Publications by authors named "A Quiros"

Human dietary exposure to chemical compounds is a priority issue for public health authorities since it constitutes a key step in risk assessment, and food packaging could be an important source of contamination. In this study, the bioaccessibility of cyclodi-BADGE was evaluated in canned seafood samples using a standardized protocol of in vitro gastrointestinal digestion and an analytical method based on liquid chromatography coupled to tandem mass spectrometry. The impact of enzymes, different gastric pHs, and food-covering liquids on the bioaccessibility of cyclodi-BADGE was studied.

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Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We trained an SSL Barlow Twins-encoder on 435 TCGA colon adenocarcinoma WSIs to extract features from small image patches. Leiden community detection then grouped tiles into histomorphological phenotype clusters (HPCs).

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A general-purpose photonic processor can be built integrating a silicon photonic programmable core in a technology stack comprising an electronic monitoring and controlling layer and a software layer for resource control and programming. This processor can leverage the unique properties of photonics in terms of ultra-high bandwidth, high-speed operation, and low power consumption while operating in a complementary and synergistic way with electronic processors. These features are key in applications such as next-generation 5/6 G wireless systems where reconfigurable filtering, frequency conversion, arbitrary waveform generation, and beamforming are currently provided by microwave photonic subsystems that cannot be scaled down.

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Article Synopsis
  • - Primary cutaneous squamous cell carcinoma (cSCC) causes around 10,000 deaths each year in the U.S., and assessing the risk of poor outcomes right at the time of initial biopsy can greatly influence clinical decisions.
  • - A multi-institutional study introduced a self-supervised deep-learning model that can predict the likelihood of poor outcomes in cSCC, revealing that certain histomorphological features like poor differentiation and deep invasion are linked to worse prognoses.
  • - This new model is particularly effective for assessing risk in specific types of cSCC (T2a/T2), which can help identify patients who might need closer monitoring or more aggressive treatment right after diagnosis.
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