Publications by authors named "R Arranz"

Metal-Organic Frameworks (MOFs) attract attention for their intrinsic porosity, large surface area, and functional versatility. To fully utilize their potential in applications requiring precise control at smaller scales, it is essential to overcome challenges associated with their bulk form. This is particularly difficult for 3D MOFs with spin crossover (SCO) behavior, which undergo a reversible transition between high-spin and low-spin states in response to external stimuli.

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Here we report the characterization of 17T2, a SARS-CoV-2 pan-neutralizing human monoclonal antibody isolated from a COVID-19 convalescent individual infected during the first pandemic wave. 17T2 is a class 1 VH1-58/κ3-20 antibody, derived from a receptor binding domain (RBD)-specific IgA memory B cell, with a broad neutralizing activity against former and new SARS-CoV-2 variants, including XBB.1.

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This paper summarizes in depth the state of the art of aerial swarms, covering both classical and new reinforcement-learning-based approaches for their management. Then, it proposes a hybrid AI system, integrating deep reinforcement learning in a multi-agent centralized swarm architecture. The proposed system is tailored to perform surveillance of a specific area, searching and tracking ground targets, for security and law enforcement applications.

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Single-stranded RNA viruses (ssRNAv) are characterized by their biological diversity and great adaptability to different hosts; traits which make them a major threat to human health due to their potential to cause zoonotic outbreaks. A detailed understanding of the mechanisms involved in viral proliferation is essential to address the challenges posed by these pathogens. Key to these processes are ribonucleoproteins (RNPs), the genome-containing RNA-protein complexes whose function is to carry out viral transcription and replication.

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