Image Research Articles

Probing the cosmic web in Lyα emission over large scales: an Intensity Mapping forecast for DECaLS/BASS and DESI

Abstract Being the most prominent HI line, Lyα permeates the cosmic web in emission. Despite its potential as a cosmological probe, its detection on large scales remains elusive. We present a new methodology to perform Lyα intensity mapping with broad-band optical images, by cross-correlating them with Lyα forest data using a custom one-parameter estimator. We also develop an analytical large-scale Lyα emission model with two parameters (average luminosity 〈LLyα〉 and bias be) that respects observational constraints from QSO luminosity functions. We compute a forecast for DECaLS/BASS g-band images cross-correlated with DESI Lyα forest data, setting guidelines for reducing images into Lyα intensity maps. Given the transversal scales of our cross-correlation (26.4 arcmin, ∼33 cMpc/h), our study effectively integrates Lyα emission over all the cosmic volume inside the DESI footprint at 2.2 < z < 3.4 (the g-band Lyα redshift range). Over the parameter space (〈LLyα〉, be) sampled by our forecast, we find a 3σ of large-scale structure in Lyα likely, with a probability of detection of 23.95% for DESI-DECaLS/BASS, and 54.93% for a hypothetical DESI phase II with twice as much Lyα QSOs. Without a detection, we derive upper bounds on 〈LLyα〉 competitive with optimistic literature estimates (2.3 ± 1 · 1041 erg/s/cMpc3 for DESI, and ∼35% lower for its hypothetical phase II). Extrapolation to the DESI-Rubin overlap shows that a detection of large-scale structure with Lyα intensity mapping using next-generation imaging surveys is certain. Such detection would allow constraining 〈LLyα〉, and explore the constraining power of Lyα intensity mapping as a cosmological probe.

Research on Radar Target Detection Based on the Electromagnetic Scattering Imaging Algorithm and the YOLO Network

In this paper, the radar imaging technology based on the time-domain (TD) electromagnetic scattering algorithm is used to generate image datasets quickly and apply them to target detection research. Considering that radar images are different from optical images, this paper proposes an improved strategy for the traditional You Only Look Once (YOLO)v3 network to improve target detection accuracy on radar images. The speckle noise in radar images can cover the real information of a target image and increase the difficulty of target detection. The attention mechanisms are added to the traditional YOLOv3 network to strengthen the weight of the target region. By comparing the target detection accuracy under different attention mechanisms, an attention module with higher detection accuracy is obtained. The validity of the proposed detection network is verified on a simulation dataset, a measured real dataset, and a mixed dataset. This paper is about an interdisciplinary study of computational electromagnetics, remote sensing, and artificial intelligence. Experiments verify that the proposed composite network has better detection performance.

Early vascular healing after neXt-generation drug-eluting stent implantation in Patients with non-ST elevation acute Coronary syndrome: a randomized optical coherence Tomography imaging study (EXPECT).

Early vascular healing after drug-eluting stent (DES) implantation is associated with better outcomes and lower incidence of in-stent thrombosis. To examine vascular healing response in patients with non-ST elevation acute coronary syndrome (NSTE-ACS) undergoing percutaneous coronary intervention (PCI) guided by optical coherence tomography (OCT) versus angiography alone. Sixty patients were randomized 1:1:1 to OCT-guided PCI with 3-month OCT follow-up (O3 group, n = 20), angiography-guided PCI with 3-month OCT follow-up (A3 group, n = 20), or angiography-guided PCI with 6-month OCT follow-up (A6 group, n = 20). The primary endpoint was the proportion of covered struts at 3- or 6-month follow-up. The proportion of covered struts in the O3 group was significantly higher than in the A3 group (95.2% vs. 92.3%, p < 0.001), but lower than in the A6 group (95.2% vs. 97.4%, p < 0.001). The O3 group had a lower proportion of incomplete strut apposition (ISA) than the A3 group (0.46% vs. 0.76%, p = 0.006), and higher than the A6 group (0.46% vs. 0.27%, p = 0.018) at follow-up. The optimal cut-off value of ISA after implantation of DES for predicting stent coverage at 3 and 6-month follow-up was 200μm and 308μm, respectively. Only one patient experienced target lesion revascularization in the A3 group during a 3-year clinical follow-up. In patients with NSTE-ACS undergoing PCI with DES, OCT guidance achieved higher strut coverage compared with angiography guidance at 3-month follow-up. However, the difference in the strut coverage between the OCT-guided group and the angiography-guided group at 6 months indicates that the degree of endothelialization may be more time-dependent instead of invasive device guidance.

Detection of Floating Algae Blooms on Water Bodies Using PlanetScope Images and Shifted Windows Transformer Model

The increasing water temperature due to climate change has led to more frequent algae blooms and deteriorating water quality in coastal areas and rivers worldwide. To address this, we developed a deep learning-based model for identifying floating algae blooms using PlanetScope optical images and the Shifted Windows (Swin) Transformer architecture. We created 1,998 datasets from 105 scenes of PlanetScope imagery collected between 2018 and 2023, covering 14 water bodies known for frequent algae blooms. The methodology included data pre-processing, dataset generation, deep learning modeling, and inference result generation. The input images contained six bands, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), enhancing the model’s responsiveness to algae blooms. Evaluations were conducted using both single-period and multi-period datasets. The single-period model achieved a mean Intersection over Union (mIoU) between 72.18% and 76.47%, while the multi-period model significantly improved performance, with an mIoU of 91.72%. This demonstrates the potential of our model and highlights the importance of change detection in multi-temporal images for algae bloom monitoring. Additionally, the padding technique proposed in this study resolved the border issue that arises when mosaicking inference results from individual patches, providing a seamless view of the satellite scene.

Multimodal photoacoustic microscopy and optical coherence tomography ocular biomarker imaging in Alzheimer's disease in mice

Alzheimer's disease (AD) is a neurodegenerative disease characterized by amyloid beta (Aβ)-containing extracellular plaques and tau-containing intracellular neurofibrillary tangles. Reliable and more accessible biomarkers along with associated imaging methods are essential for early diagnosis and to develop effective therapeutic interventions. Described here is an integrated photoacoustic microscopy (PAM) and optical coherence tomography (OCT) dual-modality imaging system for multiple ocular biomarker imaging in an AD mouse model. Anti-Aβ-conjugated Au nanochains (AuNCs) were engineered and administered to the mice to provide molecular contrast of Aβ. The retinal vasculature structure and Aβ deposition in AD mice and wild-type (WT) mice were imaged simultaneously by dual-wavelength PAM. OCT distinguished significant differences in retinal layer thickness between AD and WT animals. With the unique ability of imaging the multiple ocular biomarkers via a coaxial multimodality imaging system, the proposed system provides a new tool for investigating the progression of AD in animal models, which could contribute to preclinical studies of AD.

Development of Fiber-Reinforced Polymer Composites for Additive Manufacturing and Multi-Material Structures in Sustainable Applications

This study investigates the mechanical properties of carbon and natural fiber-reinforced Polylactic Acid (PLA) and Polyethylene Terephthalate Glycol (PETG) composites produced via Additive Manufacturing (AM), focusing on Material Extrusion (MEX). The performance of filaments made from pre-consumer recycled PLA (rPLA) and PETG, with varying weight percentages of hemp and jute short fibers, was evaluated through tensile testing. Comparisons were made between the original filaments (PLA, carbon fiber-reinforced PLA [CF–PLA], and PETG) and their recycled versions. Multi-material compositions—neat PLA and PETG, single-graded (PLA + CF–PLA, PETG + CF–PETG), and multi-gradient (PLA + CF–PLA + PLA, PETG + CF–PETG + PETG)—were analyzed for mechanical properties. Optical microscope images of multi-material specimens were captured before and after fracture to assess failure mechanisms. The results indicate that the original CF–PETG filaments achieved a tensile strength of 50.14 MPa, which is higher than rPLA, PLA, and CF–PLA by 2%, 70%, and 6.7%, respectively. The re-manufactured PLA filaments reinforced with 7 wt% hemp fibers exhibited a tensile strength of 38.8 MPa, representing a 29% increase compared to the original PLA filaments and a 26% improvement over recycled PLA. Additionally, incorporating 7% jute fiber into PETG resulted in a tensile strength of 62.38 MPa, reflecting a 12% improvement over the original PETG filaments and a 15% increase compared to the recycled PETG filaments. Among specimens produced by AM, CF–PLA and rPLA demonstrated the highest tensile and compressive strengths. However, multi-material composites showed reduced mechanical performance compared to neat PLA and PETG, highlighting the need for improved interlayer adhesion. This study emphasizes the importance of optimizing material combinations and fiber reinforcement to enhance the mechanical properties of composites produced through AM.

Comparison and analysis of ophthalmic resources and service ability in Shanxi Province in 2014 and 2021

Objective: To compare and analyze the ophthalmic resources and service ability in Shanxi Province in 2014 and 2021, and to provide reference for the development planning of ophthalmology and eye care. Methods: In this cross-sectional study, a questionnaire survey was conducted among all ophthalmic institutions registered in the health administrative department in 11 cities of Shanxi Province by the end of 2014 and 2021 by using the National Ophthalmology Competency Resource Questionnaire (compiled by the Medical Administration and Hospital Authority of the National Health and Wellness Commission). The basic situations of ophthalmic institutions in 2014 and 2021, as well as the human resources, ophthalmic equipment configuration, technology and medical service capabilities, were recorded and compared. The chi-square test was used for statistical analysis. Results: A total of 270 questionnaires for 2014 were collected from 270 ophthalmic institutions, and 292 questionnaires for 2021 from 292 ophthalmic institutions. The institution categories in 2014 (197 general hospitals, 31 eye hospitals and 42 others) were statistically different from those in 2021 (182 general hospitals, 45 eye hospitals and 65 others) (P<0.05). The proportion of doctors with senior titles in ophthalmic institutions increased from 25.5% (346/1 358) in 2014 to 41.7% (580/1 391) in 2021, the proportion of senior technicians increased from 9.4% (14/148) to 25.3% (110/435), the proportion of doctors with junior titles decreased from 42.1% (572/1 358) to 26.5% (369/1 391), and the proportion of junior technicians decreased from 45.3% (67/148) to 39.1% (170/435). There was a significant difference in the distribution of professional titles between doctors and technicians in ophthalmic institutions (P<0.05). The number of doctors (130 to 28), nurses (152 to 50), technicians (33 to 11) and full-time optometrists (44 to 12) in the first-class ophthalmic institutions decreased, the increase of equipment with the ophthalmic A-ultrasound scan system, fundus camera and coherent optical tomography system (35.1%, 28.9% and 25.4%; P<0.05) ranked the top three, and the increase of performing the phacoemulsification procedure, intraocular lens implantation, intravitreal injection and iris surgery (30.2%, 24.7% and 20.7%; P<0.05) ranked the top three in 2021 as compared to 2014. Conclusions: The resources and service ability of ophthalmology in Shanxi Province developed in 2021 in comparison with 2014. However, there were some problems, such as changes in the type and structure of eye institutions, unreasonable proportions of professional titles, insufficient human resources in primary ophthalmic institutions, and slow improvement in the allocation of ophthalmology equipment and technical capabilities.

Underwater Optical Imaging: Methods, Applications and Perspectives

Underwater optical imaging is essential for exploring the underwater environment to provide information for planning and regulating underwater activities in various underwater applications, such as aquaculture farm observation, underwater topographical survey, and underwater infrastructure monitoring. Thus, there is a need to investigate the underwater imaging process and propose clear and long-range underwater optical imaging methods to fulfill the demands of academia and industry. In this manuscript, we classify the eighteen most commonly used underwater optical imaging methods into two groups regarding the imaging principle, (1) hardware and (2) software-based methods, each with an explanation of the theory, features, and applications. Furthermore, we also discuss the current challenges and future directions for improving the performance of current methods, such as improving the accuracy of underwater image formation model estimation, enlarging the underwater image dataset, proposing comprehensive underwater imaging evaluation metrics, estimating underwater depth and integrating different methods (e.g., hardware- and software-based methods for computational imaging) to promote the imaging performance not only in the laboratory but also in practical underwater scenarios.

Multicolor iLIFE (m-iLIFE) volume cytometry for high-throughput imaging of multiple organelles

To be able to resolve multiple organelles at high throughput is an incredible achievement. This will have immediate implications in a range of fields ranging from fundamental cell biology to translational medicine. To realize such a high-throughput multicolor interrogation modality, we have developed a light-sheet based flow imaging system that is capable of visualizing multiple sub-cellular components with organelle-level resolution. This is possible due to the unique optical design that combines an illumination system comprising two collinear light sheets illuminating the flowing cells and a dedicated dual-color 4f-detection, enabling simultaneous recording of multiple organelles. The system PSF sections up to 4 parallel microfluidic channels through which cells are flowing, and multicolor images of cell cross-sections are recorded. The data is then computationally processed (filtered using ML algorithm, shift-corrected, and merged) and combined to reconstruct the 3D multicolor volume. System testing is conducted using multicolor fluorescent nano-beads (size ∼175\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim 175$$\\end{document} nm) and flow-based imaging parameters (PSF size, motion-blur, flow rate, frame rate, and number of cell-sections) are determined for quality imaging. Drug treatment studies were carried out for healthy and cancerous HeLa cells to check the performance of the proposed system. The cells were treated with a drug (Vincristine, which is known to promote mitochondrial fission in cells), and the same is compared with untreated control cells. The proposed multicolor iLIFE system could screen ∼800\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim 800$$\\end{document} cells/min (at a flow speed of 2490μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2490 ~\\upmu$$\\end{document}m/s), and the drug treatment studies were carried out up to 24 h. Studies showed the disintegration of mitochondrial network and dysfunctional lysosomes and their accumulation at the cell membrane, which is a clear indication of cell apoptosis. Compared to control cells (untreated), the mortality is highest at a concentration of 500 nM post 12 h of drug treatment. With the capability of multiorganelle interrogation and organelle-level resolution, the multicolor iLIFE cytometry system is suitably placed to assist optical imaging and biomedical research.

Chiral, Topological, and Knotted Colloids in Liquid Crystals

The geometric shape, symmetry, and topology of colloidal particles often allow for controlling colloidal phase behavior and physical properties of these soft matter systems. In liquid crystalline dispersions, colloidal particles with low symmetry and nontrivial topology of surface confinement are of particular interest, including surfaces shaped as handlebodies, spirals, knots, multi-component links, and so on. These types of colloidal surfaces induce topologically nontrivial three-dimensional director field configurations and topological defects. Director switching by electric fields, laser tweezing of defects, and local photo-thermal melting of the liquid crystal host medium promote transformations among many stable and metastable particle-induced director configurations that can be revealed by means of direct label-free three-dimensional nonlinear optical imaging. The interplay between topologies of colloidal surfaces, director fields, and defects is found to show a number of unexpected features, such as knotting and linking of line defects, often uniquely arising from the nonpolar nature of the nematic director field. This review article highlights fascinating examples of new physical behavior arising from the interplay of nematic molecular order and both chiral symmetry and topology of colloidal inclusions within the nematic host. Furthermore, the article concludes with a brief discussion of how these findings may lay the groundwork for new types of topology-dictated self-assembly in soft condensed matter leading to novel mesostructured composite materials, as well as for experimental insights into the pure-math aspects of low-dimensional topology.

Developing Specific Emission Factors for Natural Gas Driven Pneumatic Devices.

A measurement study was conducted in 2023 to derive operator-specific emission factors for natural gas driven pneumatic devices at onshore production facilities in the United States. A total of 369 intermittent bleed and 26 continuous low-bleed pneumatic devices were measured using a high-volume sampler. Considering all intermittent bleed devices, the emission factor from this study was statistically lower than the factor in the revised Greenhouse Gas Reporting Rule (GHGRP) issued May 6, 2024. Intermittent devices were classified by inspection with an optical gas imaging camera as functioning or malfunctioning. Measurements of functioning intermittent bleed devices were statistically higher while measurements of malfunctioning intermittent bleed devices were statistically lower than the corresponding emission factor in the final revisions to the GHGRP. Measurements of continuous low-bleed pneumatic devices were statistically lower than the updated factor in the final revisions to GHGRP. Additionally, a Monte Carlo analysis was conducted to investigate the potential impact of measurement duration and sample size on emission factors derived for intermittent bleed devices. We conclude that while a short measurement duration may miss actuations of properly operating devices with low vent frequencies, potentially resulting in an emission factor with low bias, the sample size of greater than 300 measurements each greater than 3 min in duration, as included in this study, is unlikely to be biased low in a statistically significant manner, particularly when one considers the material contribution of malfunctioning devices.

Atomically precise Fluorescent Gold Nanocluster as a barrier-permeable and brain-specific imaging probe.

Photonic nanomaterials play a crucial role in facilitating the necessary signal for optical brain imaging, presenting a promising avenue for early diagnosis of brain-related disorders. However, the blood-brain barrier (BBB) presents a significant challenge, blocking the entry of most molecules or materials from the bloodstream into the brain. To overcome this, photonic nanocrystals in the form of gold clusters (LAuC) with size less than 3nm, have been developed, with Levodopa conjugated to LAuC (Dop@LAuC) for targeted brain imaging. Dop@LAuC crosses the BBB and emits in the near-infrared (NIR) wavelength, enabling real-time optical brain imaging. An in vitro BBB model using brain endothelial cells showed that 50% of Dop@LAuC crossed the barrier within 3 hours, compared to only 10% of LAuC, highlighting the enhanced ability of L-dopa-conjugated gold clusters to penetrate the BBB. In vivo optical imaging in healthy mice further confirmed the material's efficacy to cross BBB without compromising the barrier integrity.

Dry age-related macular degeneration classification from optical coherence tomography images based on ensemble deep learning architecture.

Dry age-related macular degeneration (AMD) is a retinal disease, which has been the third leading cause of vision loss. But current AMD classification technologies did not focus on the classification of early stage. This study aimed to develop a deep learning architecture to improve the classification accuracy of dry AMD, through the analysis of optical coherence tomography (OCT) images. We put forward an ensemble deep learning architecture which integrated four different convolution neural networks including ResNet50, EfficientNetB4, MobileNetV3 and Xception. All networks were pre-trained and fine-tuned. Then diverse convolution neural networks were combined. To classify OCT images, the proposed architecture was trained on the dataset from Shenyang Aier Excellence Hospital. The number of original images was 4,096 from 1,310 patients. After rotation and flipping operations, the dataset consisting of 16,384 retinal OCT images could be established. Evaluation and comparison obtained from three-fold cross-validation were used to show the advantage of the proposed architecture. Four metrics were applied to compare the performance of each base model. Moreover, different combination strategies were also compared to validate the merit of the proposed architecture. The results demonstrated that the proposed architecture could categorize various stages of AMD. Moreover, the proposed network could improve the classification performance of nascent geographic atrophy (nGA). In this article, an ensemble deep learning was proposed to classify dry AMD progression stages. The performance of the proposed architecture produced promising classification results which showed its advantage to provide global diagnosis for early AMD screening. The classification performance demonstrated its potential for individualized treatment plans for patients with AMD.

Severity Stratification of Coronary Artery Disease Using Novel Inner Ellipse-Based Foveal Avascular Zone Biomarkers.

Given the similarities between the retinal and coronary microvasculature, the retina holds promising potential to serve as a non-invasive screening tool for coronary artery disease (CAD). We aimed to develop novel inner ellipse-based metrics and discern whether foveal avascular zone (FAZ) alterations can serve as indicators for CAD presence and severity. Patients admitted to the Department of Cardiology who underwent coronary angiography were included. This resulted in an inclusion of 212 patients, of which 73 had no CAD. During the same visit, 6 × 6-mm (nominal size) fovea-centered optical coherence tomography angiography images of both eyes were acquired. The Gensini score (GS) was utilized to quantify CAD severity. Six known FAZ shape metrics were assessed and three novel biomarkers based on the inner ellipse were defined: absolute inner ellipse difference, Hausdorff distance, and Chamfer distance. Eight out of nine metrics showed significant associations with the GS in the left eye. However, significant differences across three CAD severity groups were only demonstrated by the novel metrics. Utilizing the Chamfer distance, age, and sex, patients with and without CAD could be distinguished with an average area under the curve (AUC) of 0.89 (95% confidence interval [CI], 0.84-0.95). Moreover, three CAD severity groups could be discerned with a macro average AUC of 0.77 (95% CI, 0.72-0.84). A comprehensive assessment of FAZ shape descriptors was performed, and a strong association with CAD was found. The inner ellipse-based biomarkers especially demonstrated high predictive abilities for CAD presence and severity.

Natural gas leakage detection from offshore platform by OGI camera and unsupervised deep learning

Natural gas leak from offshore platform poses a potential to cause explosion disaster and bring significant causalities and economic losses. Existing deep learning-based leak detection approaches are limited by the requirement of a large number of labeled leak datasets, and also has worse performance in the complex and changeable marine environment. This study proposes a detection approach of natural gas leakage from offshore platform by integrating optical gas imaging (OGI) camera and unsupervised deep probability learning. In this approach, unsupervised deep learning is applied to learn the changeable infrared features of offshore platform, and variational Bayesian inference is integrated to provide the larger epistemic uncertainty contour corresponding to the infrared natural gas plume. An epistemic uncertainty-based detection score is proposed as the detection criterion to improve the accuracy of natural gas plume detection and localization. An OGI imaging experiment of natural gas leak from offshore platform is conducted to construct the benchmark dataset. With such datasets, two pre-defined parameters, namely Monte Carlo sampling number m = 100 and dropout probability p=0.1 are determined to guarantee the detection accuracy and efficiency. Comparison between the proposed approach and prevalent unsupervised deep learning approach is also conducted. The results demonstrate that our proposed approach has the higher detection accuracy with AUC = 0.9753, as well as the real-time capability with inference time of 4s/frame. Overall, our proposed approach provides a more accurate and generalized approach of natural gas detection for safety monitoring and detection management of offshore platforms.

NIR-II Anti-Stokes Luminescence Nanocrystals with 1710 nm Excitation for in vivo Bioimaging.

Anti-Stokes luminescence (ASL) based on lanthanide nanocrystals holds immense promise for in vivo optical imaging and bio-detection, which benefits from filtered autofluorescence. However, the current longest emission and excitation wavelengths of lanthanide ASL system were shorter than 1200 nm and 1532 nm, respectively, which limited tissue penetration depth and caused low signal-to-noise ratio (SNR) of in vivo imaging due to tissue scattering and water absorption. In this work, we extended the excitation wavelength to 1710 nm with the second near-infrared (NIR-II, 1000-1700 nm) emission up to 1650 nm through a novel ASL nanocrystal LiYF4:10%Tm@LiYF4:70%Er@LiYF4. Compared with 1532 nm excited ASL nanoprobes, the 1710 nm excited nanocrystals could improve in vivo imaging SNR by 12.72 folds. Based on this excellent imaging performance of the proposed ASL nanoprobes, three-channel in vivo dynamic multiplexed imaging was achieved, which quantitatively revealed metabolic rates of intestinal dynamics and liver enrichment under anesthetized and awake states. This innovative ASL nanoprobes and dynamic multiplexed imaging technology would be conducive to optimizing dosing regimen and treatment plans across various physiological conditions.

Multi-category sorting of plastic waste using Swin Transformer: A vision-based approach

Sorting out plastic waste (PW) from municipal solid waste (MSW) by material type is crucial for reutilization and pollution reduction. However, current automatic separation methods are costly and inefficient, necessitating an advanced sorting process to ensure high feedstock purity. This study introduces a Swin Transformer-based model for effectively detecting PW in real-world MSW streams, leveraging both morphological and material properties. And, a dataset comprising 3560 optical images and infrared spectra data was created to support this task. This vision-based system can localize and classify PW into five categories: polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS). Performance evaluations reveal an accuracy rate of 99.75% and a mean Average Precision (mAP50) exceeding 91%. Compared to popular convolutional neural network (CNN)-based models, this well-trained Swin Transformer-based model offers enhanced convenience and performance in five-category PW detection task, maintaining a mAP50 over 80% in the real-life deployment. The model's effectiveness is further supported by visualization of detection results on MSW streams and principal component analysis of classification scores. These results demonstrate the system's significant effectiveness in both lab-scale and real-life conditions, aligning with global regulations and strategies that promote innovative technologies for plastic recycling, thereby contributing to the development of a sustainable circular economy.

NIR‐II Anti‐Stokes Luminescence Nanocrystals with 1710 nm Excitation for in vivo Bioimaging

Anti‐Stokes luminescence (ASL) based on lanthanide nanocrystals holds immense promise for in vivo optical imaging and bio‐detection, which benefits from filtered autofluorescence. However, the current longest emission and excitation wavelengths of lanthanide ASL system were shorter than 1200 nm and 1532 nm, respectively, which limited tissue penetration depth and caused low signal‐to‐noise ratio (SNR) of in vivo imaging due to tissue scattering and water absorption. In this work, we extended the excitation wavelength to 1710 nm with the second near‐infrared (NIR‐II, 1000‐1700 nm) emission up to 1650 nm through a novel ASL nanocrystal LiYF4:10%Tm@LiYF4:70%Er@LiYF4. Compared with 1532 nm excited ASL nanoprobes, the 1710 nm excited nanocrystals could improve in vivo imaging SNR by 12.72 folds. Based on this excellent imaging performance of the proposed ASL nanoprobes, three‐channel in vivo dynamic multiplexed imaging was achieved, which quantitatively revealed metabolic rates of intestinal dynamics and liver enrichment under anesthetized and awake states. This innovative ASL nanoprobes and dynamic multiplexed imaging technology would be conducive to optimizing dosing regimen and treatment plans across various physiological conditions.

The possibilities of using adaptive optics in modern ophthalmology

Until recently, the assessment of individual retinal cells was possible only with the help of histological examination, since such retinal imaging methods as scanning laser ophthalmoscopy and optical coherence tomography had low resolution to obtain images of structures at the cellular level, which was mainly due to aberrations caused by the optics of the eye. Adaptive optics technology has improved the performance of optical systems by correcting optical wavefront aberrations. Adaptive optics allows noninvasively visualizing the retina at the microscopic level in vivo, providing the opportunity to analyze individual structures such as photoreceptors, blood vessels, nerve fibers, ganglion cells and a lattice plate. Adaptive optics imaging in patients with diabetic retinopathy makes it possible to accurately determine the spatial distribution of cones, a decrease in which is associated with the presence of diabetic retinopathy and an increase in the severity of the disease. The detection of differences in cone distribution density between the control group and patients with diabetes mellitus without clinical signs of diabetic retinopathy may contribute to its early diagnosis, as well as a deeper understanding of the consequences of changes in the photoreceptor apparatus. Adaptive optics imaging methods are able to identify disorders of photoreceptor cells and assess the degree of progression of age-related macular degeneration, which definitely expands diagnostic capabilities at the early stages of its detection. Assessment of the condition of nerve fiber bundles through the use of Adaptive optics helps to identify changes associated with glaucoma, and also provides the ability to visualize details that cannot be evaluated using optical coherence tomography. Adaptive optics imaging allows you to directly measure the wall of retinal vessels and the diameter of their lumen. The ratio of wall thickness to vessel lumen and the cross-sectional area of the vessel wall directly reflect the remodeling process and can be used for the purpose of early diagnosis and monitoring of hypertension.

Retraction Note: Application of optical medical equipment imaging detection and fire needle therapy for intestinal mucosal barrier function in patients with intestinal diseases

Retraction Note: Application of optical medical equipment imaging detection and fire needle therapy for intestinal mucosal barrier function in patients with intestinal diseases