High-resolution Imaging Research Articles

Medical imaging for the diagnosis, recurrence and metastasis evaluation of clear cell sarcoma

Clear cell sarcoma (CCS) of soft tissue is extremely rare, accounting for approximately 1% of all soft tissue tumours. It is very difficult to diagnose CCS based on clinical manifestations. Magnetic resonance imaging (MRI) provides high-resolution images of soft tissues and pathological features such as mucus, necrosis, bleeding, and fat through high and low signals on T1 weighted image (T1WI) and T2 weighted image (T2WI). On the other hand, the paramagnetism of melanin in CCS shortens the relaxation time of T1 and T2, and high signal intensity on T1WI and low signal intensity on T2WI can be found. This is different from most other soft tissue sarcomas. At present, the treatment method for CCS is surgical resection. MRI can effectively display the tumour edge, extent of surrounding oedema, and extent of fat involvement, which is highly important for guiding surgical resection and predicting postoperative recurrence. As an invasive sarcoma, CCS has a high risk of metastasis. Regardless of the pathological condition of the resected tumour, MRI or computed tomography (CT) should be performed every 1-2 years to assess recurrence at the primary site and to screen for metastasis in the lungs, liver, and bones. If necessary, PET-CT can be performed to evaluate the overall condition of the patient.

Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer

Background/Objectives: Head and neck cancer (HNC), predominantly squamous cell carcinoma (SCC), presents a significant global health burden. Conventional diagnostic approaches often face challenges in terms of achieving early detection and accurate diagnosis. This review examines recent advancements in hyperspectral imaging (HSI), integrated with computer-aided diagnostic (CAD) techniques, to enhance HNC detection and diagnosis. Methods: A systematic review of seven rigorously selected studies was performed. We focused on CAD algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and linear discriminant analysis (LDA). These are applicable to the hyperspectral imaging of HNC tissues. Results: The meta-analysis findings indicate that LDA surpasses other algorithms, achieving an accuracy of 92%, sensitivity of 91%, and specificity of 93%. CNNs exhibit moderate performance, with an accuracy of 82%, sensitivity of 77%, and specificity of 86%. SVMs demonstrate the lowest performance, with an accuracy of 76% and sensitivity of 48%, but maintain a high specificity level at 89%. Additionally, in vivo studies demonstrate superior performance when compared to ex vivo studies, reporting higher accuracy (81%), sensitivity (83%), and specificity (79%). Conclusion: Despite these promising findings, challenges persist, such as HSI’s sensitivity to external conditions, the need for high-resolution and high-speed imaging, and the lack of comprehensive spectral databases. Future research should emphasize dimensionality reduction techniques, the integration of multiple machine learning models, and the development of extensive spectral libraries to enhance HSI’s clinical utility in HNC diagnostics. This review underscores the transformative potential of HSI and CAD techniques in revolutionizing HNC diagnostics, facilitating more accurate and earlier detection, and improving patient outcomes.

Highfield imaging of the subgenual anterior cingulate cortex in uni- and bipolar depression

BackgroundThe subgenual Anterior Cingulate Cortex (sgACC), as a part of the Anterior Cingulate Cortex and the limbic system plays a crucial role in mood regulation. Previous structural and functional brain imaging studies of the sgACC have revealed alterations of Gray Matter (GM) volumes and Blood Oxygenation Level Dependent signals (BOLD) in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD), suggesting potential biomarker traits for affective disorders.MethodIn this study we investigated the gray matter volume of the sgACC in 3 different patient groups: 40 MDD patients, of which 20 were medicated (MDDm) and 20 were unmedicated (MDDu), and 21 medicated BD patients, and compared them with 23 healthy volunteers. We examined GM volume alteration using high-resolution 7T Magnetic Resonance Imaging (MRI) which produced quantitative maps of the spin-lattice relaxation time (T1). T1 maps provide high contrast between gray and white matter, and at 7 Tesla voxels with submillimeter resolution can be acquired in a reasonable scan time. We developed a semi-automatic segmentation protocol based on refined landmarks derived from previous volumetric studies using quantitative T1 maps as raw input data for automatic tissue segmentation of GM, WM and CSF (cerebrospinal fluid) tissue. The sgACC ROI was then superimposed on these tissue probability maps and traced manually by two independent raters (F.B., M.M.) following our semi-automatic segmentation protocol. Interrater reliability was calculated on a subset of 10 brain scans for each hemisphere, showing an Intra-Class Correlation coefficient (ICC) r = 0.96 for left sgACC and r = 0.84 for right sgACC respectively. In summary, we have developed a reproducible and reliable semi-automatic segmentation protocol to measure gray matter volume in the sgACC. Based on previous findings from meta-analyses on morphometric studies of the sgACC, we hypothesized that patients with MDD would have lower gray matter sgACC volumes compared to healthy subjects.ResultsPost-hoc analysis revealed smaller subgenual volumes for the left hemisphere in both the medicated (MDDm) and non-medicated (MDDu) group versus healthy controls (p = .001, p = .008) respectively. For the right hemisphere, the (MDDu) and BD group exhibited significantly lower subgenual volumes than healthy controls (p < .001, p = .004) respectively.ConclusionTo our knowledge, this is the first morphometric MRI study using T1 maps obtained in high-resolution 7 Tesla MRI to compare MDD and BD patients with healthy controls.

The Acid-Stimulated Self-Assembled DNA Nanonetwork for Sensitive Detection and Living Cancer Cell Imaging of MicroRNA-221.

Herein, a novel functional DNA structure, acid-stimulated self-assembly DNA nanonetwork (ASDN), was proposed for miRNA-221 sensitive detection and high-resolution living cancer cell imaging. Significantly, the self-assembly of ASDN only occurred in the acidic extracellular environment of cancer cells, which could be endocytosed by cancer cells to eliminate the interference of noncancer cells and deliver the ASDN into cancer cells. Subsequently, endogenous miRNA-221 could trigger the catalytic hairpin assembly within ASDN, resulting in the separation of the fluorophore Cy5 and the quencher BHQ2 to recover the substantial Cy5 fluorescence signals, thus achieving signal amplification for sensitive detection of miRNA-221 with a detection limit of 5.5 pM, as well as facilitating high-resolution and low-background imaging of miRNA-221 in cancer cells. In consequence, this strategy provides an innovative DNA nanonetwork to distinguish cancer cells from other cells for sensitive detection of biomarkers, offering a meaningful reference for the application of DNA nanostructure self-assembly technology in relevant fundamental research and disease diagnosis.

Unsupervised Multi-Scale Hybrid Feature Extraction Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Generating pixel-level annotations for semantic segmentation tasks of high-resolution remote sensing images is both time-consuming and labor-intensive, which has led to increased interest in unsupervised methods. Therefore, in this paper, we propose an unsupervised multi-scale hybrid feature extraction network based on the CNN-Transformer architecture, referred to as MSHFE-Net. The MSHFE-Net consists of three main modules: a Multi-Scale Pixel-Guided CNN Encoder, a Multi-Scale Aggregation Transformer Encoder, and a Parallel Attention Fusion Module. The Multi-Scale Pixel-Guided CNN Encoder is designed for multi-scale, fine-grained feature extraction in unsupervised tasks, efficiently recovering local spatial information in images. Meanwhile, the Multi-Scale Aggregation Transformer Encoder introduces a multi-scale aggregation module, which further enhances the unsupervised acquisition of multi-scale contextual information, obtaining global features with stronger feature representation. The Parallel Attention Fusion Module employs an attention mechanism to fuse global and local features in both channel and spatial dimensions in parallel, enriching the semantic relations extracted during unsupervised training and improving the performance of unsupervised semantic segmentation. K-means clustering is then performed on the fused features to achieve high-precision unsupervised semantic segmentation. Experiments with MSHFE-Net on the Potsdam and Vaihingen datasets demonstrate its effectiveness in significantly improving the accuracy of unsupervised semantic segmentation.

The application of GIS technology in building a multivariate taphonomic profile for improving PMI estimations in Greece.

Environmental conditions highly affect decomposition rates and therefore a forensic practitioner should consider context-specific information when estimating the post mortem interval (PMI). Traditional methods of collecting environmental data, however, are time-consuming and often impractical for large-scale studies or routine forensic investigations. This study developed an automated computer method by employing the technology of geographic information systems (GIS) and Python programming language to provide contextual information for bodies found outdoors in Greece. The generated coding script underwent testing on 95 bodies in various stages of decomposition, which were examined between the years 1999 and 2022 at the National and Kapodistrian University of Athens and the Forensic Medical Service of Thessaloniki. Using ArcGIS Pro software and publicly available online data, a multilayer map was developed. Individual layers included high-resolution aerial images and data on the European Nature Information System ecosystem type, the Köppen-Geiger climatic type, the population density, the elevation, and the slope. Additionally, 99 national weather stations and their corresponding meteorological data were integrated. By leveraging the geographical coordinates of the recovery site of each case and information about the decedent's disappearance and recovery dates, this script automatically generates details from each of the above layers. Additionally, it calculates the accumulated degree days (ADD) and accumulated humidity days (AHD) values by extracting data from the nearest weather station. The GIS-based approach enables rapid, objective, and reproducible taphonomic profile construction, which can greatly improve the reliability of PMI estimations. By utilizing this method, forensic practitioners can accurately evaluate environmental effects on decomposition, thus standardizing taphonomic profiling globally.

Innovative sample preparation using alcohol dehydration and high refractive index medium enables acquisition of two-channel super-resolution 3D STED image of an entire oocyte.

Super-resolution (SR) microscopy is a cutting-edge method that can provide detailed structural information with high resolution. However, the thickness of the specimen has been a major limitation for SR methods, and large biological structures have posed a challenge. To overcome this, the key step is to optimise sample preparation to ensure optical homogeneity and clarity, which can enhance the capabilities of SR methods for the acquisition of thicker structures. Oocytes are the largest cells in the mammalian body and are crucial objects in reproductive biology. They are especially useful for studying membrane proteins. However, oocytes are extremely fragile and sensitive to mechanical manipulation and osmotic shocks, making sample preparation a critical and challenging step. We present an innovative, simple and sensitive approach to oocyte sample preparation for 3D STED acquisition. This involves alcohol dehydration and mounting into a high refractive index medium. This extended preparation procedure allowed us to successfully obtain a unique two-channel 3D STED SR image of an entire mouse oocyte. By optimising sample preparation, it is possible to overcome current limitations of SR methods and obtain high-resolution images of large biological structures, such as oocytes, in order to study fundamental biological processes. Lay Abstract: Super-resolution (SR) microscopy is a cutting-edge tool that allows scientists to view incredibly fine details in biological samples. However, it struggles with larger, thicker specimens, as they need to be optically clear and uniform for the best imaging results. In this study, we refined the sample preparation process to make it more suitable for SR microscopy. Our method includes carefully dehydrating biological samples with alcohol and then transferring them into a mounting medium that enhances optical clarity. This improved protocol enables high-resolution imaging of thick biological structures, which was previously challenging. By optimizing this preparation method, we hope to expand the use of SR microscopy for studying large biological samples, helping scientists better understand complex biological structures.

Single-shot 20-fold expansion microscopy.

Expansion microscopy (ExM) is in increasingly widespread use throughout biology because its isotropic physical magnification enables nanoimaging on conventional microscopes. To date, ExM methods either expand specimens to a limited range (~4-10× linearly) or achieve larger expansion factors through iterating the expansion process a second time (~15-20× linearly). Here, we present an ExM protocol that achieves ~20× expansion (yielding <20-nm resolution on a conventional microscope) in a single expansion step, achieving the performance of iterative expansion with the simplicity of a single-shot protocol. This protocol, which we call 20ExM, supports postexpansion staining for brain tissue, which can facilitate biomolecular labeling. 20ExM may find utility in many areas of biological investigation requiring high-resolution imaging.

Intra-Aneurysmal High-Resolution 4D MR 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 4D MR 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 MR flow imaging markers for aneurysm instability by relating hemodynamics in the aneurysm sac to 3D morphologic proxy parameters for aneurysm instability. In 35 patients with 37 unruptured IAs, a 3T MRA and a 7T 4D MRI flow scan were performed. Five hemodynamic parameters-peak-systolic wall shear stress (WSSMAX) and time-averaged wall shear stress (WSSMEAN), oscillatory shear index (OSI), mean velocity, and velocity pulsatility index-were correlated to 6 3D morphology proxy parameters of aneurysm instability-major axis length, volume, surface area (all 3 size parameters), flatness, shape index, and curvedness-by Pearson correlation with 95% CI. Scatterplots of hemodynamic parameters that correlated with IA size (major axis length) were created. WSSMAX and WSSMEAN correlated negatively with all 3 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 0.76-0.93]). WSSMAX and WSSMEAN correlated positively with shape index (r = 0.61, 95% CI 0.36-0.78 and r = 0.49, 95% CI 0.20-0.70, 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 0.26-0.74). Velocity pulsatility index did not show any statistically relevant correlation. Out of the 5 included hemodynamic parameters, WSSMAX, WSSMEAN, and OSI showed the strongest correlation with morphologic 3D proxy parameters of aneurysm instability. Future studies should assess these promising new imaging marker parameters for predicting aneurysm instability in longitudinal cohorts of patients with IA.

Multi-image X-ray interferometer module: II. Demonstration of high-resolution X-ray imaging with regular-interval coded apertures

Multi-image X-ray interferometer module: II. Demonstration of high-resolution X-ray imaging with regular-interval coded apertures

An exploratory study of the impact of CT slice thickness and inter-rater variability on anatomical accuracy of malunited distal radius models and surgical guides for corrective osteotomy.

High-resolution CT images are essential in clinical practice to accurately replicate patient anatomy for 3D virtual surgical planning and designing patient-specific surgical guides. These technologies are commonly used in corrective osteotomy of the distal radius. This study evaluated how the virtual radius models and the surgical guides' surface that is in contact with the bone vary between experienced raters. Further, the discrepancies from the reference radius of surgical guides and radius models created from CT images with slice thicknesses larger than the reference standard of 0.625mm were assessed. Maximum overlap with radius model was measured for guides, and absolute average distance error was measured for radius models. The agreement between the lower-resolution guides surface and the raters' guide surface was evaluated. The average inter-rater guide surface overlap was -0.11mm [95% CI: -0.13-0.09]. The surface of surgical guides designed on CT images with a 1mm slice thickness deviated from the reference radius within the inter-rater range (0.03mm). For slice thicknesses of 1.25mm and 1.5mm, the average guide surface overlap was 0.12mm and 0.15mm, respectively. The average inter-rater radius surface variability was 0.03mm [95% CI: 0.025-0.035]. The discrepancy from the reference of all radius models created from CT images with a slice thickness larger than the reference slice thickness was notably larger than the inter-rater variability but, excluding one case, did not exceed 0.2mm. The results suggest that 1mm CT images are suitable for surgical guide design. While 1.25mm slices are commonly used for virtual planning in hand and forearm surgery, slices larger than 1mm may approach the limit of clinical acceptability. Discrepancies in radius models were below 1mm, likely below clinical relevance.

Mapping stem volume in fast-growing eucalypt plantations: integrating spectral, textural, and temporal remote sensing information with forest inventories and spatial models

Key messageAccurate volume estimation in Eucalyptus plantation stands was achieved by a linear model using SPOT and Landsat multispectral imagery, specifically texture indices and pixel-scale NDVI time integrals, which reflect the local plantation growth history. Spatial modelling techniques such as Kriging with External Drift and Generalized Additive Model slightly improved predictions by accounting for spatial correlation of volume between sample points.ContextForest inventories are widely used to estimate stand production. To capture the inherent spatial variability within stands, spatial modelling techniques such as Kriging with External Drift (KED) and the generalized additive model (GAM) have emerged. These models incorporate information on spatial correlation and auxiliary variables that can be obtained from satellite imagery.AimsOur study explored the use of reflectance data from SPOT and Landsat multispectral imagery. We focused on texture indices and temporal integration of vegetation indices as auxiliary variables in KED and GAM to predict stem volume of fast-growing Eucalyptus sp. plantations in Brazil.MethodsThe components extracted from the high-resolution SPOT-6 image included spectral band values, band ratio metrics, key vegetation indices (NDVI, SAVI, and ARVI), texture measurements, and indices derived from texture analysis. Additionally, we included the accumulated NDVI time series acquired from Landsat 5, 7, and 8 satellites between the planting date and the forest inventory measurement date.ResultsThe best linear model of stem volume using remotely sensed predictors gave an R-squared value of 0.95 and a Root Mean Square Error (RMSE) of 12.44 m3/ha. The R-squared increased to 0.96 and the RMSE decreased to 10.6 m3/ha when the same predictors were included as auxiliary variables in the KED and GAM spatial models.ConclusionThe linear model using remotely sensed predictors contributed most to volume prediction, but the addition of spatial coordinates in the KED and GAM spatial models improved local volume predictions.

Quantification of porcine lower thoracic spinal cord morphology with intact dura mater using high-resolution μCT.

Spinal cord stimulation (SCS) is approved by the Food and Drug Administration for treating chronic intractable pain in the back, trunk, or limbs through stimulation of the dorsal column. Numerous studies have used swine as an analog of the human spinal cord to better understand SCS and further improve its efficacy. We performed high-resolution imaging of the porcine spinal cord with intact dura mater using micro-computed tomography (μCT) to construct detailed 3-dimensional (3D) visualizations of the spinal cord and characterize the morphology of the dorsal and ventral rootlets. We obtained spinal cords from Yorkshire/Landrace crossbred swine (N=7), stained samples with osmium tetroxide, and performed μCT imaging of the T12-T15 levels at isotropic voxel resolutions ranging from 3.3 to 50μm. We measured the anatomical morphology using the 3D volumes and compared our results to measurements previously collected from swine and human spinal cords via microdissection techniques in prior literature. While the porcine thoracic-lumbar spinal cord is a popular model for SCS, we highlight multiple notable differences compared to previously published T8-T12 human measurements including rootlet counts (porcine dorsal/ventral: 12.2±2.6, 26.6±3.4; human dorsal/ventral: 5.3±1.3, 4.4±2.4), rootlet angles (porcine ventral-rostral: 161±1°, ventral-caudal: 155±6°, dorsal-rostral: 148±9°, dorsal-caudal: 142±6°; human ventral-rostral: 170±3°, ventral-caudal: 22±10°, dorsal-rostral: 171±3°, dorsal-caudal: 15±7°), and the presence and count of dorsal rootlet bundles. Detailed measurements and highlighted differences between human and porcine spinal cords can inform variations in modeling and electrophysiological experiments between the two species. In contrast to other approaches for measuring the spinal cord and rootlet morphology, our method keeps the dura intact, reducing potential artifacts from dissection.

Subpixel three-dimensional imaging using two-dimensional optical encoding with a downscaled avalanche photodiode array

Scannerless laser three-dimensional (3D) imaging relies on large-scale detector arrays to achieve high-resolution imaging and is one of the main technologies in light detection and ranging (LIDAR). However, the high production cost and complex manufacturing process is limiting the imaging resolution. In this paper, we demonstrate a subpixel 3D imaging method based on two-dimensional (2D) encoding and decoding. First, an LED array projects 2D spatio-temporal coded light beams onto the target space. Accordingly, an optical multiplexer separates and reconstructs the backscattered signals into a small-scale avalanche photodiode (APD) array. We designed a prototype with a 16×16 optical encoding and a 2×2 APD array. The scannerless imaging reconstructs 256 pixels per frame within the distance range of 39–68 cm.

Extended-depth of field random illumination microscopy, EDF-RIM, provides super-resolved projective imaging

The ultimate aim of fluorescence microscopy is to achieve high-resolution imaging of increasingly larger biological samples. Extended depth of field presents a potential solution to accelerate imaging of large samples when compression of information along the optical axis is not detrimental to the interpretation of images. We have implemented an extended depth of field (EDF) approach in a random illumination microscope (RIM). RIM uses multiple speckled illuminations and variance data processing to double the resolution. It is particularly adapted to the imaging of thick samples as it does not require the knowledge of illumination patterns. We demonstrate highly-resolved projective images of biological tissues and cells. Compared to a sequential scan of the imaged volume with conventional 2D-RIM, EDF-RIM allows an order of magnitude improvement in speed and light dose reduction, with comparable resolution. As the axial information is lost in an EDF modality, we propose a method to retrieve the sample topography for samples that are organized in cell sheets.

Adaptive optics scanning laser ophthalmoscopy in a heterogenous cohort with Stargardt disease

Image based cell-specific biomarkers will play an important role in monitoring treatment outcomes of novel therapies in patients with Stargardt (STGD1) disease and may provide information on the exact mechanism of retinal degeneration. This study reports retinal image features from conventional clinical imaging and from corresponding high-resolution imaging with a confocal adaptive optics scanning laser ophthalmoscope (AOSLO) in a heterogenous cohort of patients with Stargardt (STGD1) disease. This is a prospective observational study in which 16 participants with clinically and molecularly confirmed STGD1, and 7 healthy controls underwent clinical assessment and confocal AOSLO imaging. Clinical assessment included short-wavelength and near-infrared fundus autofluorescence, spectral-domain optical coherence tomography, and macular microperimetry. AOSLO images were acquired over a range of retinal eccentricities (0°–20°) and mapped to areas of interest from the clinical images. A regular photoreceptor mosaic was identified in areas of normal or near normal retinal structure on clinical images. Where clinical imaging indicated areas of retinal degeneration, the photoreceptor mosaic was disorganised and lacked unambiguous cones. Discrete hyper-reflective foci were identified in 9 participants with STGD1 within areas of retinal degeneration. A continuous RPE cell mosaic at the fovea was identified in one participant with an optical gap phenotype. The clinical heterogeneity observed in STGD1 is reflected in the findings on confocal AOSLO imaging.

Advancements in sarcopenia diagnosis: from imaging techniques to non-radiation assessments

Sarcopenia is a prevalent condition with significant clinical implications, and it is expected to escalate globally, demanding for effective diagnostic strategies, possibly at an early stage of the disease. Imaging techniques play a pivotal role in comprehensively evaluating sarcopenia, offering insights into both muscle quantity and quality. Among all the imaging techniques currently used for the diagnosis and follow up of sarcopenia, it is possible to distinguish two classes: Rx based techniques, using ionizing radiations, and non-invasive techniques, which are based on the use of safe and low risk diagnostic procedures. Dual-energy x-ray Absorptiometry and Computed Tomography, while widely utilized, entail radiation exposure concerns. Ultrasound imaging offers portability, real-time imaging, and absence of ionizing radiation, making it a promising tool Magnetic Resonance Imaging, particularly T1-weighted and Dixon sequences, provides cross- sectional and high-resolution images and fat-water separation capabilities, facilitating precise sarcopenia quantification. Bioelectrical Impedance Analysis (BIA), a non-invasive technique, estimates body composition, including muscle mass, albeit influenced by hydration status. Standardized protocols, such as those proposed by the Sarcopenia through Ultrasound (SARCUS) Working Group, are imperative for ensuring consistency across assessments. Future research should focus on refining these techniques and harnessing the potential of radiomics and artificial intelligence to enhance diagnostic accuracy and prognostic capabilities in sarcopenia.

High-resolution Imaging and Morphological Phenotyping of C. elegans through Stable Robotic Sample Rotation and AI-based 3D Reconstruction

High-resolution Imaging and Morphological Phenotyping of C. elegans through Stable Robotic Sample Rotation and AI-based 3D Reconstruction

Nonlinear geometric phase coded ferroelectric nematic fluids for nonlinear soft-matter photonics

Simultaneous manipulation of multiple degrees of freedom of light lies at the heart of photonics. Nonlinear wavefront shaping offers an exceptional way to achieve this goal by converting incident light into beams of new frequencies with spatially varied phase, amplitude, and angular momenta. Nevertheless, the reconfigurable control over structured light fields for advanced multimode nonlinear photonics remains a grand challenge. Here, we propose the concept of nonlinear geometric phase in an emerging ferroelectric nematic fluid, of which the second-order nonlinear susceptibility carries spin-dependent nonlinearity phase. A case study with photopatterned q-plates demonstrates the generation of second-harmonic optical vortices with spin-locked topological charges by using cascaded linear and nonlinear optical spin-orbit interactions. Furthermore, we present the dynamic tunability of second-harmonic structured light through temperature, electric field, and twisted elastic force. The proposed strategy opens new avenues for reconfigurable nonlinear photonics, with potential applications in optical communications, quantum computing, high-resolution imaging, etc.

Fast On-Site Speciation and High Spatial Resolution Imaging of Labile Arsenic in Freshwater and Sediment Using the DGT-SERS Sensor.

Diffusive gradients in thin films (DGT) technique is renowned for in situ passive sampling but not for rapid on-site analysis, whereas surface-enhanced Raman spectroscopy (SERS) excels in ultrasensitive on-site detection but is limited by substrate contamination from complex matrices. Here, a hierarchical nanostructure of silver (Ag) mirror-supported large Ag nanoparticles (∼120 nm) was grown in situ in polyacrylamide hydrogel with a restricted pore size (PAM/Ag mirror/AgNPs) to serve as both the DGT binding phase and the SERS substrate. The substrate exhibited a maximum electric field enhancement factor of 9.9 × 108 and a signal relative standard error of 4.8%. Using the DGT-SERS sensor, As(III) and As(V) in freshwater were simultaneously detected at limits of 0.9 and 0.8 μg L-1, respectively, applicable across a wide range of environmental conditions. The DGT-SERS effectively mitigated the interfacial reduction of As(V) caused by humic acid by excluding it from plasmonic hotspots through size exclusion of the diffusive layer. The Raman analysis of a DGT sample in the field requires only 2 s using a portable spectrometer without DGT device disassembly. More importantly, the DGT-SERS captured the first two-dimensional image of As(III) and As(V) in one DGT at the micron scale resolution, revealing their spatially supplementary distribution patterns at the sediment-water interface. This study paves the way for next-generation speciation imaging DGT and the application of SERS in complex environments.