Animals and humans are able to quickly and effortlessly estimate the number of items in a set: their numerosity. Numerosity perception is thought to be critical to behavior, from feeding to escaping predators to human mathematical cognition. Virtually, all scientific studies on numerosity mechanisms use well controlled but artificial stimuli to isolate the numerosity dimension from other physical quantities. Here, we probed the ecological validity of these artificial stimuli and evaluate whether an important component in numerosity processing, the numerosity-selective neural populations, also respond to numerosity of items in real-world natural scenes. Using 7T MRI and natural images from a wide range of categories, we provide evidence that the numerosity-tuned neuronal populations show numerosity-selective responses when viewing images from a real-world natural scene. Our findings strengthen the role of numerosity-selective neurons in numerosity perception and provide an important link to their function in numerosity perception in real-world settings.
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http://dx.doi.org/10.1016/j.isci.2022.105267 | DOI Listing |
Clin Drug Investig
January 2025
Department of Medicine, Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
Purpose: The REDUCE-IT randomized trial demonstrated a cardiovascular benefit of icosapent ethyl (IPE) but also raised potential safety signals for atrial fibrillation (AF) and serious bleeding. We aimed to evaluate the real-world safety of IPE versus mixed omega-3 polyunsaturated fatty acid (OM-3) formulations.
Methods: This retrospective active comparator new-user cohort study compared rates of new-onset AF and major bleeding (MB) among adult new users of IPE versus OM-3 in 2020-2024 US Veterans Affairs data.
J Clin Med
December 2024
UOC Allergologia-Asma Center, University of Verona, 37129 Verona, Italy.
Benralizumab is an anti-IL-5 receptor alpha monoclonal antibody that induces the near-complete depletion of eosinophils. This study aimed to evaluate the long-term safety and effectiveness of benralizumab in patients with severe eosinophilic asthma (SEA) over an extended 48-month follow-up period, offering one of the longest real-world perspectives available. This was a single-arm, retrospective, observational, multicenter study involving 123 SEA patients treated with benralizumab at a dosage of 30 mg every 4 weeks for the first 3 doses and then every 8 weeks.
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January 2025
Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, TX 77705, USA.
Automated ultrasonic testing (AUT) is a critical tool for infrastructure evaluation in industries such as oil and gas, and, while skilled operators manually analyze complex AUT data, artificial intelligence (AI)-based methods show promise for automating interpretation. However, improving the reliability and effectiveness of these methods remains a significant challenge. This study employs the Segment Anything Model (SAM), a vision foundation model, to design an AI-assisted tool for weld defect detection in real-world ultrasonic B-scan images.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Automation, Southeast University, Nanjing 210096, China.
Transferring knowledge learned from standard GelSight sensors to other visuotactile sensors is appealing for reducing data collection and annotation. However, such cross-sensor transfer is challenging due to the differences between sensors in internal light sources, imaging effects, and elastomer properties. By understanding the data collected from each type of visuotactile sensors as domains, we propose a few-sample-driven style-to-content unsupervised domain adaptation method to reduce cross-sensor domain gaps.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Computer Science and Systems Engineering, Faculty of Information and Communication Technology, Wrocław University of Science and Technology, 50-370 Wrocław, Poland.
The distributed nature of IoT systems and new trends focusing on fog computing enforce the need for reliable communication that ensures the required quality of service for various scenarios. Due to the direct interaction with the real world, failure to deliver the required QoS level can introduce system failures and lead to further negative consequences for users. This paper introduces a prediction-based resource allocation method for Multi-Access Edge Computing-capable networks, aimed at assurance of the required QoS and optimization of resource utilization for various types of IoT use cases featuring adaptability to changes in users' requests.
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