High-resolution Imaging Research Articles

Internal pipe corrosion assessment method in water distribution system using ultrasound and convolutional neural networks

Internal pipe corrosion within water distribution systems leads to iron oxide deposits on pipe walls, potentially contaminating the water supply. Consuming iron oxide-contaminated water can cause significant health issues such as gastrointestinal infections, dermatological problems, and lymph node complications. Therefore, non-destructive and continuous monitoring of pipe corrosion is imperative for water sustainability initiatives. This study introduces a dual-mode methodology utilizing advanced ultrasound technology and convolutional neural networks (CNN) to quantify pipe corrosion. Scanning acoustic microscopy (SAM) employs high-frequency ultrasound to generate high-resolution images of pipe thickness, indicating iron oxide accumulation. SAM also captures internal pipe data to measure iron oxide concentration in the water. This data, analyzed by CNN, achieves an impressive 95% accuracy. This dual-mode system effectively assesses both the extent of pipe corrosion and water contamination, exemplifying the successful integration of SAM and CNN for precise and reliable monitoring.

Water body extraction from high spatial resolution remote sensing images based on enhanced U-Net and multi-scale information fusion

Employing deep learning techniques for the semantic segmentation of remote sensing images has emerged as a prevalent approach for acquiring information about water bodies. Yet, current models frequently fall short in accurately extracting water bodies from high-resolution remote sensing images, as these images often present intricate details of terrestrial objects and complex backgrounds. Vegetation, shadows, and other objects close to water boundaries have increased similarity to water bodies. Moreover, water bodies in high-resolution images have different boundary complexities, shapes, and sizes. This situation makes it somewhat challenging to accurately distinguish water bodies in high-resolution images. To overcome these difficulties, this paper presents a novel network model named EU-Net, specifically designed to extract water bodies from high-resolution remote sensing images. The proposed EU-Net model, with U-net as the backbone network, incorporates improved residual connections and attention mechanisms, and designs multi-scale dilated convolution and multi-scale feature fusion modules to enhance water body extraction performance in complex scenarios. Specifically, in the proposed model, improved residual connections are introduced to enable the learning of more complex features; the attention mechanism is employed to improve the model's discriminative ability by focusing on important channels and spatial areas. The implemented multi-scale dilated convolution technique enhances the model's receptive field while maintaining the same number of parameters. The designed multi-scale feature fusion module is capable of processing both small-scale details and large-scale structures in images, while simultaneously modeling the spatial context relationships of features at different scales. Experimental results validate the superior performance of EU-Net in accurately identifying water bodies from high-resolution remote sensing images, outperforming current models in terms of water extraction accuracy.

High resolution imaging of human development: shedding light on contrast agents.

Visualizing (micro)vascular structures remains challenging for researchers and clinicians due to limitations in traditional radiological imaging methods. Exploring the role of vascular development in craniofacial malformations in experimental settings can enhance understanding of these processes, with the effectiveness of high-resolution imaging techniques being crucial for successful research in this field. Micro-CT imaging offers 3D microstructural insights, but requires contrast-enhancing staining agents (CESAs) for visualizing (micro)-vascular tissues, known as contrast-enhanced micro-CT (CECT). As effective contrast agents are crucial for optimal visualization, this review focuses on comparative studies investigating such agents for micro-vascular tissue imaging using micro-CT. Furthermore, we demonstrate the utilization of B-Lugol solution as a promising contrast agent for acquiring high-quality micro-CT images of (micro)vascular structures in human embryonic samples. This scoping review followed Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols. PubMed database provided relevant articles, screened initially by title and abstract. Inclusion and exclusion criteria defined outcomes of interest. From an initial search, 273 records were identified, narrowed down to 9 articles after applying our criteria. Additionally, two articles were added through citation searching. This, a total of 11 articles were incorporated in this study. This micro-CT contrast agent review underscores the need for tailored choices based on research goals. Both Barium sulfate and Iodine-based agents showing excellent results, providing high resolution (micro) vascular content, especially in ex-vivo specimens. However, careful consideration of protocols and tissue characteristics remains imperative for optimizing the effectiveness of micro-CT imaging for the study of cranio-facial vascular development.

Improved abdominal T1 weighted imaging at 0.55T.

Breath-held fat-suppressed volumetric T1-weighted MRI is an important and widely-used technique for evaluating the abdomen. Both fat-saturation and Dixon-based fat-suppression methods are used at conventional field strengths; however, both have challenges at lower field strengths (<1.5T) due to insufficient fat suppression and/or inadequate resolution. Specifically, at lower field strengths, fat saturation often fails due to the short T1 of lipid; and Cartesian Dixon imaging provides poor spatial resolution due to the need for a long ∆TE, due to the smaller ∆f between water and lipid. The purpose of this work is to demonstrate a new approach capable of simultaneously achieving excellent fat suppression and high spatial resolution on a 0.55T whole-body system. We applied 3D stack-of-spirals Dixon imaging at 0.55T, with compensation of concomitant field phase during reconstruction. The spiral readouts make efficient use of the requisite ∆TE. We compared this with 3D Cartesian Dixon imaging. Experiments were performed in 2 healthy and 10 elevated liver fat volunteers. Stack-of-spirals Dixon imaging at 0.55T makes excellent use of the required ∆TE, provided high SNR efficiency and finer spatial resolution (1.7 × 1.7 × 5 mm3) compared Cartesian Dixon (3.5 × 3.5 × 5 mm3), within a 17-s breath-hold. We observed successful fat suppression, and improved definition of structures such as the liver, kidneys, and bowel. We demonstrate that high-resolution single breath-hold volumetric abdominal T1-weighted imaging is feasible at 0.55T using spiral sampling and concomitant field correction. This is an attractive alternative to existing Cartesian-based methods, as it simultaneously provides high-resolution and excellent fat-suppression.

A biopsy-sized 3D skin model with a perifollicular vascular plexus enables studying immune cell trafficking in the skin

Human skin vasculature features a unique anatomy in close proximity to the skin appendages and acts as a gatekeeper for constitutive lymphocyte trafficking to the skin. Approximating such structural complexity and functionality in 3D skin models is an outstanding tissue engineering challenge. In this study, we leverage the capabilities of the digital-light-processing bioprinting to generate an anatomically-relevant and miniaturized 3D skin-on-a-chip (3D-SoC) model in the size of a 6 mm punch biopsy. The 3D-SoC contains a perfusable vascular network resembling the superficial vascular plexus of the skin and closely surrounding bioengineered hair follicles. The perfusion capabilities of the 3D-SoC enables the circulation of immune cells, and high-resolution imaging of the immune cell-endothelial cell interactions, namely tethering, rolling, and extravasation in real-time. Moreover, the vascular pattern in 3D-SoC captures the physiological range of shear rates found in cutaneous blood vessels and allows for studying the effect of shear rate on T cell trafficking. In 3D-SoC, as expected, in vitro-polarized T helper 1 (Th1) cells show a stronger attachment on the vasculature compared to naïve T cells. Both naïve and T cells exhibit higher retention in the low-shear zones in the early stages (<5 min) of T cell attachment. Interestingly, at later stages T cell retention rate becomes independent of the shear rate. The attached Th1 cells further transmigrate from the vessel walls to the extracellular space and migrate toward the bioengineered hair follicles and interfollicular epidermis. When the epidermis is not present, Th1 cell migration toward the epidermis is significantly hindered, underscoring the role of epidermal signals on T cell infiltration. Our data validates the capabilities of 3D-SoC model to study the interactions between immune cells and skin vasculature in the context of epidermal signals. The biopsy-sized 3D-SoC model in this study represents a new level of anatomical and cellular complexity, and brings us a step closer to generating a truly functional human skin with its tissue-specific vasculature and appendages in the presence of circulating immune cells.

Uncertainty quantification for velocity measurement with 2D2C particle image velocimetry

Abstract In this paper, a theoretical model of dimensionless instantaneous and average velocity measurement uncertainty quantification with 2D2C particle image velocimetry (PIV) is established under the framework of generally adopted international uncertainty quantification standards. The effectiveness of the model is verified using uniform flow field testing data. Combined with semi-quantitative analysis of the theoretical model, uncertainty control suggestions for PIV measurements are given. The major sources affecting the uncertainty of instantaneous velocity measurements are the reference velocity, particle instantaneous pixel displacement, and their correlation term. For average velocity measurement uncertainty quantification, the uncertainty of particle average pixel displacement is effectively controlled by taking a large number of particle images. Thus, three single-component terms — the calibration factor, particle average pixel displacement and reference velocity — and two correlation terms — the particle average pixel displacement–calibration factor and the particle average pixel displacement–reference velocity — all make an important contribution to the average velocity measurement uncertainty. To reduce the uncertainty of PIV velocity measurement, one can reduce the reference velocity measurement uncertainty, optimize the PIV algorithm and improve the calibration factor by applying a high spatial resolution imaging system in experiments. In addition, reducing the reference velocity measurement uncertainty and improving the spatial resolution are key feasible methods.

Intra-aneurysmal high-resolution 4D MRI flow imaging for hemodynamic imaging markers in intracranial aneurysm instability.

Prediction of aneurysm instability is crucial to guide treatment decisions and to select appropriate patients with unruptured intracranial aneurysms (IAs) for preventive treatment. High resolution four-dimensional magnetic resonance (4D MRI) flow imaging and 3D quantification of aneurysm morphology could offer insights and new imaging markers for aneurysm instability. In this cross-sectional study, we aim to identify 4D MRI flow imaging markers for aneurysm instability by relating hemodynamics in the aneurysm sac to 3D morphological proxy parameters for aneurysm instability. In 35 patients with 37 unruptured IAs, a 3T MRA and a 7T 4D flow MRI scan was performed. Five hemodynamic parameters -peak-systolic (WSSMAX) and time-averaged wall shear stress (WSSMEAN), oscillatory shear index (OSI), mean velocity, and velocity pulsatility index (vPI)-were correlated to six 3D morphology proxy parameters of aneurysm instability -major axis length, volume, surface area (all three size parameters), flatness, shape index, and curvedness -by Pearson's correlation with 95% confidence intervals (CI). Scatterplots of hemodynamic parameters that correlated with IA size (major axis length) were created. WSSMAX and WSSMEAN correlated negatively with all three size parameters (strongest for WSSMEAN with volume (r = -0.70, 95% CI -0.83 to -0.49)) and OSI positively (strongest with major axis length (r = 0.87, 95% CI 0.76 to 0.93)). WSSMAX and WSSMEAN correlated positively with shape index (r = 0.61, 95% CI 0.36 to 0.78 and r = 0.49, 95% CI 0.20 to 0.70, respectively) and OSI negatively (r = 0.82, 95% CI -0.9 to -0.68). WSSMEAN and mean velocity correlated negatively with flatness (r = -0.35, 95% CI -0.61 to -0.029 and r = 0.33, 95% CI -0.59 to 0.007, respectively) and OSI positively (r = 0.54, 95% CI 0.26 to 0.74). vPI did not show any statistically significant correlation. Out of the five included hemodynamic parameters, WSSMAX, WSSMEAN, and OSI showed the strongest correlation with morphological 3D proxy parameters of aneurysm instability. Future studies should assess these promising new imaging marker parameters for predicting aneurysm instability in longitudinal cohorts of IA patients. IA = intracranial aneurysm; 3D = three dimensional; 4D MRI flow = four-dimensional Magnetic Resonance Imaging flow; TOF-MRA = Time-of-flight Magnetic Resonance Angiography; WSS = wall shear stress; WSSMAX = WSS calculated at peak systole; WSSMEAN = time averaged WSS; OSI = oscillatory shear index; vPI = velocity pulsatility index.

From pixels to plant health: accurate detection of banana Xanthomonas wilt in complex African landscapes using high-resolution UAV images and deep learning

Bananas and plantains are vital for food security and smallholder livelihoods in Africa, but diseases pose a significant threat. Traditional disease surveillance methods, like field visits, lack accuracy, especially for specific diseases like Xanthomonas wilt of banana (BXW). To address this, the present study develops a Deep-Learning system to detect BXW-affected stems in mixed-complex landscapes within the Eastern Democratic Republic of Congo. RGB (Red, Green, Blue) and multispectral (MS) images from unmanned aerial vehicles UAVs were utilized using pansharpening algorithms for improved data fusion. Using transfer learning, two deep-learning model architectures were used and compared in our study to determine which offers better detection capabilities. A single-stage model, Yolo-V8, and the second, a two-stage model, Faster R-CNN, were both employed. The developed system achieves remarkable precision, recall, and F1 scores ranging between 75 and 99% for detecting healthy and BXW-infected stems. Notably, the RGB and PAN UAV images perform exceptionally well, while MS images suffer due to the lower spatial resolution. Nevertheless, specific vegetation indexes showed promising performance detecting healthy banana stems across larger areas. This research underscores the potential of UAV images and Deep Learning models for crop health assessment, specifically for BXW in complex African systems. This cutting-edge deep-learning approach can revolutionize agricultural practices, bolster African food security, and help farmers with early disease management. The study’s novelty lies in its Deep-Learning algorithm development, approach with recent architectures (Yolo-V8, 2023), and assessment using real-world data, further advancing crop-health assessment through UAV imagery and deep-learning techniques.

CResU-Net: a method for landslide mapping using deep learning

Landslides, which can occur due to earthquakes and heavy rainfall, pose significant challenges across large areas. To effectively manage these disasters, it is crucial to have fast and reliable automatic detection methods for mapping landslides. In recent years, deep learning methods, particularly convolutional neural and fully convolutional networks, have been successfully applied to various fields, including landslide detection, with remarkable accuracy and high reliability. However, most of these models achieved high detection performance based on high-resolution satellite images. In this research, we introduce a modified Residual U-Net combined with the Convolutional Block Attention Module, a deep learning method, for automatic landslide mapping. The proposed method is trained and assessed using freely available data sets acquired from Sentinel-2 sensors, digital elevation models, and slope data from ALOS PALSAR with a spatial resolution of 10 m. Compared to the original ResU-Net model, the proposed architecture achieved higher accuracy, with the F1-score improving by 9.1% for the landslide class. Additionally, it offers a lower computational cost, with 1.38 giga multiply-accumulate operations per second (GMACS) needed to execute the model compared to 2.68 GMACS in the original model. The source code is available at https://github.com/manhhv87/LandSlideMapping.git.

The core spindle pole body scaffold Ppc89 links the pericentrin ortholog Pcp1 to the fission yeast spindle pole body via an evolutionarily conserved interface.

Centrosomes and spindle pole bodies (SPBs) are important for mitotic spindle formation and serve as cellular signaling platforms. Although centrosomes and SPBs differ in morphology, many mechanistic insights into centrosome function have been gleaned from SPB studies. In the fission yeast Schizosaccharomyces pombe, the ɑ-helical protein Ppc89, identified based on its interaction with the septation initiation network scaffold Sid4, comprises the SPB core. High-resolution imaging has suggested that SPB proteins assemble on the Ppc89 core during SPB duplication, but such interactions are undefined. Here, we define a connection between Ppc89 and the essential pericentrin Pcp1. Specifically, we found that a predicted third helix within Ppc89 binds the Pcp1 PACT domain complexed with calmodulin. Ppc89 helix 3 contains similarity to PINC motifs found in the centrosomal proteins fly SAS-6 and human Cep57 and also to the S. cerevisiae SPB protein Spc42. These motifs bind pericentrin-calmodulin complexes and AlphaFold2 models suggest a homologous complex assembles in all four organisms. Mutational analysis of the S. pombe complex supports the importance of Ppc89-Pcp1 binding interface in vivo. Our studies provide insight into the core architecture of the S. pombe SPB and suggest an evolutionarily conserved mechanism of scaffolding pericentrin-calmodulin complexes for mitotic spindle formation.

The core spindle pole body scaffold Ppc89 links the pericentrin ortholog Pcp1 to the fission yeast spindle pole body via an evolutionarily conserved interface.

Centrosomes and spindle pole bodies (SPBs) are important for mitotic spindle formation and serve as cellular signaling platforms. Although centrosomes and SPBs differ in morphology, many mechanistic insights into centrosome function have been gleaned from SPB studies. In the fission yeast Schizosaccharomyces pombe, the ɑ-helical protein Ppc89, identified based on its interaction with the septation initiation network scaffold Sid4, comprises the SPB core. High-resolution imaging has suggested that SPB proteins assemble on the Ppc89 core during SPB duplication, but such interactions are undefined. Here, we define a connection between Ppc89 and the essential pericentrin Pcp1. Specifically, we found that a predicted third helix within Ppc89 binds the Pcp1 PACT domain complexed with calmodulin. Ppc89 helix 3 contains similarity to PINC motifs found in the centrosomal proteins fly SAS-6 and human Cep57 and also to the S. cerevisiae SPB protein Spc42. These motifs bind pericentrin-calmodulin complexes and AlphaFold2 models suggest a homologous complex assembles in all four organisms. Mutational analysis of the S. pombe complex supports the importance of Ppc89-Pcp1 binding interface in vivo. Our studies provide insight into the core architecture of the S. pombe SPB and suggest an evolutionarily conserved mechanism of scaffolding pericentrin-calmodulin complexes for mitotic spindle formation.

Oversampling for Enhanced Spatial Resolution of Zebrafish by Top-Hat IR-MALDESI-MSI.

Mass spectrometry imaging (MSI) has become a significant tool for measuring chemical species in biological tissues, where much of the impact of these platforms lies in their capability to report the spatial distribution of analytes for correlation to sample morphology. As a result, enhancement of spatial resolution has become a frontier of innovation in the field, and necessary developments are dependent on the ionization source. More particularly, laser-based imaging sources may require modifications to the optical train or alternative sampling techniques. These challenges are heightened for systems with infrared (IR) lasers, as their operating wavelength generates spot sizes that are inherently larger than their ultraviolet counterparts. Recently, the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source has shown the utility of a diffractive optical element (DOE) to produce square ablation patterns, termed top-hat IR-MALDESI. If the DOE optic is combined with oversampling methods, smaller ablation volumes can be sampled to render higher spatial resolution imaging experiments. Further, this approach enables reproducible spot sizes and ablation volumes for better comparison between scans. Herein, we investigate the utility of oversampling with top-hat IR-MALDESI to enhance the spatial resolution of measured lipids localized within the head of sectioned zebrafish tissue. Four different spatial resolutions were evaluated for data quality (e.g., mass measurement accuracy, spectral accuracy) and quantity of annotations. Other experimental parameters to consider for high spatial resolution imaging are also discussed. Ultimately, 20 μm spatial resolution was achieved in this work and supports feasibility for use in future IR-MALDESI studies.

Oversampling for Enhanced Spatial Resolution of Zebrafish by Top-Hat IR-MALDESI-MSI.

Mass spectrometry imaging (MSI) has become a significant tool for measuring chemical species in biological tissues, where much of the impact of these platforms lies in their capability to report the spatial distribution of analytes for correlation to sample morphology. As a result, enhancement of spatial resolution has become a frontier of innovation in the field, and necessary developments are dependent on the ionization source. More particularly, laser-based imaging sources may require modifications to the optical train or alternative sampling techniques. These challenges are heightened for systems with infrared (IR) lasers, as their operating wavelength generates spot sizes that are inherently larger than their ultraviolet counterparts. Recently, the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source has shown the utility of a diffractive optical element (DOE) to produce square ablation patterns, termed top-hat IR-MALDESI. If the DOE optic is combined with oversampling methods, smaller ablation volumes can be sampled to render higher spatial resolution imaging experiments. Further, this approach enables reproducible spot sizes and ablation volumes for better comparison between scans. Herein, we investigate the utility of oversampling with top-hat IR-MALDESI to enhance the spatial resolution of measured lipids localized within the head of sectioned zebrafish tissue. Four different spatial resolutions were evaluated for data quality (e.g., mass measurement accuracy, spectral accuracy) and quantity of annotations. Other experimental parameters to consider for high spatial resolution imaging are also discussed. Ultimately, 20 μm spatial resolution was achieved in this work and supports feasibility for use in future IR-MALDESI studies.

Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds.

Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Pseudopus pannonicus (Squamata), the largest known anguid lizard-Redescription of the type material and new specimens from the Neogene and Quaternary of Hungary and Poland.

Herein, we revise the material of the extinct taxon Pseudopus pannonicus from Central Europe, the largest known anguid lizard and iconic member of herpetofaunas from the Upper Cenozoic of Europe. The geographical position of Polgárdi 2, the type locality of P. pannonicus, as well as several other closely located important localities make Central Europe a valuable area of high interest for studies regarding European Cenozoic palaeoherpetological assemblages. We clarified the nature of the type material of Pseudopus pannonicus, showing that it consisted not only of the five specimens originally figured. Instead, the syntype series also includes a considerable number of specimens from Polgárdi 2 that were only described or figured 12 years after the original description of P. pannonicus. Detailed osteological descriptions are provided for specimens from the type series and Polish specimens, with the aid of high-resolution imaging (SEM and μCT scanning), and intraspecific variability is discussed. The articular surface with the lappet of the parietal overlapping the frontal is discussed as a character potentially relevant for the diagnosis of P. pannonicus. We updated the identification of several specimens of P. pannonicus and discussed the biogeographic implications of such revisions. In Poland, P. pannonicus is an abundant component of Neogene and early Quaternary herpetofaunas, known with certainty from the Middle Miocene of Przeworno (the oldest known occurrence of the species globally), the Early-Late Pliocene of Węże I, the Late Pliocene of Rębielice Królewskie I, the Late Pliocene of Węże II, and the Early Pleistocene of Kadzielnia (one of the youngest occurrences documented globally). An indeterminate anguine with Anguis affinities is newly reported from Rębielice Królewskie II. The taxonomic status of other large anguids from the Neogene of Europe is discussed and we conclude that most are junior synonyms of P. pannonicus. We also show that another purported synonym, that is, Ophisaurus intermedius from the Early Pleistocene of Romania, is instead a nomen nudum. We conducted phylogenetic analyses (18 taxa, 65 characters) to understand the relationship of P. pannonicus relative to other anguid representatives and anguid-related group (i.e., glyptosaurids). A single most parsimonious tree (length: 134 steps) was recovered. The clade Pseudopus is stable, comprising the two distinct sister subclades [Pseudopus laurillardi + Pseudopus ahnikoviensis] and [Pseudopus pannonicus + Pseudopus apodus]. These phylogenetic results are in accordance with previously published works.

Extending the emission peak tail of indole cyanine for deep-near-infrared bioimaging

We propose a novel strategy for tailoring the structure of fluorescent molecules to achieve emission at the tail end of the NIR-II window. The favorable spectroscopic properties and low cytotoxicity of YNs make them powerful tools for bioimaging. Notably, YN-4 exhibits a brightness 2.5 times greater than YN-3, 6 times that of IR-783, and 5 times that of ICG. This enhanced brightness enabled high-resolution imaging of mouse thoracic and abdominal cavities, tumor vasculature, and real-time monitoring of gastrointestinal motility using YN-4. Furthermore, covalent grafting of glucose onto the YN-Glu scaffold significantly improved tumor-targeting capability and facilitated tracking of glucose metabolism. This work aims to extend the application of fluorescent molecule imaging beyond the NIR-IIa window.

High-Fidelity and Efficient Pluralistic Image Completion with Transformers.

Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity. However, due to some inherent properties (e.g., local inductive prior, spatial-invariant kernels), CNNs do not perform well in understanding global structures or naturally support pluralistic completion. Recently, transformers demonstrate their power in modeling the long-term relationship and generating diverse results, but their computation complexity is quadratic to input length, thus hampering the application in processing high-resolution images. This paper brings the best of both worlds to pluralistic image completion: appearance prior reconstruction with transformer and texture replenishment with CNN. The former transformer recovers pluralistic coherent structures together with some coarse textures, while the latter CNN enhances the local texture details of coarse priors guided by the high-resolution masked images. To decode diversified outputs from transformers, auto-regressive sampling is the most common method, but with extremely low efficiency. We further overcome this issue by proposing a new decoding strategy, temperature annealing probabilistic sampling (TAPS), which firstly achieves more than 70× speedup of inference at most, meanwhile maintaining the high quality and diversity of the sampled global structures. Moreover, we find the full CNN architecture will lead to suboptimal solutions for guided upsampling. To render more realistic and coherent contents, we design a novel module, named texture-aware guided attention, to concurrently consider the procedures of texture copy and generation, meanwhile raising several important modifications to solve the boundary artifacts. Through dense experiments, we found the proposed method vastly outperforms state-of-the-art methods in terms of four aspects: 1) large performance boost on image fidelity even compared to deterministic completion methods; 2) better diversity and higher fidelity for pluralistic completion; 3) exceptional generalization ability on large masks and generic dataset, like ImageNet. 4) Much higher decoding efficiency over previous auto-regressive based methods.

Detection of proteins with ascorbic acid-capped gold nanoparticles: a simple and highly sensitive colorimetric assay.

We report a simple and highly sensitive colorimetric method for the detection and quantification of proteins, based on the aggregation of ascorbic acid (AA) capped gold nanoparticles (AuNPs) by proteins. The interactions between our AuNPs and nine different proteins of various sizes and shapes (cytochrome C (12 kDa), lysozyme (14.3 kDa), myoglobin (17 kDa), human serum albumin (66 kDa), bovine serum albumin (66.4 kDa), human transferrin (80 kDa), aldolase (160 kDa), catalase (240 kDa), and human H-ferritin (500 kDa)) generated similar AuNPs-protein absorption spectra in a concentration-dependent manner in the range of 1-15 nM. Upon the addition of a protein, the UV-visible spectra of AuNPs-protein conjugates shifted from 524 nm for the AuNps alone to longer wavelength (600-750 nm) due to the presence of one of these proteins. This bathochromic shift is accompanied by a color change from a cherry red, to dark purple, and then light grey or colorless if excess protein has been added, indicating the formation of AuNPs-protein conjugates followed by protein-induced aggregation of the AuNPs. High-resolution transmission electron microscopy images revealed uniformly distributed spherical nanoparticles with an average size of 27.5 ± 15.2 nm, increasing in size to 39.6 ± 12.9 nm upon the addition of a protein, indicating the formation of AuNPs-protein conjugates in solution. A general mechanism for the protein-induced aggregation of our AuNPs is proposed. The consistent behavior observed with the nine proteins tested in our study suggests that our assay can be universally applied for the quantification of pure proteins in a solution, regardless of size, shape, or molecular weight.

Simultaneous Multifocal Plane Fourier Ptychographic Microscopy Utilizing a Standard RGB Camera

Fourier ptychographic microscopy (FPM) is a computational imaging technology that can acquire high-resolution large-area images for applications ranging from biology to microelectronics. In this study, we utilize multifocal plane imaging to enhance the existing FPM technology. Using an RGB light emitting diode (LED) array to illuminate the sample, raw images are captured using a color camera. Then, exploiting the basic optical principle of wavelength-dependent focal length variation, three focal plane images are extracted from the raw image through simple R, G, and B channel separation. Herein, a single aspherical lens with a numerical aperture (NA) of 0.15 was used as the objective lens, and the illumination NA used for FPM image reconstruction was 0.08. Therefore, simultaneous multifocal plane FPM with a synthetic NA of 0.23 was achieved. The multifocal imaging performance of the enhanced FPM system was then evaluated by inspecting a transparent organic light-emitting diode (OLED) sample. The FPM system was able to simultaneously inspect the individual OLED pixels as well as the surface of the encapsulating glass substrate by separating R, G, and B channel images from the raw image, which was taken in one shot.

Vision transformer and CNN-based skin lesion analysis: classification of monkeypox

AbstractMonkeypox is an important health problem. Rapid diagnosis of monkeypox skin lesions and emergency isolation when necessary is essential. Also, some skin lesions, such as melanoma, can be fatal and must be rapidly distinguished. However, in some cases, it is difficult to distinguish the lesions visually. Methods such as dermoscopy, high-resolution ultrasound imaging, etc. can be used for better observation. But these methods are often based on qualitative analysis, subjective and time-consuming. Therefore, in this study, a quantitative and objective classification tool has been developed to assist dermatologists and scientists. The proposed system classifies seven skin lesions, including monkeypox. A popular approach Vision Transformer and some popular deep learning convolutional networks have been trained with the transfer learning approach and all results have been compared. Then, the models that show the best accuracy score have been combined to make the final prediction using bagging-ensemble learning. The proposed ensemble-based system produced 81.91% Accuracy, 65.94% Jaccard, 87.16% Precision, 74.12% Recall, and 78.16% Fscore values. In terms of different criteria metrics, the system produced competitive or even better results than the literature.