Imaging Methodology Research Articles

Controlled-source electromagnetic survey in a volcanic area: relationship between stacking time and signal-to-noise ratio and comparison with magnetotelluric data

Summary Although controlled-source electromagnetic (CSEM) methods have higher sensitivity to thin resistive bodies than the magnetotelluric (MT) method, their delineation by the inversion requires CSEM data with high signal-to-noise ratio (SNR). This study aims to enhance the SNR of CSEM data by increasing the number of stacks. To efficiently stack long-term data, we use an EM-Accurately Controlled, Routinely Operated Signal System (ACROSS), which can transmit accurately controlled waveforms by synchronizing the transmitting waveforms with a 10 MHz GPS signal. We conducted a CSEM survey using the EM-ACROSS in the Kusatsu-Shirane Volcano to demonstrate that the SNR can be improved by extensive observation data and the CSEM inversion can delineate hydrothermal systems, including resistive bodies of vapor-rich reservoirs. Our EM-ACROSS simultaneously transmitted waveforms from two dipoles during a 192-h of the survey; five-component receivers located 4–6 km away from the transmitter captured EM-ACROSS signals ranging between 146 h and 192 h. By stacking extensive observation data using a weighted method, the CSEM responses show minimal error levels, with standard errors <2% for most frequencies. The SNR roughly followed the square root of the stacking times. Three-dimensional inversion of the collected CSEM data delineated a relatively resistive body, interpreted as a vapor-dominated reservoir below a cap-rock layer, while the MT inversion failed to recover the same. This highlights the ability of an EM-ACROSS-based CSEM survey to delineate hydrothermal systems including vapor-dominated reservoirs, and provides a compelling rationale for establishing CSEM as a standard methodology in hydrothermal imaging. Furthermore, this study suggests that the enhanced imaging capabilities of CSEM data can be further improved when integrated with MT data.

Focusing on ligamentous soft tissue inflammation for the future understanding of early axial psoriatic arthritis

Abstract Imaging has transformed the understanding of inflammatory and degenerative arthritis in both peripheral and axial disease. In axial inflammation, fat suppression magnetic resonance imaging (MRI) has unravelled the role of sub-fibrocartilaginous osteitis in axial spondyloarthritis and the role of peri-entheseal vertebral body osteitis and subsequent spinal new bone formation. Established or late-stage axial psoriatic arthritis (PsA) cases often exhibit impressive para-marginal or chunky syndesmophytosis on conventional X-ray that pathologically represents entheseal soft tissue ossification. However, the spinal entheseal soft tissue and contiguous ligamentous tissues are poorly visualized on MRI in subjects with early inflammatory back pain including those with axial PsA. In this article, we highlight the need for imaging modalities to discern the crucial soft tissue “ligamentous” component of axial PsA towards diagnosis, prognosis and therapy validation. We issue a clarion call to focus advanced imaging methodology on spinal ligamentous soft tissue that represents the last hidden backwater of PsA immunopathology that needs visualization to fully decipher axial PsA pathogenesis. This in combination with the existing ability to visualize ligamentous bony anchorage site osteitis is needed to define a gold standard test for axial PsA.

Multi-crack damage identification and quantification using Lamb wave-based structural health monitoring

The simultaneous presence of multi-crack damage in a structural component is a crucial issue affecting system safety. To address the accurate and quantitative monitoring of multi-crack damage in an aeronautical structure, an innovative multi-crack damage localization, orientation and quantification algorithm using Lamb wave-based structural health monitoring is presented in this paper. An improved Hausdorff distance-based weighted average imaging methodology is introduced for precise multi-crack damage position identifications. To account for the critical role of crack orientation in damage quantification, a cross-orthogonality-based method is developed, enabling orientation detection at arbitrary positions and angles while simplifying the problem into a mathematical formulation. A wave scattering sources-based quantification algorithm incorporating the estimated position and orientation information is further proposed to estimate the multi-crack lengths. Additionally, a singular elliptic trajectories removal scheme is presented to suppress useless ambient noise and enhance the damage information discriminability. Experiments on the aircraft wing-box structure of a real airplane are implemented to substantiate the proposed techniques. Due to the outstanding properties, such as flexibility, electrically stabilized and applicability to complex structures, the sensor layer with built-in PZT sensor network surface-installed on the monitored structure is adopted to generate and receive Lamb wave signal. The results manifest that the proposed monitoring algorithm, without prior information or calibration required, is effective and straightforward for detecting multi-crack damage. This study can also provide a feasible technique for accurately locating and quantitatively identifying cracks in some concealed parts or inside the structure.

Fluorescence Lifetime Imaging for Quantification of Targeted Drug Delivery in Varying Tumor Microenvironments.

Trastuzumab (TZM) is a monoclonal antibody that targets the human epidermal growth factor receptor 2 (HER2) and is clinically used for the treatment of HER2-positive breast tumors. However, the tumor microenvironment can limit the access of TZM to the HER2 targets across the whole tumor and thereby compromisingTZM's therapeutic efficacy. An imaging methodology that can non-invasively quantify the binding of TZM-HER2, which is required for therapeutic action, and distribution within tumors with varying tumor microenvironments is much needed. Near-infrared (NIR) fluorescence lifetime (FLI) Forster Resonance Energy Transfer (FRET) is performed to measure TZM-HER2 binding, using in vitro microscopy and in vivo widefield macroscopy, in HER2 overexpressing breast and ovarian cancer cells and tumor xenografts, respectively. Immunohistochemistry is used to validate in vivo imaging results. NIR FLI FRET in vitro microscopy data show variations in intracellular distribution of bound TZM in HER2-positive breast AU565 and AU565 tumor-passaged XTM cell lines in comparison to SKOV-3 ovarian cancer cells. Macroscopy FLI (MFLI) FRET in vivo imaging data show that SKOV-3 tumors display reduced TZM binding compared to AU565 and XTM tumors, as validated by ex vivo immunohistochemistry. Moreover, AU565/XTM and SKOV-3 tumor xenografts display different amounts and distributions of TME components, such as collagen and vascularity. Therefore, these results suggest that SKOV-3 tumors are refractory to TZM delivery due to their disrupted vasculature and increased collagen content. The study demonstrates that FLI is a powerful analytical tool to monitor the delivery of antibodydrugs both in cell cultures and in vivo live systems. Especially, MFLI FRET is a unique imaging modality that can directly quantify target engagement with the potential to elucidate the role of the TME in drug delivery efficacy in intact live tumor xenografts.

Deep learning‐based inversion with discrete cosine transform discretization for two‐dimensional basement relief imaging of sedimentary basins from observed gravity anomalies

AbstractSedimentary basins, integral to Earth's geological history and energy resource exploration, undergo complex changes driven by sedimentation, subsidence and geological processes. Gravity anomaly inversion is a crucial technique offering insights into subsurface structures and density variations. Our study addresses the challenge of complex subsurface structure assessment by leveraging deep neural networks to invert observed gravity anomalies. Optimization approaches traditionally incorporate known density distributions obtained from borehole data or geological logging for inverting basement depth in sedimentary basins using observed gravity anomalies. Our study explores the application of deep neural networks in accurate architectural assessment of sedimentary basins and demonstrates their significance in mineral and hydrocarbon exploration. Recent years have witnessed a surge in the use of machine learning in geophysics, with deep learning models playing a pivotal role. Integrating deep neural networks, such as the feedforward neural networks, has revolutionized subsurface density distribution and basement depth estimation. This study introduces a deep neural network specifically tailored for inverting observed gravity anomalies to estimate two‐dimensional basement relief topographies in sedimentary basins. To enhance computational efficiency, a one‐dimensional discrete cosine transform based discretization approach is employed. Synthetic data, generated using non‐Gaussian fractals, compensates for the scarcity of true datasets for training the deep neural network model. The algorithm's robustness is validated through noise introduction with comparisons against an efficient and traditional global optimization‐based approach. Gravity anomalies of real sedimentary basins further validate the algorithm's efficacy, establishing it as a promising methodology for accurate and efficient subsurface imaging in geological exploration.

NIMG-69. INTERROGATION OF GLIOBLASTOMA FATTY-ACID SYNTHESIS AND METABOLISM WITH DEUTERIUM METABOLIC IMAGING

Abstract Glioblastoma, an invariably lethal brain tumor, undergoes marked metabolic alterations during tumorigenesis and is extraordinarily difficult to treat. Detecting recurrence after treatment and understanding metabolic alterations within the tumor microenvironment are extremely challenging. An exciting and emerging imaging methodology, deuterium metabolic imaging (DMI), detects metabolic adaptations via non-radioactively labeled deuterated substrates. This study developed approaches to image lipid synthesis with deuterium oxide (D2O) and metabolism with D31-palmitate (D31-Pal) in glioblastoma. A fatty-acid phantom was generated for characterization of lipid DMI signal at 12 T. DMI was performed in mice orthotopically implanted with GL261 glioblastoma utilizing either orally administered D31-Pal or D2O and was correlated with pharmacokinetic metabolic measurements from mouse serum. Mice were either orally gavaged with an emulsion of D31-Pal prior to imaging or provided 16% D2O for ad libitum consumption following tumor implantation. Although D2O administration resulted in robust enrichment of lipid signal in GBM, D31-Pal uptake and metabolism were not significantly detected within intracranial tumors. Interestingly, D31-Pal uptake and metabolism were detected in normal tissues, indicating preferential utilization of fatty acids in non-neoplastic tissues. These findings suggest de novo lipogenesis within GBM, rather than uptake of administered long chain fatty acids, is the preferential pathway for cellular lipids. DMI can assist in interrogating GBM fatty-acid synthesis and metabolism, as well as furthering our understanding of lipid distribution within the body by providing a non-invasive scaffold for dynamic interrogation of fatty-acid distribution in an oncologic model. Developing a non-invasive method for characterization of tumor fatty-acid synthesis and metabolism may improve our understanding of tumor metabolism and could dynamically guide selection of patient-tailored metabolic therapies in the future.

Recent Updates of PET in Lymphoma: FDG and Beyond.

Lymphoma is one of the most common cancers worldwide, categorized into Hodgkin lymphoma and non-Hodgkin lymphoma. 18F-fluorodeoxyglucose positron emission tomography (FDG PET) has become an essential imaging tool for evaluating patients with lymphoma in terms of initial diagnosis, staging, prognosis, and treatment response assessment. Recent advancements in imaging technology and methodologies, along with the development of artificial intelligence, have revolutionized the evaluation of complex imaging data, enhancing the diagnostic and predictive power of PET in lymphoma. However, FDG is not cancer-specific, but it primarily reflects glucose metabolism, which has prompted the investigation of alternative PET tracers to address this limitation. Novel PET radiotracers, such as fibroblast activation protein inhibitors targeting the tumor microenvironment, have recently shown promising results in evaluating various malignancies compared to FDG PET. Furthermore, with the rapid advancements in immunotherapy and the favorable imaging properties of 89Zr, immunoPET has emerged as a promising modality, offering insights into the functional and molecular status of the immune system. ImmunoPET can also facilitate the development of new antibody therapeutics and radioimmunotherapy by providing pharmacokinetic and pharmacodynamic data. This review provides comprehensive insights into the current clinical applications of FDG PET in lymphoma, while also exploring novel PET imaging radiotracers beyond FDG, discussing their mechanisms of action and potential impact on patient management.

Experiments for the Assessment of the Probabilistic Multistage Algorithm for Damage Detection in Flat and Curved CFRP Panels

The Probabilistic Multistage (PM) algorithm was developed by authors for identifying and localizing damages in isotropic plates. More specifically, PM algorithm consists of a fully automated probabilistic damage imaging methodology based on ultrasonic guided wave propagation and a 5-sensor array working in a pitch-catch approach. The algorithm processes the gathered data in three steps to identify key features associated with damage. The aim of this work is to assess the effectiveness of the PM algorithm on more complex structures. Specifically, the study investigates three cases of study, made of Carbon Fiber Reinforced Plastic (CFRP) composite, characterized by different geometries and layups. The algorithm is initially tested on panels with artificial damage in the form of a Teflon disk located in a specific location between the middle laminae of the panels, which is used to replicate the effect of delamination. In order to expand the experimental dataset without incurring additional costs or waste, new damage conditions are simulated by adding masses on the upper surface of the panels. Each plate is investigated considering three different damage sizes and 16 different damage locations. The proposed algorithm successfully detects damages both within and outside the sensor network. The PM algorithm produces a clear damage positioning map and a positioning (probabilistic field) range for the identified damage. This information can be used to assist operators in conducting inspections more efficiently by focusing on the highlighted areas, which may potentially lead to reduced maintenance and repair expenses.

Electronic interactions in Dirac fluids visualized by nano-terahertz spacetime interference of electron-photon quasiparticles.

Ultraclean graphene at charge neutrality hosts a quantum critical Dirac fluid of interacting electrons and holes. Interactions profoundly affect the charge dynamics of graphene, which is encoded in the properties of its electron-photon collective modes: surface plasmon polaritons (SPPs). Here, we show that polaritonic interference patterns are particularly well suited to unveil the interactions in Dirac fluids by tracking polaritonic interference in time at temporal scales commensurate with the electronic scattering. Spacetime SPP interference patterns recorded in terahertz (THz) frequency range provided unobstructed readouts of the group velocity and lifetime of polariton that can be directly mapped onto the electronic spectral weight and the relaxation rate. Our data uncovered prominent departures of the electron dynamics from the predictions of the conventional Fermi-liquid theory. The deviations are particularly strong when the densities of electrons and holes are approximately equal. The proposed spacetime imaging methodology can be broadly applied to probe the electrodynamics of quantum materials.

Integrated miRNA Signatures: Advancing Breast Cancer Diagnosis and Prognosis.

Breast cancer ranks first in incidence and second in deaths worldwide, presenting alarmingly rising mortality rates. Imaging methodologies and/or invasive biopsies are routinely used for screening and detection, although not always with high sensitivity/specificity. MicroRNAs (miRNAs) could serve as diagnostic and prognostic biomarkers for breast cancer. We have designed a computational approach combining transcriptome profiling, survival analyses, and diagnostic power calculations from 1165 patients with breast invasive carcinoma from The Cancer Genome Atlas (TCGA-BRCA). Our strategy yielded two separate miRNA signatures consisting of four up-regulated and five down-regulated miRNAs in breast tumors, with cumulative diagnostic strength of AUC 0.93 and 0.92, respectively. We provide direct evidence regarding the breast cancer-specific expression of both signatures through a multicancer comparison of >7000 biopsies representing 19 solid cancer types, challenging their diagnostic potency beyond any of the current diagnostic methods. Our signatures are functionally implicated in cancer-related processes with statistically significant effects on overall survival and lymph-node invasion in breast cancer patients, which underlie their strong prognostic implication. Collectively, we propose two novel miRNA signatures with significantly elevated diagnostic and prognostic power as a functionally resolved tool for binary and accurate detection of breast cancer and other tumors of the female reproductive system.

Analysis of wood anatomy via high-resolution micro-MRI and micro-CT: first approaches and results from Pozzo di Bellafonte (Montaione, Florence, Italy)

Several artefacts were found during the excavation performed at “Pozzo di Bellafonte”, located in an archaeological area in the province of Florence (Italy). The material found in the archaeological site included a large amount of wood samples; therefore, a method for understanding the positioning, the shape and the orientation of the wood sample vessels was necessary for identification of botanical taxa. In recent years, the study of ancient remains has benefited greatly from the use of imaging techniques commonly used for medical diagnosis and biomedical research, such as the micro-Computed Tomography (micro-CT) and high-field micro-Magnetic Resonance Imaging (micro-MRI). In this paper such imaging methodologies were employed for assessing the internal structure of representative wood samples extracted from the above mentioned. The complementarity of micro-CT and micro-MRI for the investigation of wood specimen anatomy is also discussed, by underlining what micro-MRI provides as compared with micro-CT and by proposing a tool constituted by the combination of micro-CT/micro-MRI for the study of such wood samples in a multimodal approach.

High contrast computational imaging with vortex phase diversity

Abstract Optical imaging systems employing spatially incoherent illumination are widely used in routine imaging applications like photography and bright-field microscopy. We describe an incoherent computational imaging system that uses an open aperture as well as a vortex phase aperture for recording the same scene. The two raw recorded images provide a diversity of information that can be effectively combined using the generalized Wiener filter. For the specific choice of aperture functions used here, the two corresponding generalized Wiener filters have nearly opposing polarity. This property leads to an effective computational PSF whose central lobe is $0.6$ times smaller compared to the diffraction-limited PSF and has a super-oscillatory character with side-lobes. The resultant computational imaging system provides images with significantly improved contrast. While our methodology requires two image records, the enhanced point spread function (PSF) with super-oscillatory character is obtained by employing bulk off-the-shelf optical elements instead of sub-wavelength structured masks. The vortex phase diversity concept along with computational image reconstructions are illustrated with both simulation and experimental data. The proposed imaging methodology may be used to improve imaging performance for wide ranging imaging systems without changing their form factor.

One-Year Surveillance of a Patient Cohort’s Lung Ultrasonography Findings Post Mild-Moderate COVID-19 Infection

Objective: Lung involvement due to COVID-19 infection relates to patient clinical outcomes. However, little evidence exists on whether an abnormal lung sonogram, during an initial outpatient infection, may signal an abnormal immunologic response and lasting abnormalities. The aim of this study was to observe whether high-risk outpatients, previously infected with SARS CoV-2 and found to have an initial abnormal lung sonogram, had persistent diagnostic findings at a 1-year re-examination. Materials and Methods: A prospective longitudinal cohort study was performed, based on a prior study completed in January 2022 wherein 55/201 (27%) of consecutive outpatients infected with SARS-CoV-2 received therapy at an outpatient monoclonal antibody clinic and had presented with an abnormal lung ultrasound containing at least 3 apical lung B-line artifacts (LUS+). One year later, the original 55 LUS+ patients were contacted for re-examination; as a result, 14 LUS+ patients consented to a repeat LUS tospecifically recheck the apical area of the lungs for persistent B-line artifacts. The same ultrasound equipment system and imaging methodology were used for this cohort of patients. Additional data was collected regarding COVID-19 test-positive re-infection, persistent pulmonary symptoms during the past year, and COVID-19 vaccination status. Results: Of the 14 LUS+ patients, the mean cohort age was 63 ± 9 years, of which 71% were men. Over the ensuing year, 43% had persistent symptoms, 71% had updated vaccination, and 36% had a self-reported re-infection. The re-infection was reported to have occurred a median of 7 months after the initial infection. The cohort was sub-divided into those with persistent LUS+ compared to those that reverted to a normal scan. The mean age was 69 ± 10 years among those LUS+ compared to 56 ± 8 years in the normalized group. The body mass index (BMI) was 32 kg/m2 in those LUS+ compared to 27 kg/m2 in the normalized group. The cohort has 6 men and 2 women in the LUS+ group compared to 4 men and 2 women in the normalized group. One LUS+ patient out of eight reportedly had been reinfected compared to four normalized patients out of six stated being reinfected. Three LUS+ patients out of eight reported having persistent symptoms compared to three normalized patients out of six complaining of continued COVID-19 symptoms. Conclusion: Based on the 1-year repeated LUS, for this cohort, there was a high prevalence of persistent symptoms and lung abnormalities, in those who had mild-moderate outpatient COVID-19 and received monoclonal antibody therapy. The longitudinal data collection on this cohort of COVID-19 positive outpatients may suggest that individual immune responses or viral virulence factors may play a role in B-line persistence. Certainly, future LUS results may be confounded by a previous COVID-19 infection.

Nondestructively Determining Soluble Solids Content of Blueberries Using Reflection Hyperspectral Imaging Technique

Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The reflection hyperspectral images in the 900–1700 nm waveband range were collected from 480 fresh blueberry samples. An image analysis pipeline was developed to extract the spectrums of blueberries from the hyperspectral images. A regression model for quantifying SSC values was successfully established based on the full range of wavebands, achieving the highest RP2 of 0.8655 and the lowest RMSEP value of 0.4431 °Brix. Furthermore, three variable selection methods, namely the Successive Projections Algorithm (SPA), interval PLS (iPLS), and Genetic Algorithm (GA), were utilized to identify the feature wavebands for modeling. The models calibrated from feature wavebands generated an RMSEP of 0.4643 °Brix, 0.4791 °Brix, and 0.4764 °Brix, as well as the RP2 of 0.8507, 0.8397, and 0.8420 for SPA, iPLS, and GA, respectively. Furthermore, a pseudo-color distribution diagram of the SSC values within blueberries was successfully generated based on established models. This study demonstrated a novel approach for blueberry quality detection and inspection by jointly using hyperspectral imaging and machine learning methodologies. It can serve as a valuable reference for the development of grading equipment systems and portable testing devices for fruit quality assurance.

Rapid Hyperspectral Photothermal Mid-Infrared Spectroscopic Imaging from Sparse Data for Gynecologic Cancer Tissue Subtyping.

Ovarian cancer detection has traditionally relied on a multistep process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. This method resolves the longstanding trade-off between imaging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled data sets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%. Our work demonstrates the feasibility of integrating rapid MIR hyperspectral photothermal imaging with machine learning in enhancing ovarian cancer tissue characterization, paving the way for quantitative, label-free, automated histopathology.

From gut to brain: unveiling probiotic effects through a neuroimaging perspective-A systematic review of randomized controlled trials.

The gut-brain axis, a bidirectional communication network between the gastrointestinal system and the brain, significantly influences mental health and behavior. Probiotics, live microorganisms conferring health benefits, have garnered attention for their potential to modulate this axis. However, their effects on brain function through gut microbiota modulation remain controversial. This systematic review examines the effects of probiotics on brain activity and functioning, focusing on randomized controlled trials using both resting-state and task-based functional magnetic resonance imaging (fMRI) methodologies. Studies investigating probiotic effects on brain activity in healthy individuals and clinical populations (i.e., major depressive disorder and irritable bowel syndrome) were identified. In healthy individuals, task-based fMRI studies indicated that probiotics modulate brain activity related to emotional regulation and cognitive processing, particularly in high-order areas such as the amygdala, precuneus, and orbitofrontal cortex. Resting-state fMRI studies revealed changes in connectivity patterns, such as increased activation in the Salience Network and reduced activity in the Default Mode Network. In clinical populations, task-based fMRI studies showed that probiotics could normalize brain function in patients with major depressive disorder and irritable bowel syndrome. Resting-state fMRI studies further suggested improved connectivity in mood-regulating networks, specifically in the subcallosal cortex, amygdala and hippocampus. Despite promising findings, methodological variability and limited sample sizes emphasize the need for rigorous, longitudinal research to clarify the beneficial effects of probiotics on the gut-brain axis and mental health.

Advanced Imaging Methodology in Bacterial Biofilms with a Fluorescent Enzymatic Sensor for pepN Activity.

This research explores the use of the pepN activity fluorescent sensor DCM-Ala in bacterial biofilms, emphasizing its significance due to the critical role of biofilms in various biological processes. Advanced imaging techniques were employed to visualize pepN activity, introducing a novel approach to examining biofilm maturity. We found that the overexpression of pepN increases the ability of E. coli to form biofilm. The findings demonstrate varying levels of pepN activity throughout biofilm development, suggesting potential applications in biofilm research and management. The results indicate that the fluorescent emission from this sensor could serve as a reliable indicator of biofilm maturity, and the imaging techniques developed could enhance our understanding and control of biofilm-related processes. This work highlights the importance of innovative methods in biofilm study and opens new avenues for utilizing chemical emissions in biofilm management.

Dynamic plasmonic structural color with deep sub-diffraction resolution based on borophene metasurfaces.

Structural colors have seen rapid development in recent years, yet two-dimensional (2D) materials have seldom taken center stage as pixel materials. In this study, we propose a novel approach utilizing the emerging 2D material borophene, wherein resulting metasurfaces can generate plasmonic structural colors with tunability and ultra-high resolution. Numerical investigations demonstrate that borophene metasurfaces support visible localized surface plasmon resonances at deep subwavelength scales under linear-polarized light excitation, thus enabling the realization of structural colors with an unparalleled resolution of up to 106 dots per inch (dpi)-an advancement of one order of magnitude over conventional counterparts. Furthermore, by modulating the electron density of borophene, these structural colors can be dynamically tuned across a broad spectrum. We highlight their high robustness against incident light angles and explore the influence of periodicity and polarization angle on color rendition. Finally, we present their potential applications in optical anti-counterfeiting, encryption, and switchable imaging methodologies. This work may promise future advancements in ultracompact, tunable, and lightweight display technologies.

A study on an improved laser speckle contrast blood flow imaging methodology

The laser speckle contrast imaging technique based on the dynamic light scattering theory presents a non-scanning and wide-field method for blood flow imaging. However, its accuracy in biological tissues is limited to the decreased contrast and reduced image clarity as conventional single-exposure approaches are susceptible to static scattering. In this paper, based on the adaptive window space direction contrast (awsdK) imaging method proposed by the laboratory, combined with the optimized single exposure technology, the effect of static scattering under a single exposure is reduced. The experimental results show that the method can effectively correct static scattering and eliminate the effect of system noise on speckle contrast. This method not only improves the imaging quality, but also realizes the rapid monitoring of blood flow changes by using the speckle contrast ratio measured in a single exposure, which provides an effective solution for the further development of laser speckle contrast imaging technology.

Cryo-EM structures of cotton wool plaques’ amyloid β and of tau filaments in dominantly inherited Alzheimer disease

Cotton wool plaques (CWPs) have been described as features of the neuropathologic phenotype of dominantly inherited Alzheimer disease (DIAD) caused by some missense and deletion mutations in the presenilin 1 (PSEN1) gene. CWPs are round, eosinophilic amyloid-β (Aβ) plaques that lack an amyloid core and are recognizable, but not fluorescent, in Thioflavin S (ThS) preparations. Amino-terminally truncated and post-translationally modified Aβ peptide species are the main component of CWPs. Tau immunopositive neurites may be present in CWPs. In addition, neurofibrillary tangles coexist with CWPs. Herein, we report the structure of Aβ and tau filaments isolated from brain tissue of individuals affected by DIAD caused by the PSEN1 V261I and A431E mutations, with the CWP neuropathologic phenotype. CWPs are predominantly composed of type I Aβ filaments present in two novel arrangements, type Ic and type Id; additionally, CWPs contain type I and type Ib Aβ filaments. Tau filaments have the AD fold, which has been previously reported in sporadic AD and DIAD. The formation of type Ic and type Id Aβ filaments may be the basis for the phenotype of CWPs. Our data are relevant for the development of PET imaging methodologies to best detect CWPs in DIAD.