Mikhail Sokolov | Searcharxiv

arXiv2026

Optically Encoded Suspension Microarrays: Materials, Design Strategies, and Future Directions for Multiplexed Bioanalysis

Suspension microarrays based on optically encoded microbeads have become one of the most versatile platforms for multiplexed bioanalysis because they combine solution-phase reaction kinetics, flexible assay design, and high-throughput detection. However, despite more than two decades of intense research, no consensus has emerged regarding the optimal strategies for particle encoding, surface functionalization, and signal decoding. Progress has mainly been driven by incremental improvements in individual materials rather than by systematic comparison of competing technological concepts. This review critically evaluates the main approaches to the fabrication of optically encoded microbeads, including post-synthetic (swelling and layer-by-layer assembly) and in situ encoding strategies, and proposes a mechanistic classification of in situ methods of particle formation into polymerization-driven and confinement-controlled ones. Instead of comparing the fabrication methods solely in terms of encoding capacity, we assess their relative merits in terms of structural control, code stability, scalability, compatibility with biofunctionalization, and suitability for clinical implementation. We further examine the strengths and limitations of organic fluorophores, aggregation-induced emission luminogens, semiconductor quantum dots, and upconversion nanoparticles and show that no encoding material is universally optimal and that performance is determined by trade-offs between optical properties, manufacturing complexity, and stability in biological media. We argue that future progress will depend not as much on increasing the theoretical number of optical codes as on improving the code reproducibility, minimizing spectral crosstalk and nonspecific interactions, and employing microfluidic fabrication, antifouling surface chemistry, automated spectral decoding, and artificial intelligence-assisted data analysis. These developments are expected to transform suspension microarrays from multiplexed analytical tools into standardized lab-on-a-microbead platforms suitable for next-generation clinical diagnostics.

physics.med-ph↗

arXiv2022

High-resolution semantically-consistent image-to-image translation

Deep learning has become one of remote sensing scientists' most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing datasets means that scientists need to solve the domain adaptation problem to narrow the discrepancy between satellite image datasets. As a result, image segmentation models that are then trained, could better generalize and use an existing set of labels instead of requiring new ones. This work proposes an unsupervised domain adaptation model that preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. This paper's major contribution is proposing the improved architecture of the SemI2I model, which significantly boosts the proposed model's performance and makes it competitive with the state-of-the-art CyCADA model. A second contribution is testing the CyCADA model on the remote sensing multi-band datasets such as WorldView-2 and SPOT-6. The proposed model preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. Thus, the semantic segmentation model, trained on the adapted images, shows substantial performance gain compared to the SemI2I model and reaches similar results as the state-of-the-art CyCADA model. The future development of the proposed method could include ecological domain transfer, {\em a priori} evaluation of dataset quality in terms of data distribution, or exploration of the inner architecture of the domain adaptation model.

cs.CV↗