Publications by authors named "P Leon Moreno"
Article Synopsis
- A team of over 180 researchers from more than 40 countries is addressing the issues related to "phantom agents," which are proposed pathogenic agents that are listed without concrete evidence of their existence.
- These phantom agents, identified only through symptoms and lacking proper isolates or genetic data, create obstacles for trade and plant certification, making effective detection and risk assessment difficult.
- The researchers recommend removing these agents from regulatory lists and updating standards in line with modern diagnostic methods to facilitate germplasm exchange and support global agriculture.
Background: Whether the high bleeding risk (HBR) criteria of the Academic Research Consortium (ARC) have a consistent predictive ability across different categories of body mass index (BMI) remains unclear.
Methods: Consecutive patients undergoing percutaneous coronary intervention (PCI) between 2012 and 2019 at Mount Sinai Hospital (New York, USA) were stratified into five BMI categories (18.5-24.
Aim: Due to the absence of validated bleeding risk tools in cancer patients undergoing percutaneous coronary intervention (PCI), we aimed to validate an adapted version of the Academic Research Consortium (ARC) High Bleeding Risk (HBR) criteria.
Methods: Consecutive patients with active or remission cancer undergoing PCI between 2012 and 2022 at Mount Sinai Hospital (New York, USA) were included. Patients were considered at HBR if they met at least one of the major ARC-HBR criteria, other than cancer, or two minor criteria.
As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques.
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