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Digital cameras1 and displays2 use picture elements (pixels3) that perform a single function: detecting or emitting light intensity. To exploit the full information content of electromagnetic waves, more advanced elements are required. This has driven the development of multifunctional components that, for example, simultaneously detect and emit intensity4,5 or extract intensity and spectral information6-8. However, no pixel exists that both senses and generates optical wavefronts with full control over amplitude, phase and polarization, limiting bidirectional control and feedback of sophisticated light fields. Here we present a route to such pixels by demonstrating a versatile platform of miniaturized diffractive elements based on Fourier optics9. We use plasmonic surface waves10, which propagate coherently11 and efficiently12-15 across metallic surfaces. When these plasmons are launched towards wavy microstructures16 designed with simple Fourier analysis, arbitrary and background-free optical wavefronts are generated. Conversely, incoming light can be sensed, and its amplitude, phase and polarization can be fully characterized. By combining or superposing several such components, we create multifunctional 'Fourier pixels' that provide compact and accurate control over the optical field. Our approach, which we extend to photonic waveguide modes, establishes a scalable, universal architecture for vectorially programmable pixels with applications in adaptive optics17,18, holographic displays19-21, optical communication22,23 and quantum information processing24.

Accurate diagnosis of allergic diseases demands sensitive measurement of biomarkers, particularly specific immunoglobulin E. Although standard laboratory tests can provide this information accurately, they have not been able to keep up with the urgent need for speedy point-of-care alternatives. Biosensors, including immunosensors (antibody-antigen) and aptasensors (nucleic acid receptors), offer improved stability, reusability, and cost-effectiveness. Both types of biosensors have receptors that incorporate with transducers to produce measurable optical or electrochemical signals. This review consolidates known biomarkers for four major allergic conditions: asthma, atopic dermatitis (AD), allergic rhinitis, and food allergies. It then evaluates the analytical performance of optical and electrochemical immunosensors and aptasensors employed in diagnosing and monitoring these disorders. Furthermore, the development of point-of-care biosensor platforms for allergic disease detection is discussed, emphasizing their potential to enable rapid, decentralized testing and improve patient management through accessible, real-time diagnostic solutions. The primary focus remains on sensor-based platforms capable of detecting allergic disease biomarkers, providing meaningful insights into current technological capabilities and their clinical translation.

Circular dichroism originates from symmetry breaking in a material structure and leads to differential absorption of left-handed and right-handed circularly polarized light. However, circular dichroism in most materials is inherently weak and spectrally narrow, especially in the mid-to-far infrared. Here we uncover giant infrared circular dichroism in the magnetic-field-forced Weyl semimetal Mn(Bi,Sb)2Te4 driven by extreme particle-hole symmetry breaking. Helicity-resolved magneto-infrared spectroscopy reveals circular dichroism exceeding 3,000 mdeg (~130 mdeg nm-1) with an above-degree response extending over the 6-13 μm spectral range. The optical resonances are enhanced by a strong band nesting effect intrinsic to the Landau levels of type-II Weyl dispersion. A symmetry-based k·p model reproduces these magneto-infrared responses and demonstrates that magnetization-induced asymmetric spin-orbit coupling generates particle-hole symmetry breaking, which suppresses spin-up, parity-even wavefunction components in the valence Landau band and thereby produces pronounced optical helicity-selectivity. Our findings establish particle-hole symmetry breaking as an effective route towards helicity-resolved optical control in quantum materials.

Spatiotemporal optical vortices (STOVs) carrying transverse orbital angular momentum (OAM) fundamentally expand light-structuring capabilities. However, current rigid-body generation paradigms constrain transverse OAM to a single scalar property, leaving rich internal wavepacket dynamics inaccessible. This rigidity contrasts with ubiquitous natural vortices where symmetry breaking is the norm. Here, we break rotational symmetry via the nonlinear mapping of the azimuthal phase gradient, realizing programmable spatiotemporal flux breathing. We theoretically and experimentally demonstrate that local phase gradient variations induce instantaneous group velocity anisotropy. This compels local OAM density to spontaneously reorganize into stable, multilobed lattice structures while strictly preserving global topological charge. Furthermore, we harness these structures' modulation frequency for free-space information transfer, achieving high-fidelity encoding and decoding of spatiotemporal topological states. This work transitions STOVs from passive scalar objects to structured functional carriers, opening avenues for high-dimensional optical communications, ultrafast spatiotemporal manipulation, strong-field physics, and high-dimensional quantum entanglement.

By profiting from recent developments in detector technologies, making it possible to access a stream of detection events with few-ns time resolutions, a new ptychographic workflow is established. This methodological framework, referred to as guided progressive reconstructive imaging, relies on a quantization-based description of the acquired intensity, through an elementary derivation. Established direct phase retrieval solutions, such as the Wigner distribution deconvolution approach, can then be adapted to a continuous treatment of received counts, with no need for a dense data representation. Consequently, the result is obtained in the form of a progressively improving estimate, while providing immediate user feedback thanks to a processing speed high enough to surpass the acquisition bandwidth. This fast measurement is enabled by the cumulative usage of a pre-calculated library of kernel-limited functions, accumulating count-wise contributions as a function of the triggered detector pixel. Hence, the reconstruction offers the same advantages of direct phase retrieval methods, in particular a high dose-efficiency and the absence of complex convergence dynamics, with much less stringent restrictions on the field of view than is typical in current alternatives. Its implementation is also significantly more straightforward and flexible. Overall, this work constitutes a major evolution in the state-of-the-art, facilitating repeatable and low-dose experiments with high accessibility, and being applicable to electron-based imaging, X-ray diffraction and optical microscopy.

High-resolution 3D microscopy has become a foundational tool in biomedical research by reconstructing vascular and neural networks from subcellular to organ scales, thereby facilitating quantitative analysis of tissue microenvironments, disease progression, and early pathological changes. However, accurately segmenting tubular structure like microvascular and neurite in 3D microscopy remains challenging due to their densely packed, span a wide range of calibers, and branch extremely frequently. These characteristics hinder existing deep learning-based segmentation methods from simultaneously enforcing global connectivity and locally plausible radius profiles, often resulting in fragmented branches, spurious connections, and missing fine terminal processes. In this work, we propose a fully 3D topology-supervised segmentation framework for tubular structure that improves connectivity preservation and morphological consistency. Firstly, we introduce a radius-aware topology that integrates local radius estimates directly into connectivity constraints,so that the network simultaneously enforces correct continuity and physiologically credible thickness. Additionally, we implement adaptive topology-error modulation, which amplifies supervision in regions with significant topological deviations. This mechanism directs the network's focus toward critical errors (e.g., breaks and misjoins) rather than allowing sparse annotations or class imbalance to dominate optimization. Furthermore, we employ efficient large-receptive-field convolutions to capture long-range directional continuity in volumetric data, effectively recovering low-contrast, distal, thin-caliber terminal branches. We validate the approach on three publicly available modalities-electron microscopy vasculature, optical microscopy vasculature, and fluorescently labeled neuronal fibers-and observe that it preserves global 3D connectivity and avoids implausible radius jumps while maintaining strong voxel-level accuracy. Quantitatively, the proposed method achieves the highest Dice/clDice scores on SELMA3D (0.8839/0.9173), Mini-vessel (0.8842/0.9097), and FISBe (0.7803/0.8033), outperforming the strongest competing baselines across all three datasets. These results suggest consistent performance across different microscopy settings without requiring organ-specific anatomical priors.

Mid-infrared Optical Coherence Tomography (MIR-OCT) is a promising Non-Destructive Testing (NDT) technique due to its high-resolution imaging capabilities and extensive applicability across various industrial domains. Studies developing Deep Learning (DL) models to detect defects in MIR-OCT scans are scarce, and few have been used for ceramic quality assessment. To address this gap, we introduce the MIR-OCT Scan Dataset for Ceramic Quality Assessment (CeraMIRScan), including labels to detect and segment defects. The dataset comprises 29 volumes corresponding to MIR-OCT scans of 3D printed ceramic pieces, decomposed into 21,882 B-scan images, each paired with expert-annotated binary masks capturing defects such as pores, delaminations, and inclusions. Notably, 41.38% of the images contain visible anomalies. To illustrate the dataset's applicability to DL, we provide baseline segmentation results using a U-Net architecture, achieving 80.55% precision, 80.00% recall, and an 80.27% Dice score. CeraMIRScan offers a benchmark resource for advancing automated quality assessment and supports the development of MIR-OCT-based defect characterization methods.

Viral outbreaks continue to require diagnostic platforms that are sensitive, selective, rapid, and compatible with decentralized use. Quantum dot (QD)-based Förster resonance energy transfer (QD-FRET) biosensors offer a promising route because viral recognition can be translated into ratiometric optical signals through nanoscale changes in donor-acceptor distance, orientation, and local interfacial environment. However, QD-FRET performance is not determined by photophysics alone. It emerges from the coupled behavior of QD composition, core/shell architecture, ligand-shell packing, hydration, protein-corona formation, bioconjugation geometry, and assay format. This review examines QD-FRET viral biosensors from an interfacial physical chemistry perspective. First, the photophysical basis of QD-mediated energy transfer is summarized with emphasis on Förster distance, donor lifetime, spectral overlap, multivalency, and distance heterogeneity. QD materials are then compared in terms of composition, shell engineering, trap-mediated recombination, toxicity, and colloidal stability. Particular attention is given to surface functionalization strategies, including ligand exchange, compact thiol ligands, polymer and silica encapsulation, zwitterionic and PEG-based antifouling layers, and matrix-induced interfacial restructuring. Biochemical recognition layers based on nucleic acids, antibodies, protein binders, receptor-mimetic peptides, aptamers, and glycans are treated as distinct nanoscale architectures that control spacing, orientation, accessibility, and matrix tolerance. Representative mechanisms are then analyzed across hybridization-induced, CRISPR-cleavage-enabled, immuno-FRET, plasmon- and 2D-material-assisted, time-gated, chemiexcited, and whole-virus or pseudo-virion assemblies. Across these systems, reported limits of detection are interpreted together with matrix complexity, amplification chemistry, readout mode, donor-acceptor geometry, and stability controls. Progress toward robust QD-FRET viral diagnostics will require reproducible QD materials, standardized characterization, tighter control of ligand and recognition-layer heterogeneity, and integration with field-compatible point-of-care formats.

Rapid and accurate mechanical detection is essential across numerous fields. However, conventional camera-based methods, which depend on computationally intensive analysis of high-frame-rate footage and predictive algorithms, are often time-consuming and can introduce interpretive errors that compromise reliability. Herein, we present a class of near-infrared stress memory emitters, allowing fast and visualized mechanical detection in ambient environments. We develop Ca(Sr)ZnOS:Yb3+/Pb2+ crystals and achieve persistent mechanoluminescence at 981 nm by combinatorial engineering of sub-bandgap states. Deliberate modulation of the persistent mechanoluminescence intensity and duration is achieved through isostructural host blending coupled with prescribed ultraviolet charging, enabling direct capture and visualization of ball impacts with short processing time (0.39 s) and high accuracy. The bright (up to 11 × 107 photons per event) and durable (up to 100 s) persistent mechanoluminescence broadens the scope of optical materials in applications such as stress sensors, human-machine interfaces, and mechano-opto-electronics.

This report presents a case of amelanotic metastatic cutaneous melanoma to the vitreous masquerading as a fungal endophthalmitis. A 74-year-old immunocompromised man with a history of Stage 4 metastatic cutaneous melanoma presented with 1+ vitreous cell and a fluffy white peripheral infiltrate in the left eye. Following subsequent vitrectomy for a vitreous hemorrhage, this patient developed preretinal macular deposits that continued to increase in size while undergoing treatment for a presumed fungal endophthalmitis. The patient went on to develop a total retinal detachment with a diffuse hemorrhagic retinopathy. Cytopathologic analysis revealed necrotic melanoma cells from the preretinal surface and within the vitreous cavity. This case highlights the importance of including vitreous metastasis in the differential of vitritis in the setting of cutaneous melanoma. Optical coherence tomography can be used to monitor the progression of preretinal deposits, with an increase in size of the deposits prompting reevaluation of treatment options.

Digital assessment technologies, such as optical motion capture and inertial measurement units, enable detailed kinematic analysis and continuous monitoring of upper limb activity in persons with neurological conditions. While such digital metrics of functioning are increasingly recognized in research, their uptake in clinical neurorehabilitation is limited. It remains unclear which digital metrics of functioning clinicians perceive as most meaningful and how these are integrated into patient-centered care. Understanding clinicians' information needs and reasoning processes is a prerequisite for implementing digital assessment technology. This study aims to characterize how rehabilitation professionals perceive, prioritize, and integrate digital metrics of functioning into clinical reasoning and to identify features that would support their routine use. Three 90-minute focus groups were conducted in 3 Swiss neurorehabilitation centers, involving 11 clinicians with diverse professional backgrounds (5 physiotherapists, 4 occupational therapists, 1 movement scientist, and 1 medical practitioner). Participants discussed essential parameter domains and individually rated the relevance and meaningfulness of 17 kinematic metrics for the well-studied drinking task and 10 established arm use performance metrics. Verbatim transcripts were analyzed using reflexive thematic analysis, and rating data were summarized descriptively. Five main themes were identified. (1) Functional requirements to interpret movement quality and performance (active/passive range of motion, strength, selective muscle control, and grasp) form the basis for interpreting movement. (2) Essential aspects of movement quality (smoothness, efficiency, and compensatory movement) are valued when aligned with observable task execution. (3) Added value of real-world performance (hourly activity profiles, arm-use symmetry, and functional workspace) represents the reference for patient-centered reasoning. (4) Individualizing what matters, including diagnosis-specific preferences, shapes assessment selection. (5) Blending clinical eye and reference data reflects clinicians' reliance on visual judgment complemented by normative values. Intuitive metrics such as task duration, number of movement units, and range of motion were favored, whereas confidence was lower in more complex metrics (eg, jerk and interjoint coordination). Clinicians value intuitive digital metrics of functioning when they are clearly linked to patient-centered outcomes and supported by normative references. The findings highlight the need for targeted educational strategies and digital competency training that help clinicians interpret digital metrics and integrate them with contextual information and clinical reasoning.

The development of new magnetic materials with tunable properties has been the focus of significant research because of the quick development of spintronics, which utilizes the intrinsic spin of electrons in addition to their charge. Although Co3O4 is a promising material for high-end spintronic devices because of its well-defined magnetic hysteresis, its weak ferromagnetic nature results in low saturation magnetization. In this work, a simple sol-gel approach was employed to synthesize Sr-doped Co3O4 nanorods, which were successfully obtained with varying Sr dopant concentrations (1%, 2%, and 3%). With the incorporation of XRD, Raman Spectra, TEM, XPS, UV-Vis spectroscopy, and VSM, the structural, morphological, chemical, optical, and magnetic properties have all been examined. For both pure and doped samples, the XRD data validate the spinel cubic phase Co3O4 crystalline structure with the space group Fd3m. The diameter and length of a typical individual nanorod, displayed in TEM images of 3% Sr-doped Co3O4, are 14 and 99 nm, respectively, with an aspect ratio of 7.1 nm. The Sr-doped Co3O4 NPs' X-ray photoelectron spectroscopy (XPS) shows evidence of dopant incorporation. As the Sr content rises, the band gap falls from 1.50 eV to 1.31 eV, according to UV-Vis spectra. A weak ferromagnetism is established due to the doping, as evidenced by the notable 3% Sr-doped Co3O4 nanorods with a robust ferromagnetic characteristic, which exhibit a maximum saturation magnetization of 0.59 emu/g at room temperature, higher than that of pristine Co3O4 nanostructures. Additionally, this material contributes to a coercivity of 293.8 Oe and a remanence of 0.05 emu/g. 2% Sr-doped Co3O4 exhibited the highest zone of inhibition against S. aureus (18 ± 0.32 mm at 1 mg/ml) among the studied samples, but pristine Co3O4 was the most effective against E. coli (15 ± 0.24 mm at 1 mg/ml). Thus, Sr-doped Co3O4 nanorods' potential as a promising bioactive material with antimicrobial applications is highlighted by their antibacterial activity. These findings denote the significance and enormous potential of Sr-doped Co3O4 in the development of high-performance spintronic devices and antimicrobial applications.

Inspired by the human brain, neuromorphic devices hold the potential to overcome the limitations of the traditional von Neumann architecture. Synapses serve as the core component of neuromorphic devices, which act as the fundamental units for signal conversion. In particular, artificial optoelectronic synapses with color perception capabilities offer significant advantages in multicolor image sensing and visual simulation. In this work, we demonstrate a Cs3Bi2I9 microplate/poly(3-hexylthiophene) (P3HT) heterojunction (CPH) as an ecofriendly and stable optoelectronic synaptic device. The device exhibits typical synaptic behaviors under optical stimulation, including excitatory postsynaptic current, paired-pulse facilitation with a maximum index of about 190%, and controllable transition from short-term to long-term memory. Notably, the CPH synapse achieves low power consumption (<90 fJ per synaptic event), outperforming traditional complementary metal oxide semiconductor-based circuits. Furthermore, the device demonstrates multi-wavelength response capabilities, enabling dual-wavelength perception and preprocessing. Our work provides a sustainable and high-performance alternative for neuromorphic vision systems, paving the way toward environmentally benign synaptic hardware.

In nature, fruit flies Drosophila have evolved a simple but superb vision system characterised by a three-level synergy: natural compound eyes for panoramic perception, head muscles for continuous tracking in dim conditions, and neural circuits for dynamic scenes. This vision system serves as an ideal model for biomimicry, yet achieving true replication remains challenging, despite significant progress in artificial compound eyes recently. In this study, we present a flexible artificial compound eye camera that adopts such a three-level synergy. An artificial compound eye is constructed by plastic optical fibres and curved microlens arrays for real-time panoramic imaging. Two tethers simulate head muscle movements, achieving a 270&#xb0; field of view for continuous tracking of weak signals. An artificial intelligence algorithm mimics neural processing, enabling the reconstruction of interactive mixed-reality scenes at rates up to 7000 fps. This unique integration of panoramic sensing, active tracking, and neural-like processing establishes a framework for vision-based metaverse applications and bioinspired wearable technologies.

Light carries both spin (polarization) and orbital angular momentum. Combining these degrees of freedom produces hybrid spin-orbit states that live in a high-dimensional Hilbert space, offering greater information capacity and robustness for optical communication, quantum technologies, and metrology. However, generating arbitrary states in these spaces and characterizing them efficiently has remained difficult. Here we show a compact metasurface that generates arbitrary spin-orbit states in a four-dimensional Hilbert space, visualized on a Poincar&#xe9; hypersphere, with straightforward scalability to higher dimensions. Using a tetratomic unit cell, the single-layer device precisely controls complex amplitude, phase, and polarization. We further introduce an efficient interferometric scheme that reconstructs the full density matrix of any N-dimensional spin-orbit state using only three interferograms. This approach uncovers an intrinsic spin-orbit parity order that governs the symmetry of projected intensity patterns, independent of the weighting of the eigenmodes, and enables controlled mode transformations through higher-order geometric phases. These advances establish a versatile platform for high-dimensional photonic technologies.

Structured light beams carrying orbital angular momentum (OAM), such as Laguerre-Gaussian modes, are promising tools for high-capacity optical communications and advanced biomedical imaging. However, multiple scattering in turbid media distorts their phase and amplitude, complicating the retrieval of topological charge. Using experimentally acquired three-channel intensity and interference measurements from 25 independent acquisition sessions, we evaluate signed 11-class and unsigned 6-class topological-charge classification with a matched CNN baseline, an Angular Fourier Transform CNN (AFT-CNN), and a pretrained ResNet18 baseline. The best-performing models achieve high accuracy in the low-scattering regime, with the CNN and ResNet18 remaining near 95% at [Formula: see text] but accuracy drops sharply around [Formula: see text] These results indicate that sign-dependent OAM information can survive multiple scattering in the low-scattering regime and can be decoded from three-channel measurements with deep learning.

To improve the performance of pressurized metered dose inhalers (pMDIs) a better mechanistic understanding of early deposition within the device and upper airways is needed to move toward more efficient inhalation therapies. The present work aimed to address the longstanding gap between plume geometry (PG) measurements and pulmonary drug delivery by evaluating PG with the Plume Induction Port Evaluator (PIPE), a mass-based method that characterizes plume angles under flow and within a restricted geometry. Three Rhodamine B solution pMDI formulations containing 2.49, 9.99, and 19.99% ethanol were evaluated at 5, 25, and 37&#xa0;&#xb0;C. PG was characterized by high-speed laser imaging (HSLI, 0 L/min) and by PIPE connected to a Next Generation Impactor (NGI, 30 L/min). Formulation vapor pressure, droplet size by laser diffraction, aerodynamic particle size by cascade impaction, early deposition, fine particle fraction (FPF), and respirable fraction (RF) were also determined. Deposition patterns within PIPE were log-normally distributed and consistently downward oriented, allowing calculation of mass median plume angle (MMPA) under flow. HSLI and MMPA showed opposite responses to formulation vapor pressure, where higher vapor pressure produced narrower optical PG but wider mass-based plume angles. A multivariable model incorporating MMPA, volume median diameter, formulation vapor pressure, and the MMPA&#x2009;&#xd7;&#x2009;pressure interaction predicted RF with strong agreement (R2&#x2009;=&#x2009;0.919). These results show that early deposition in pMDIs is not governed by particle size alone, but also by plume trajectory under inhalation flow.