Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients' risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these "big data" in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer

NEMO-SN1 Abyssal Cabled Observatory in the Western Ionian Sea

The NEutrinoMediterranean Observatory—Submarine Network 1 (NEMO-SN1) seafloor observatory is located in the central Mediterranean Sea, Western Ionian Sea, off Eastern Sicily (Southern Italy) at 2100-m water depth, 25 km from the harbor of the city of Catania. It is a prototype of a cabled deep-sea multiparameter observatory and the first one operating with real-time data transmission in Europe since 2005. NEMO-SN1 is also the first-established node of the European Multidisciplinary Seafloor Observatory (EMSO), one of the incoming European large-scale research infrastructures included in the Roadmap of the European Strategy Forum on Research Infrastructures (ESFRI) since 2006. EMSO will specifically address long-term monitoring of environmental processes related to marine ecosystems, marine mammals, climate change, and geohazards

Delphi Initiative for Early-Onset Colorectal Cancer (DIRECt) International Management Guidelines

Background & aims: Patients with early-onset colorectal cancer (eoCRC) are managed according to guidelines that are not age-specific. A multidisciplinary international group (DIRECt), composed of 69 experts, was convened to develop the first evidence-based consensus recommendations for eoCRC. Methods: After reviewing the published literature, a Delphi methodology was used to draft and respond to clinically relevant questions. Each statement underwent 3 rounds of voting and reached a consensus level of agreement of ≥80%. Results: The DIRECt group produced 31 statements in 7 areas of interest: diagnosis, risk factors, genetics, pathology-oncology, endoscopy, therapy, and supportive care. There was strong consensus that all individuals younger than 50 should undergo CRC risk stratification and prompt symptom assessment. All newly diagnosed eoCRC patients should receive germline genetic testing, ideally before surgery. On the basis of current evidence, endoscopic, surgical, and oncologic treatment of eoCRC should not differ from later-onset CRC, except for individuals with pathogenic or likely pathogenic germline variants. The evidence on chemotherapy is not sufficient to recommend changes to established therapeutic protocols. Fertility preservation and sexual health are important to address in eoCRC survivors. The DIRECt group highlighted areas with knowledge gaps that should be prioritized in future research efforts, including age at first screening for the general population, use of fecal immunochemical tests, chemotherapy, endoscopic therapy, and post-treatment surveillance for eoCRC patients. Conclusions: The DIRECt group produced the first consensus recommendations on eoCRC. All statements should be considered together with the accompanying comments and literature reviews. We highlighted areas where research should be prioritized. These guidelines represent a useful tool for clinicians caring for patients with eoCRC

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

Km3Net Italy – Seafloor network

The KM3NeT European project aims to construct a large volume underwater neutrino telescope in the depths of the Mediterranean Sea. INFN and KM3NeT collaboration, thanks to a dedicated funding of 21.000.000 € (PON 2007–2013), are committed to build and deploy the Phase 1 of the telescope, composed of a network of detection units: 8 towers, equipped with single photomultiplier optical modules, and 24 strings, equipped with multi-photomultipliers optical modules. All the towers and strings are connected to the main electro optical cable by means of a network of junction boxes and electro optical interlink cables. Each junction box is an active node able to provide all the necessary power to the detection units and to guarantee the data transmission between the detector and the on-shore control station. The KM3NeT Italia project foresees the realization and the installation of the first part of the deep sea network, composed of three junction boxes, one for the towers and two for the strings. In July 2015, two junction boxes have been deployed and connected to the new cable termination frame installed during the same sea campaign. The third and last one will be installed in November 2015. The status of the deep sea network is presented together with technical details of the project

Preserving Information from Real Objects to Digital Shapes

Nowadays, the success of the scientific enterprise largely depends on the ability of sharing resources among the scientific community. This problem is particularly relevant in the field of Computer Graphics and Vision. During the last years specific domains ontologies are emerging in support of different contexts, aiming at facilitating automated resource-sharing among information systems in specific fields. A fast evolution of Computer Graphics and Vision is now conditioned by how research teams will be able to intercommunicate. The shared resources should preserve as much meaningful information as possible, in order to allow and improve collaborative research and complete understanding of complex tasks. A critical phase in Computer Graphics and Vision is the Acquisition Phase, which includes different conditions and properties related to the object to be scanned, to the surrounding environment or even to the knowledge of the scanning experts. The novelty of our work is the tentative to integrate Knowledge Management approaches to Computer Graphics and Vision and, in particular, we aim at preserving information when passing from the real world (Real Objects) to the digital one (Digital Shapes). This is a fundamental step for moving knowledge from humans to machines.Eurographics Italian Chapter Conferenc

Enhancing cognition through pharmacological and environmental interventions: Examples from preclinical models of neurodevelopmental disorders

In this review we discuss the role of environmental and pharmacological treatments to enhance cognition with special regards to neurodevelopmental related disorders and aging. How the environment influences brain structure and function, and the interactions between rearing conditions and gene expression, are fundamental questions that are still poorly understood. We propose a model that can explain some of the discrepancies in findings for effects of environmental enrichment on outcome measures. Evidence of a direct causal correlation of nootropics and treatments that enhanced cognition also will be presented, and possible molecular mechanisms that include neurotrophin signaling and downstream pathways underlying these processes are discussed. Finally we review recent findings achieved with a wide set of behavioral and cognitive tasks that have translational validity to humans, and should be useful for future work on devising appropriate therapies. As will be discussed, the collective findings suggest that a combinational therapeutic approach of environmental enrichment and nootropics could be particularly successful for improving learning and memory in both developmental disorders and normal aging