as incurable tumors. However, in biology and clinical practice, exceptions are frequent and complexity is the rule. First of all, H3K27 mutations have also been found in non-diffuse gliomas. On the other hand, a minority of DMGs are H3K27 wild-type but have a similarly poor prognosis. Furthermore, adult-type tumors may rarely occur in children, and differences in prognosis have emerged between adult and pediatric H3K27-altered DMGs. As well, tumor location can determine differences in the outcome: patients with thalamic and spinal DMG have significantly better survival. Finally, other concomitant molecular alterations in H3K27 gliomas have been shown to influence prognosis. So, when such additional mutations are found, which one should we focus on in order to make the correct clinical decision? Our review of the current literature on pediatric diffuse midline H3K27-altered DMG tries to address such questions. Indeed, H3K27 status has become a fundamental supplement to the histological grading of pediatric gliomas; however, it might not be sufficient alone to exhaustively define the complex biological behavior of DMG in children and might not represent an indication for a unique treatment strategy across all patients, irrespective of age, additional molecular alterations, and tumor location

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