Follow-up in Head and Neck Cancer: Do More Does It Mean Do Better? A Systematic Review and Our Proposal Based on Our Experience

As the patients population ages, cancer screening increases, and cancer treatments improve, millions more head and neck carcinoma (HNC) patients will be classified as cancer survivors in the future. Change in epidemiology with human papillomavirus related HNC leads to a number of young treated patients. After treatment for HNC intensive surveillance, including ear, nose and throat (ENT) endoscopy, imaging, and serology, confers a survival benefit that became less evident in unresectable recurrence. We performed a comprehensive revision of literature and analyzed the experience of our centre. We revised publications on this topic and added data derived from the interdisciplinary work of experts within medical oncology, ENT, and radiation oncology scientific societies. We retrospectively collected local and distant recurrence of chemoradiation treated patients at Santa Croce and Carle University Hospital. A HNC follow-up program is not already codified and worldwide accepted. There is a need of scheduled follow-up. We suggest adopting a standardized follow-up guideline, although a multidisciplinary approach is frequently requested to tailor surveillance program and treatment on each patient

An Analysis of Visually Grounded Instructions in Embodied AI Tasks

Thanks to Deep Learning models able to learn from Internet-scale corpora, we observed tremendous advances in both text-only and multi-modal tasks such as question answering and image captioning. However, real-world tasks require agents that are embodied in the environment and can collaborate with humans by following language instructions. In this work, we focus on ALFRED, a large-scale instruction-following dataset proposed to develop artificial agents that can execute both navigation and manipulation actions in 3D simulated environments. We present a new Natural Language Understanding component for Embodied Agents as well as an in-depth error analysis of the model failures for this challenge, going beyond the success-rate performance that has been driving progress on this benchmark. Furthermore, we provide the research community with important directions for future work in this field which are essential to develop collaborative embodied agents.</p

An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems

We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child's current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) and take decisions accordingly. These decisions are typically aimed at improving the child's performance by triggering appropriate re-engagement stimuli when their attention is low, by changing the game or making it more difficult when the child is losing interest in the task as it is too easy. Alongside state-of-the-art techniques for emotion recognition and head pose estimation, we use a runtime variant of a probabilistic and epistemic logic programming dialect of the Event Calculus, known as the Epistemic Probabilistic Event Calculus. In particular, the probabilistic component of this symbolic framework allows for a natural interface with the machine learning techniques. We overview the architecture and its components, and show some of its characteristics through a discussion of a running example and experiments

Inactivation of TEM‑1 by Avibactam (NXL-104): Insights from Quantum Mechanics/Molecular Mechanics Metadynamics Simulations

The fast and constant development of drug-resistant bacteria represents a serious medical emergence. To overcome this problem, the development of drugs with new structures and modes of action is urgently needed. In this context, avibactam represents a promising, innovative inhibitor of beta-lactamases with a novel molecular structure compared to previously developed inhibitors, showing a promising inhibitory activity toward a significant number of beta-lactamase enzymes. In this work, we studied, at the atomistic level, the mechanisms of formation of the covalent complex between avibactam and TEM-1, an experimentally well-characterized class A beta-lactamase, using classical and quantum mechanics/molecular mechanics (QM/MM) simulations combined with metadynamics. Our simulations provide a detailed structural and energetic picture of the molecular steps leading to the formation of the avibactam/TEM-1 covalent adduct. In particular, they support a mechanism in which the rate-determining step is the water-assisted Glu166 deprotonation by Ser70. In this mechanistic framework, the predicted activation energy is in good agreement with experimental kinetic measurements. Additionally, our simulations highlight the important role of Lys73 in assisting the Ser70 and Ser130 deprotonations. While based on the specific case of the avibactam/TEM-1, the simple protocol we present here can be immediately extended and applied to the study of covalent complex formation in different enzyme–inhibitor pairs