Modifying the Catalytic Activity of Lipopeptide Assemblies with Nucleobases.

Biohybrid catalysts that operate in aqueous media are intriguing for systems chemistry. In this paper, we investigate whether control over the self-assembly of biohybrid catalysts can tune their properties. As a model, we use the catalytic activity of functional hybrid molecules consisting of a catalytic H-dPro-Pro-Glu tripeptide, derivatized with fatty acid and nucleobase moieties. This combination of simple biological components merged the catalytic properties of the peptide with the self-assembly of the lipid, and the structural ordering of the nucleobases. The biomolecule hybrids self-assemble in aqueous media into fibrillar assemblies and catalyze the reaction between butanal and nitrostyrene. The interactions between the nucleobases enhanced the order of the supramolecular structures and affected their catalytic activity and stereoselectivity. The results point to the significant control and ordering that nucleobases can provide in the self-assembly of biologically inspired supramolecular catalysts.

Embedded Vision Intelligence for the Safety of Smart Cities.

Advances in Artificial intelligence (AI) and embedded systems have resulted on a recent increase in use of image processing applications for smart cities' safety. This enables a cost-adequate scale of automated video surveillance, increasing the data available and releasing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Additionally, new lightweight open-source middleware for constrained resource devices, such as EdgeX Foundry, have appeared to facilitate the collection and processing of data at sensor level, with communication capabilities to exchange data with a cloud enterprise application. The objective of this work is to show and describe the development of two Edge Smart Camera Systems for safety of Smart cities within S4AllCities H2020 project. Hence, the work presents hardware and software modules developed within the project, including a custom hardware platform specifically developed for the deployment of deep learning models based on the I.MX8 Plus from NXP, which considerably reduces processing and inference times; a custom Video Analytics Edge Computing (VAEC) system deployed on a commercial NVIDIA Jetson TX2 platform, which provides high level results on person detection processes; and an edge computing framework for the management of those two edge devices, namely Distributed Edge Computing framework, DECIoT. To verify the utility and functionality of the systems, extended experiments were performed. The results highlight their potential to provide enhanced situational awareness and demonstrate the suitability for edge machine vision applications for safety in smart cities.

Benchmarking Object Detection Deep Learning Models in Embedded Devices.

Object detection is an essential capability for performing complex tasks in robotic applications. Today, deep learning (DL) approaches are the basis of state-of-the-art solutions in computer vision, where they provide very high accuracy albeit with high computational costs. Due to the physical limitations of robotic platforms, embedded devices are not as powerful as desktop computers, and adjustments have to be made to deep learning models before transferring them to robotic applications. This work benchmarks deep learning object detection models in embedded devices. Furthermore, some hardware selection guidelines are included, together with a description of the most relevant features of the two boards selected for this benchmark. Embedded electronic devices integrate a powerful AI co-processor to accelerate DL applications. To take advantage of these co-processors, models must be converted to a specific embedded runtime format. Five quantization levels applied to a collection of DL models are considered; two of them allow the execution of models in the embedded general-purpose CPU and are used as the baseline to assess the improvements obtained when running the same models with the three remaining quantization levels in the AI co-processors. The benchmark procedure is explained in detail, and a comprehensive analysis of the collected data is presented. Finally, the feasibility and challenges of the implementation of embedded object detection applications are discussed.

Immunogenicity of Multi-Target Chimeric RHDV Virus-Like Particles Delivering Foreign B-Cell Epitopes.

The rabbit hemorrhagic disease virus (RHDV) vaccine platform is a nanoparticle composed of 180 copies of the viral capsid protein, VP60, self-assembled into virus-like particles (VLPs). RHDV VLPs are able to accept the simultaneous incorporation of target epitopes at different insertion sites. The resulting chimeric RHDV VLPs displaying immunogenic foreign antigens have been shown to induce specific protective immune responses against inserted heterologous T-cytotoxic and B-cell epitopes in the mouse and pig models. In this study, we explored whether RHDV-based engineered VLPs can be developed as efficient multivalent vaccines co-delivering different foreign B-cell antigens. We generated bivalent chimeric RHDV VLPs displaying two model B-cell epitopes at different surface-exposed insertion sites, as well as the corresponding monovalent chimeric VLPs. The immunogenic potential of the bivalent chimeric VLPs versus the monovalent constructs was assessed in the mouse model. We found that the bivalent chimeric VLPs elicited a strong and balanced antibody response towards the two target epitopes tested, although slight reductions were observed in the levels of specific serum antibody titers induced by bivalent chimeric VLPs as compared with the corresponding monovalent constructs. These results suggest that RHDV VLPs could represent a promising platform for the development of efficient multivalent vaccines.

Defining a Standard Set of Health Outcomes for Patients With Squamous Cell Carcinoma of the Head and Neck in Spain.

PURPOSE: A systematic, standardized collection of health outcomes during patient treatment and follow-up, relevant from the perspective of all stakeholders, is a crucial step toward effective and efficient disease management. This project aimed to define a standard set of health outcomes for patients with squamous cell carcinoma of the head and neck (SCCHN). METHODS: The project was led and coordinated by a scientific committee (SC). It comprised: (1) a literature review (to identify variables used during SCCHN management); (2) 1st-SC meeting (to select the variables for presentation during nominal groups-NG); (3) five NG (n=42 experts) and four interviews with patients (to reach consensus on the variables for inclusion); and (4) final-SC meeting (to review the results of NG ensuring consensus on the variables where consensus was not reached). RESULTS: Experts agreed to include the following variables in the standard set: treatment-related (treatment intent and type, response to treatment, treatment toxicity/complication, treatment completion), degree of health (performance status, patient-reported health status, pain, dysphonia, feeding and speech limitations, body image alteration, tracheotomy), survival (overall and progression-free survival, cause of death), nutritional (weight, nutritional intervention), other variables (smoking status, alcohol consumption, patient satisfaction with aftermath care, employment status), and case-mix variables (demographic, tumor-related, clinical and nutritional factors). CONCLUSIONS: This project may pave the way to standardizing the collection of health outcomes in SCCHN and promote the incorporation of patients' perspective in its management. The information provided through the systematic compilation of this standard set may define strategies to achieve high-quality, patient-centered care.