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Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson's disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a well-established validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylated-aSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001-0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status.

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Vision Transformer have achieved impressive performance in image super-resolution. However, they suffer from low inference speed mainly because of the quadratic complexity of multi-head self-attention (MHSA), which is the key to learning long-range dependencies. On the contrary, most CNN-based methods neglect the important effect of global contextual information, resulting in inaccurate and blurring details. If one can make the best of both Transformers and CNNs, it will achieve a better trade-off between image quality and inference speed. Based on this observation, firstly assume that the main factor affecting the performance in the Transformer-based SR models is the general architecture design, not the specific MHSA component. To verify this, some ablation studies are made by replacing MHSA with large kernel convolutions, alongside other essential module replacements. Surprisingly, the derived models achieve competitive performance. Therefore, a general architecture design GlobalSR is extracted by not specifying the core modules including blocks and domain embeddings of Transformer-based SR models. It also contains three practical guidelines for designing a lightweight SR network utilizing image-level global contextual information to reconstruct SR images. Following the guidelines, the blocks and domain embeddings of GlobalSR are instantiated via Deformable Convolution Attention Block (DCAB) and Fast Fourier Convolution Domain Embedding (FCDE), respectively. The instantiation of general architecture, termed GlobalSR-DF, proposes a DCA to extract the global contextual feature by utilizing Deformable Convolution and a Hadamard product as the attention map at the block level. Meanwhile, the FCDE utilizes the Fast Fourier to transform the input spatial feature into frequency space and then extract image-level global information from it by convolutions. Extensive experiments demonstrate that GlobalSR is the key part in achieving a superior trade-off between SR quality and efficiency. Specifically, our proposed GlobalSR-DF outperforms state-of-the-art CNN-based and ViT-based SISR models regarding accuracy-speed trade-offs with sharp and natural details.

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BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (FastCSE) to accelerate CSE. METHODS: FastCSE was built on a super-resolution generative adversarial network extended to enhance complex-valued image sharpness. FastCSE enhances each echo image independently before water-fat separation. FastCSE was trained with retrospectively identified cines from 1519 patients (56 ± 16 years; 866 men) referred for clinical 3T CMR. In a prospective study of 16 participants (58 ± 19 years; 7 females) and 5 healthy individuals (32 ± 17 years; 5 females), dual-echo CSE images were collected with 1.5 × 1.5mm2, 2.5 × 1.5 mm2, and 3.8 × 1.9mm2 resolution using generalized autocalibrating partially parallel acquisition (GRAPPA). FastCSE was applied to images collected with resolution of 2.5 × 1.5mm2 and 3.8 × 1.9 mm2 to restore sharpness. Fat images obtained from two-point Dixon reconstruction were evaluated using a quantitative blur metric and analyzed with 5-way analysis of variance. RESULTS: FastCSE successfully reconstructed CSE images inline. FastCSE acquisition, with a resolution of 2.5 × 1.5mm² and 3.8 × 1.9 mm², reduced the number of breath-holds without impacting visualization of fat by approximately 1.5-fold and 3-fold compared to GRAPPA acquisition with a resolution of 1.5 × 1.5 mm², from 3.0 ± 0.8 breath-holds to 2.0 ± 0.2 and 1.1 ± 0.4 breath-holds, respectively. FastCSE improved image sharpness and removed ringing artifacts in GRAPPA fat images acquired with a resolution of 2.5 × 1.5 mm2 (0.31 ± 0.03 vs. 0.35 ± 0.04, P < 0.001) and 3.8 × 1.9 mm2 (0.31 ± 0.03 vs. 0.42 ± 0.06, P < 0.001). Blurring in FastCSE images was similar to blurring in images with 1.5 × 1.5 mm² resolution (0.32 ±0.03 vs. 0.31 ± 0.03, P = 0.78; 0.32 ± 0.03 vs. 0.31 ± 0.03, P = 0.90). CONCLUSION: We showed that a deep learning-accelerated CSE technique based on complex-valued resolution enhancement can reduce the number of breath-holds in CSE imaging without impacting the visualization of fat. FastCSE showed similar image sharpness compared to a standardized parallel imaging method.

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Volumetric subcellular imaging has long been essential for studying structures and dynamics in cells and tissues. However, due to limited imaging speed and depth of field, it has been challenging to perform live-cell imaging and single-particle tracking. Here we report a 2.5D fluorescence microscopy combined with highly inclined illumination beams, which significantly reduce not only the image acquisition time but also the out-of-focus background by ∼2-fold compared to epi-illumination. Instead of sequential z-scanning, our method projects a certain depth of volumetric information onto a 2D plane in a single shot using multi-layered glass for incoherent wavefront splitting, enabling high photon detection efficiency. We apply our method to multi-color immunofluorescence imaging and volumetric super-resolution imaging, covering ∼3-4 µm thickness of samples without z-scanning. Additionally, we demonstrate that our approach can substantially extend the observation time of single-particle tracking in living cells.

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PURPOSE: Low-field (LF) MRI scanners are common in many Low- and middle-Income countries, but they provide images with worse spatial resolution and contrast than high-field (HF) scanners. Image Quality Transfer (IQT) is a machine learning framework to enhance images based on high-quality references that has recently adapted to LF MRI. In this study we aim to assess if it can improve lesion visualisation compared to LF MRI scans in children with epilepsy. METHODS: T1-weighted, T2-weighted and FLAIR were acquired from 12 patients (5 to 18 years old, 7 males) with clinical diagnosis of intractable epilepsy on a 0.36T (LF) and a 1.5T scanner (HF). LF images were enhanced with IQT. Seven radiologists blindly evaluated the differentiation between normal grey matter (GM) and white matter (WM) and the extension and definition of epileptogenic lesions in LF, HF and IQT-enhanced images. RESULTS: When images were evaluated independently, GM-WM differentiation scores of IQT outputs were 26% higher, 17% higher and 12% lower than LF for T1, T2 and FLAIR. Lesion definition scores were 8-34% lower than LF, but became 3% higher than LF for FLAIR and T1 when images were seen side by side. Radiologists with expertise at HF scored IQT images higher than those with expertise at LF. CONCLUSION: IQT generally improved the image quality assessments. Evaluation of pathology on IQT-enhanced images was affected by familiarity with HF/IQT image appearance. These preliminary results show that IQT could have an important impact on neuroradiology practice where HF MRI is not available.

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Gait recognition based on gait silhouette profiles is currently a major approach in the field of gait recognition. In previous studies, models typically used gait silhouette images sized at 64 × 64 pixels as input data. However, in practical applications, cases may arise where silhouette images are smaller than 64 × 64, leading to a loss in detail information and significantly affecting model accuracy. To address these challenges, we propose a gait recognition system named Multi-scale Feature Cross-Fusion Gait (MFCF-Gait). At the input stage of the model, we employ super-resolution algorithms to preprocess the data. During this process, we observed that different super-resolution algorithms applied to larger silhouette images also affect training outcomes. Improved super-resolution algorithms contribute to enhancing model performance. In terms of model architecture, we introduce a multi-scale feature cross-fusion network model. By integrating low-level feature information from higher-resolution images with high-level feature information from lower-resolution images, the model emphasizes smaller-scale details, thereby improving recognition accuracy for smaller silhouette images. The experimental results on the CASIA-B dataset demonstrate significant improvements. On 64 × 64 silhouette images, the accuracies for NM, BG, and CL states reached 96.49%, 91.42%, and 78.24%, respectively. On 32 × 32 silhouette images, the accuracies were 94.23%, 87.68%, and 71.57%, respectively, showing notable enhancements.

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The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real world, due to the limited complementary information, the performance of both single-frame and multi-frame super-resolution reconstruction degrades rapidly as the magnification increases. In this paper, we propose a novel two-step image super resolution method concatenating multi-frame super-resolution (MFSR) with single-frame super-resolution (SFSR), to progressively upsample images to the desired resolution. The proposed method consisting of an L0-norm constrained reconstruction scheme and an enhanced residual back-projection network, integrating the flexibility of the variational model-based method and the feature learning capacity of the deep learning-based method. To verify the effectiveness of the proposed algorithm, extensive experiments with both simulated and real world sequences were implemented. The experimental results show that the proposed method yields superior performance in both objective and perceptual quality measurements. The average PSNRs of the cascade model in set5 and set14 are 33.413 dB and 29.658 dB respectively, which are 0.76 dB and 0.621 dB more than the baseline method. In addition, the experiment indicates that this cascade model can be robustly applied to different SFSR and MFSR methods.

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The use of particle localisation and tracking algorithms on Contrast Enhanced Ultrasound (CEUS) or other ultrasound mode image data containing sparse microbubble (MB) populations, can produce super-resolved vascularization maps. Typically such data stem from conventional delay and sum (DAS) beamforming that is used widely in ultrasound imaging modes. Recently, adaptive beamforming has shown significant improvement in spatial resolution, but its value to super-resolution image analysis approaches is not fully understood. The in silico study here evaluates the performance of combining minimum variance beamformers (MV BF), established to provide improved lateral resolution, compared to DAS BFs with single particle detection. The isolated effect of a range of simplified image-affecting factors such as flow profile, pulse length, noise, vessel separations and data availability is considered. The study aims to assess the vessel recovery performance using the different beamformers and investigate the link with MB detection and localisation. The MV BF was shown to provide improved microvessel position accuracy compared to conventional DAS BFs. In particular, vessel separations between 0.3-4 λ provided superior localisation uncertainty with the MV. In addition, for a separation of 0.36λ, vessel recovery was achieved with both methods but the use of MV eliminated artifacts that appear as additional vessels. These results were found to be linked to improved MB detection and localisation for the MV BF, which is proposed as suitable for testing in Ultrasound Localisation Microscopy (ULM) imaging using patient data.

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Ultrasound imaging using an active sensing array has been extensively studied in both time domain and frequency domain. Subspace decomposition methods in match field beamforming such as the multiple signal classification (MUSIC) algorithm can achieve subwavelength resolution of distinct point scatterers. However, when the size of the target is on the order of one wavelength or larger, the MUSIC type algorithms suffer from poor performance due to a tangled eigen structure. This paper proposes an adaptive match field beamformer that does not require subspace decomposition to achieve high resolution imaging of extended targets. Specifically, the broadband coherent white noise constraint (C-WNC) algorithm is utilized to achieve high focusing ability of extended targets by exploiting the cross-frequency coherence in an active sensing scheme. The dynamic range bias in the adaptive beamformer benefits the C-WNC algorithm to achieve high contrast regardless of the SNR. Both simulations and experiments show that the C-WNC images retain their resolution cells on the tips of the extended target with sizes ranging from a wavelength to sizes as large as the physical aperture width. A robust imaging scheme is then proposed to obtain high quality images by combining C-WNC images with a statistically stable delay-multiply-and-sum (DMAS) algorithm to create high-contrast and high-resolution images of extended targets in both azimuth and axial range directions.

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Expansion Microscopy (ExM) is a widely used super-resolution technique that enables imaging of structures beyond the diffraction limit of light. However, ExM suffers from weak labeling signals and expansion distortions, limiting its applicability. Here, we present an innovative approach called Tetrahedral DNA nanostructure Expansion Microscopy (TDN-ExM), addressing these limitations by using tetrahedral DNA nanostructures (TDNs) for fluorescence labeling. Our approach demonstrates a 3- to 10-fold signal amplification due to the multivertex nature of TDNs, allowing the modification of multiple dyes. Previous studies have confirmed minimal distortion on a large scale, and our strategy can reduce the distortion at the ultrastructural level in samples because it does not rely on anchoring agents and is not affected by digestion. This results in a brighter fluorescence, better uniformity, and compatibility with different labeling strategies and optical super-resolution technologies. We validated the utility of TDN-ExM by imaging various biological structures with improved resolutions and signal-to-noise ratios.

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Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications.

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Super-resolution single-molecule localization microscopy (SMLM) of presynaptic active zones (AZs) and postsynaptic densities contributed to the observation of protein nanoclusters that are involved in defining functional characteristics and in plasticity of synaptic connections. Among SMLM techniques, direct stochastic optical reconstruction microscopy (dSTORM) depends on organic fluorophores that exert high brightness and reliable photoswitching. While multicolor imaging is highly desirable, the requirements necessary for high-quality dSTORM make it challenging to identify combinations of equally performing, spectrally separated dyes. Red-excited carbocyanine dyes, e.g., Alexa Fluor 647 (AF647) or Cy5, are currently regarded as "gold standard" fluorophores for dSTORM imaging. However, a recent study introduced a set of chemically modified rhodamine dyes, including CF583R, that promise to display similar performance in dSTORM. In this study, we defined CF583R's performance compared to AF647 and CF568 based on a nanoscopic analysis of Bruchpilot (Brp), a nanotopologically well-characterized scaffold protein at Drosophila melanogaster AZs. We demonstrate equal suitability of AF647, CF568 and CF583R for basal AZ morphometry, while in Brp subcluster analysis CF583R outperforms CF568 and is on par with AF647. Thus, the AF647/CF583R combination will be useful in future dSTORM-based analyses of AZs and other subcellularly located marker molecules and their role in physiological and pathophysiological contexts.

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SMN, linked to spinal muscular atrophy, is a key component of the Gemin complex essential for snRNP assembly. Following initial snRNP assembly in the cytoplasm, both snRNPs and SMN migrate to the nucleus and associate with Cajal bodies, where final snRNP maturation occurs. It is assumed that SMN must be free from the Cajal bodies for continuous snRNP biogenesis. Previous observation of the SMN granules docked in CB suggests the existence of a separation mechanism. However, the precise processes that regulate the spatial separation of SMN-complexes from Cajal bodies remain unclear. Here we employed a super-resolution microscope alongside the beta-carboline alkaloid harmine, which disrupted the Cajal body in a reversible manner. Upon removal of harmine, SMN and Coilin first appear as small, interconnected condensates. The SMN condensates mature into spheroidal structures encircled by Coilin, eventually segregating into distinct condensates. Expression of a multimerization-deficient SMN mutant leads to enlarged, atypical Cajal bodies where SMN is unable to segregate into separate condensates. These findings underscore the importance of multimerization in facilitating the segregation of SMN from Coilin within Cajal bodies.

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Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) is a powerful experimental technique for label-free sensing, imaging, and chemical analysis. Although Raman spectroscopy itself is an extremely "feeble" phenomenon, the intense interaction of optical fields with metallic nanostructures in the form of plasmonic hotspots can generate Raman signals from single molecules. While what constitutes a true single-molecule signal has taken some years for the scientific community to establish, many SERS experiments, even those not specifically attempting single-molecule sensitivity, have observed fluctuation in both the SERS intensity and spectral features. In this Perspective, we discuss the impact that fluctuating SERS signals have had on the continuing advancement of SM-SERS, along with challenges and current and potential future applications.

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Photochromic diarylethene has attracted broad research interest in optical applications owing to its excellent fatigue resistance and unique bistability. Photoswitchable fluorescent diarylethene become a powerful molecular tool for fluorescence imaging recently. Herein, the recent progress on photoswitchable fluorescent diarylethenes in bioimaging is reviewed. We summarize summarized the structures and properties of diarylethene fluorescence probes, and emphatically introduces their applications in bioimaging as well as super-resolution imaging. Additionally, we highlight the current challenges in practical applications and provides the prospects of the future development directions of photoswitchable fluorescent diarylethene in the field of bioimaging.  This comprehensive review aims to stimulate further research into higher performance photoswitchable fluorescent molecules and advance their progress in biological application.

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Craniomaxillofacial (CMF) and nasal landmark detection are fundamental components in computer-assisted surgery. Medical landmark detection method includes regression-based and heatmap-based methods, and heatmap-based methods are among the main methodology branches. The method relies on high-resolution (HR) features containing more location information to reduce the network error caused by sub-pixel location. Previous studies extracted HR patches around each landmark from downsampling images via object detection and subsequently input them into the network to obtain HR features. Complex multistage tasks affect accuracy. The network error caused by downsampling and upsampling operations during training, which interpolates low-resolution features to generate HR features or predicted heatmap, is still significant. We propose standard super-resolution landmark detection networks (SRLD-Net) and super-resolution UNet (SR-UNet) to reduce network error effectively. SRLD-Net used Pyramid pooling block, Pyramid fusion block and super-resolution fusion block to combine global prior knowledge and multi-scale local features, similarly, SR-UNet adopts Pyramid pooling block and super-resolution block. They can obviously improve representation learning ability of our proposed methods. Then the super-resolution upsampling layer is utilized to generate detail predicted heatmap. Our proposed networks were compared to state-of-the-art methods using the craniomaxillofacial, nasal, and mandibular molar datasets, demonstrating better performance. The mean errors of 18 CMF, 6 nasal and 14 mandibular landmarks are 1.39 ± 1.04, 1.31 ± 1.09, 2.01 ± 4.33 mm. These results indicate that the super-resolution methods have great potential in medical landmark detection tasks. This paper provides two effective heatmap-based landmark detection networks and the code is released in https://github.com/Runshi-Zhang/SRLD-Net.

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BACKGROUND: Bacterial extracellular vesicles (EVs) are pivotal mediators of intercellular communication and influence host cell biology, thereby contributing to the pathogenesis of infections. Despite their significance, the precise effects of bacterial EVs on the host cells remain poorly understood. This study aimed to elucidate ultrastructural changes in host cells upon infection with EVs derived from a pathogenic bacterium, Staphylococcus aureus (S. aureus). RESULTS: Using super-resolution fluorescence microscopy and high-voltage electron microscopy, we investigated the nanoscale alterations in mitochondria, endoplasmic reticulum (ER), Golgi apparatus, lysosomes, and microtubules of skin cells infected with bacterial EVs. Our results revealed significant mitochondrial fission, loss of cristae, transformation of the ER from tubular to sheet-like structures, and fragmentation of the Golgi apparatus in cells infected with S. aureus EVs, in contrast to the negligible effects observed following S. epidermidis EV infection, probably due to the pathogenic factors in S. aureus EV, including protein A and enterotoxin. These findings indicate that bacterial EVs, particularly those from pathogenic strains, induce profound ultrastructural changes of host cells that can disrupt cellular homeostasis and contribute to infection pathogenesis. CONCLUSIONS: This study advances the understanding of bacterial EV-host cell interactions and contributes to the development of new diagnostic and therapeutic strategies for bacterial infections.

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Background: The purpose of this study was to examine the feasibility and practical application of ultrasound (US) super-resolution imaging (SRI) in evaluating microvasculature and measuring renal allograft function. Methods: Sixteen consecutive patients who received kidney transplants were prospectively enrolled. The patients were assigned as: normal allograft function (n = 6), and allograft malfunction (n = 10). Localizing each potential contrast signal resulted in super-resolution images (SRI). SRI was utilized to assess micro-vessel density (MVD) and microvascular flow rate, whereas contrast-enhanced (CE) US images were statistically processed to get the time to peak (TTP) and peak intensity. Logistic regression was utilized to evaluate their relationship. Results: US SRI may be utilized effectively on allografts to show microvasculature with significantly higher resolution than typical color Doppler flow and CEUS pictures. In the multivariate analysis, MVD and TTP were significant US markers of renal allograft failure (p = 0.031 and p = 0.045). The combination of MVD and TTP produced an AUC of 0.783 (p < 0.05) for allograft dysfunction. Conclusions: SRI can accurately portray the microvasculature of renal allografts, while MVD and TTP are appropriate US markers for assessing renal allograft failure.

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With the development of educational technology, machine learning and deep learning provide technical support for traditional classroom observation assessment. However, in real classroom scenarios, the technique faces challenges such as lack of clarity of raw images, complexity of datasets, multi-target detection errors, and complexity of character interactions. Based on the above problems, a student classroom behavior recognition network incorporating super-resolution and target detection is proposed. To cope with the problem of unclear original images in the classroom scenario, SRGAN (Super Resolution Generative Adversarial Network for Images) is used to improve the image resolution and thus the recognition accuracy. To address the dataset complexity and multi-targeting problems, feature extraction is optimized, and multi-scale feature recognition is enhanced by introducing AKConv and LASK attention mechanisms into the Backbone module of the YOLOv8s algorithm. To improve the character interaction complexity problem, the CBAM attention mechanism is integrated to enhance the recognition of important feature channels and spatial regions. Experiments show that it can detect six behaviors of students-raising their hands, reading, writing, playing on their cell phones, looking down, and leaning on the table-in high-definition images. And the accuracy and robustness of this network is verified. Compared with small-object detection algorithms such as Faster R-CNN, YOLOv5, and YOLOv8s, this network demonstrates good detection performance on low-resolution small objects, complex datasets with numerous targets, occlusion, and overlapping students.

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Biological membranes are complex, heterogeneous, and dynamic systems that play roles in the compartmentalization and protection of cells from the environment. It is still a challenge to elucidate kinetics and real-time transport routes for molecules through biological membranes in live cells. Currently, by developing and employing super-resolution microscopy; increasing evidence indicates channels and transporter nano-organization and dynamics within membranes play an important role in these regulatory mechanisms. Here we review recent advances and discuss the major advantages and disadvantages of using super-resolution microscopy to investigate protein organization and transport within plasma membranes.