Abstract
A point-of-care test (POCT) based on low-cost and highly sensitive disposable chips was designed for the sensitive and selective detection of proteins. In particular, a pollen-based plasmonic nanostructured probe coupled, for the first time, with biomimetic receptors custom-designed as molecularly imprinted nanoparticles (MIP-NPs) for protein recognition, was developed and interrogated by an extrinsic optical fiber (OF)-based scheme. To this purpose, bovine serum albumin (BSA) was chosen in a proof-of-concept frame as an example of a protein. © 2026 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

Abstract
Optical coherence tomography angiography (OCTA), known for its high-resolution and noninvasive imaging capability, has become a key modality for visualizing retinal vasculature. Accurate and automated segmentation of capillaries, arteries, veins, and foveal avascular zone in OCTA images is essential for quantitative analysis and disease assessment. In this paper, we propose a depth enhanced cascaded framework specifically designed for multi-class OCTA segmentation. Our method investigates the spatial distribution of vasculature in retinal images and integrates a novel self-supervised depth prediction module to learn implicit depth cues from volumetric data, thereby improving the discrimination of overlapping vascular layers. In addition, we design two topology-aware loss functions that explicitly encourage structural integrity and continuity of vessel segmentation, particularly at bifurcations and endpoints. Experiments on the OCTA-6 mm and OCTA-3 mm datasets demonstrate that our method outperforms existing state-of-the-art approaches, with mIoU gains of around 2% over prior method, IPNv2, thereby highlighting enhanced segmentation accuracy and vascular topology preservation.

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This paper focuses on the comprehensive assessment of regional performance in attaining the 2030 Strategic Framework for Education and Training (ET2030) established by the European Union. To this end, we propose a composite indicator framework based on robust Benefit-of-the-doubt models empirically validated through an extensive analysis of data spanning 32 countries and 101 NUTS-I level regions for 2019. We integrate contextual variables into a robust conditional model to ensure an equitable evaluation among regions grappling with distinct circumstances. Specifically, the unemployment rate and the percentage of the population holding national citizenship are considered. Moreover, the research identifies best practices from high-performing regions that can serve as benchmarks for underperforming areas. Analyzing regional-level data is crucial for understanding disparities between European regions and within countries.

Abstract
Our team previously introduced an innovative concept for an "invisible" Electrocardiography (ECG) system, incorporating electrodes and sensors into a toilet seat design to enable signal acquisition from the thighs. Building upon that work, we now present a novel dataset featuring real-world, single-lead ECG signals captured at the thighs, offering a valuable resource for advancing research on thigh-based ECG for cardiovascular disease assessment. To our knowledge, this is the first dataset of its kind. The tOLIet dataset comprises 149 ECG recordings collected from 86 individuals (50 females, 36 males) with an average age of 31.73 ± 13.11 years, a mean weight of 66.89 ± 10.70 kg, and an average height of 166.82 ± 6.07 cm. Participants were recruited through direct contact with the Principal Investigator at Centro Hospitalar Universitario de Lisboa Central (CHULC) and via clinical consultations conducted at the same institution. Each recording includes four differential signals acquired from electrode pairs embedded in the toilet seat, with reference signals obtained from a standard 12-lead hospital ECG system.