Reflectance Imaging Research Articles

In vitro Assessment of lesion activity using simultaneous time-resolved reflectance imaging at 1300 and 1950nm.

The aim of this study was to explore the feasibility of a time-resolved reflectance imaging system employing a single photodetector to assess the activity of caries lesions that exploits the differential absorption of water at 1300 and 1950nm. The time-resolved reflectivity of 10 active and 10 arrested lesions on the proximal surfaces and 5 active and 5 arrested lesions on the occlusal surfaces of extracted teeth were monitored simultaneously at 1300 and 1950nm during forced air drying for 60s. The presence of a highly mineralized surface zone measured with microcomputed tomography (microCT) was used to indicate lesion activity. Multiple kinetic parameters were extracted from the acquired short wavelength infrared (SWIR) intensity versus time dehydration curves and used to assess lesion activity. Differences in the reflectivity between curves acquired at 1300 and 1950nm due to differential absorption of water provided improved discrimination between active and arrested lesions over the use of 1950nm alone. This study demonstrates that it is feasible to use a device with a single photodetector operating at 1950nm to collect dehydration curves for the assessment of lesion activity and that a system employing two SWIR wavelengths with differential water absorption can improve the performance of lesion activity assessment.

Extent and Topography of Subretinal Drusenoid Deposits Associate With Rod-Mediated Vision in Aging and AMD: ALSTAR2 Baseline.

In AMD, rod-mediated dark adaptation (RMDA) at 5° eccentricity is slower in eyes with subretinal drusenoid deposits (SDDs) than in eyes without. Here we quantified SDD burden using supervised deep learning for comparison to vision and photoreceptor topography. In persons ≥60 years from the Alabama Study on Early Age-Related Macular Degeneration 2, normal, early AMD, and intermediate AMD eyes were classified by the AREDS nine-step system. A convolutional neural network was trained on 55°-wide near-infrared reflectance images for SDD segmentation. Trained graders annotated ground truth (SDD yes/no). Predicted and true datasets agreed (Dice coefficient, 0.92). Inference was manually proofread using optical coherence tomography. The mean SDD area (mm2) was compared among diagnostic groups (linear regression) and to vision (age-adjusted Spearman correlations). Fundus autofluorescence images were used to mask large vessels in SDD maps. In 428 eyes of 428 persons (normal, 218; early AMD, 120; intermediate AMD, 90), the mean SDD area differed by AMD severity (P < 0.0001): 0.16 ± 0.87 (normal), 2.48 ± 11.23 (early AMD), 11.97 ± 13.33 (intermediate AMD). Greater SDD area was associated with worse RMDA (r = 0.27; P < 0.0001), mesopic (r = -0.13; P = 0.02) and scotopic sensitivity (r = -0.17; P < 0.001). SDD topography peaked at 5° superior, extended beyond the Early Treatment of Diabetic Retinopathy Study grid and optic nerve, then decreased. SDD area is associated with degraded rod-mediated vision. RMDA 5° (superior retina) probes where SDD is maximal, closer to the foveal center than the rod peak at 3 to 6 mm (10.4°-20.8°) superior and the further eccentric peak of rod:cone ratio. Topographic data imply that factors in addition to rod density influence SDD formation.

High-resolution compound-specific mapping in works of art via data fusion of MA-XRPD with hyperspectral data (part 1: Method evaluation)

BackgroundHyperspectral imaging techniques have emerged as powerful tools for non-invasive investigation of artworks. This paper employs either reflectance imaging spectroscopy (RIS) or macroscopic X-ray fluorescence (MA-XRF) imaging in combination with macroscopic X-ray powder diffraction (MA-XRPD) for state-of-the-art chemical imaging of painted cultural heritage artefacts. While RIS can provide molecular information and MA-XRF can offer elemental distribution maps of paintings of high lateral resolution, the unique advantage of MA-XRPD lies in its ability to visualize the distributions of specific pigments and estimate in a quantitative manner the relative concentrations of the crystalline phases at the surface of artworks. However, MA-XRPD is more time-consuming and offers a lower lateral resolution than RIS and MA-XRF. ResultsThis study introduces a machine learning (ML) approach to obtain the distribution of specific compounds on the surface of artworks with a resolution that is comparable to that of RIS and MA-XRF data but with the compound specificity of MA-XRPD. The general aim is to expedite non-destructive artwork imaging analysis by fusing data from different imaging modalities via machine learning models. The effect of preprocessing techniques to enhance the predictive accuracy of the models is explored. The paper demonstrates the method's efficacy on a 16th-century illuminated manuscript, showcasing the feasibility of predicting compound-specific distribution maps. Three evaluation methods—visual examination of the predicted distribution, root mean square errors (RMSE), and feature permutation importance (FPI)—are employed to assess model performance. Fusing MA-XRF with MA-XRPD led to the best RMSE scores overall. However, fusing the RIS and MA-XRPD data blocks also yield very satisfactory and easily interpretable high-resolution compound maps. SignificanceWhile MA-XRPD allows for highly specific imaging of artworks, its time-consuming nature and limited resolution presents a bottleneck during non-invasive imaging of painted works of art. By integrating data from more time-efficient hyperspectral techniques such as MA-XRF and RIS, and employing machine learning, we expedite the process without compromising accuracy. The fusion process can also denoise the distribution maps, improving their readability for heritage professionals and art historical scholars.

Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging

The most frequent kind of damage to strawberries is bruising. However, most of the bruises are so barely perceptible at an early stage on the surface, that detection of them with the human eye is quite challenging. This study proposes a method for accurately detecting and classifying the damage using reflectance imaging spectroscopy. In order to carry out the study, an experiment was devised to artificially induce bruises and a dataset was generated at different bruise intervals. A model for detecting and classifying bruises at their latent stage was developed using machine learning classifiers, including support vector machines (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), to investigate the changes over time after bruise occurrence on the detection performance. Regression models for the prediction of bruising time were developed using partial least square regression (PLSR), RF, gradient boosting (GB), support vector regression (SVR), and DT. Among the compared models, both SVM and LDA could achieve 99.99 % classification accuracy. RF was regarded as being the most advisable for detection and prediction jobs due to its high performance. It achieved MSE of 0.052 and R2 of 0.989 for prediction.

Advances in Automated Pigment Mapping for 15th-Century Manuscript Illuminations Using 1-D Convolutional Neural Networks and Hyperspectral Reflectance Image Cubes

Reflectance imaging spectroscopy (RIS) is invaluable in mapping and identifying artists’ materials in paintings. The analysis of the RIS image cube first involves classifying the cube into spatial regions, each having a unique reflectance spectrum (endmember). Second, endmember spectra are analyzed for spectral features useful to identify the pigments present to create labeled classes. The analysis process for paintings remains semi-automated because of the complex diffuse reflectance spectra due to the use of intimate pigment mixtures and optically thin paint layers by the artist. As a result, even when a group of related paintings are analyzed, each RIS cube is analyzed individually, which is time consuming. There is a need for new approaches to more efficiently analyze RIS cubes of related paintings to address the growing interest in the study of related paintings within a group of artists or artistic schools. This work builds upon prior investigations of 1-D spectral convolutional neural networks (CNNs) to address this need in two ways. First, an expanded training set was used—ten illuminated manuscripts created by artists stylistically grouped under the notname “Master of the Cypresses” (15th century Seville, Spain). Second, two 1-D CNN models were trained from the RIS cubes: reflectance and the first derivative. The results showed that the first derivative-trained CNN generally performed better than the reflectance-trained CNN in creating accurate labeled material maps for these illuminated manuscripts.

Detection of Protective Coatings Applied on Baroque Amber Artworks: Case Studies

Amber has been used to create decorative items for centuries, but its degradation presents challenges for conservators. This study identifies substances historically used to protect amber objects, especially those from 17th and 18th century Gdansk workshops. Despite their historical value, information on amber conservation is scarce. Traditional substances are noted, but their exact compositions and effects on amber remain unclear. Synthetic resins, introduced in the late 19th century, also degrade, complicating conservation due to their removal difficulty and interference with amber identification. This research aimed to develop methods for detecting and analyzing protective coatings on amber objects using macroscopic and microscopic techniques. Initial methods included analytical photography under visible and UV light and reflectance imaging spectroscopy (RIS) to assess the surface. Raman spectroscopy (RS) and X-ray fluorescence spectroscopy (XRF) were used for detailed analysis. RS provided precise layer-specific information but was sensitive to surface conditions, while XRF quickly identified inorganic compounds but not organic materials. Examining amber objects from Polish collections using this methodology revealed various protective substances, including synthetic resins and nitrocellulose varnishes. This research contributes to amber conservation by proposing a comprehensive material analysis approach, essential for developing effective conservation strategies for these historic objects.

A novel translational model of atherosclerosis, the ex vivo pump-perfused amputated human limb model

The preclinical study of atherosclerosis has traditionally centred around the use of small animal models, translating to large animal models, prior to first-in-man studies. We propose to disrupt this paradigm by designing an ex vivo pump perfused human limb model. The novel model consists of taking a freshly amputated limb and incorporating it into an ex situ pump-perfused bypass system (akin to extracorporeal membrane oxygenation), circulating warmed, oxygenated blood. The circuit incorporates an introducer sheath and guiding catheter for intravascular imaging and X-ray angiography. Regular monitoring is performed using blood gas analysis, aiming for physiological parameters. The model maintains oxygen saturations > 99% for the length of perfusion (up to 6-h). Clinical grade X-ray angiography, intravascular ultrasound and optical coherence tomography have been successfully performed. Indocyanine green, a near-infrared fluorescent dye that localises to atherosclerotic plaque, has been injected into the system and left to circulate for 90-min. Fluorescence reflectance imaging of the dissected arterial bed confirmed uptake in areas of calcific atherosclerotic plaque on intravascular imaging. This is the first demonstration of an ex vivo pump-perfused “living” limb experimental model of atherosclerosis, which shows promise for future studies in translational interventional imaging and molecular targeting.

Structured-illumination reflectance imaging combined with deep learning for detecting early decayed oranges

Fungal rot is the most serious defect in post-harvest citrus, and the timely detection and removal of early rotten citrus are particularly crucial in reducing economic losses. Imaging technology under structured illumination is a promising method to fruit surface defects. This study aims to assess the feasibility of combining the structured-illumination reflectance imaging (SIRI) system with deep learning technology for the identification and segmentation of early rot in citrus. Phase-shifted images of oranges were acquired at four spatial frequencies and demodulated to obtain direct component (DC) and amplitude component (AC) images. The optimal spatial frequency for detecting rot was determined to be 0.20 cycles mm−1 based on the contrast index between the decay and sound areas in the images. Then, the AC images are subjected to brightness correction and augmentation. Three segmentation methods, global thresholding, watershed segmentation and Unet were used to segment the rotten areas in the images. Unet achieved optimal results on AC images, with an overall accuracy of 99.4 % and an IoU of 0.903. The gradient-weighted class activation mapping (Grad-CAM) was used to visualize the areas recognized by Unet for orange rot, yielding satisfactory results. This study effectively demonstrated the defect recognition capability of SIRI combined with deep learning and providing a reliable solution for early decayed detection in oranges.

Disclosure of a Concealed Michelangelo-Inspired Depiction in a 16th-Century Painting.

Some paintings may have hidden depictions beneath the visible surface, which can provide valuable insights into the artist's creative process and the genesis of the artwork. Studies have shown that these covered paintings can be revealed through image-based techniques and integrated data processing. This study analyzes an oil painting by Beceri from the mid-16th century depicting the Holy Family, owned by the Uffizi Galleries. During the analysis of the materials, we discovered evidence of pictorial layers beneath the visible scene. To uncover the hidden figuration, we applied a multimodal approach that included microprofilometry, reflectance imaging spectroscopy, macro X-ray fluorescence, and optical coherence tomography. We analyzed the brushstrokes of the hidden painting, visualized the underdrawing, located the painted areas beneath the outermost painting, and quantified the thicknesses of the pictorial layers. The pigments used for the underpainting were identified through cross-analysis of X-ray fluorescence and spectral correlation maps. The underlying pictorial subject, Leda and the Swan, appears to be inspired by a long-lost and replicated work by Michelangelo. This information places Beceri and his production in a more defined context.

Discovery of pararealgar and semi-amorphous pararealgar in Rembrandt's The Night Watch: analytical study and historical contextualization

This article reports on the discovery of pararealgar and semi-amorphous pararealgar in Rembrandt's masterpiece The Night Watch. A large-scale research project named Operation Night Watch was started in 2019. A variety of non-invasive analytical imaging techniques, together with paint sample research, has provided new information about Rembrandt's pigments, materials, and techniques as well as the current condition of the painting. Macroscopic X-ray fluorescence, macroscopic X-ray powder diffraction and reflectance imaging spectroscopy identified the presence of arsenic sulfide pigments and degradation products of these pigments in the doublet sleeves and embroidered buff coat worn by Lieutenant Willem van Ruytenburch (central figure to the right of Captain Frans Banninck Cocq). Examination by light microscopy of two paint samples taken from this area shows a mixture of large sharp-edged tabular yellow and orange to red pigment particles, and scanning electron microscopy-energy dispersive X-ray analysis identified these particles as containing arsenic and sulfur. Using micro-Raman spectroscopy, the yellow particles were identified as pararealgar, and the orange to red particles as semi-amorphous pararealgar. Synchrotron-based X-ray diffraction allowed visualization of the presence of multiple degradation products associated with arsenic sulfides throughout the paint layer. The discovery of pararealgar and semi-amorphous pararealgar is a new addition to Rembrandt's pigment palette. To contextualize our findings and to hypothesize why, how, and where Rembrandt obtained the pigments, we studied related historical sources. A comprehensive review of historical sources gives insight into the types of artificial arsenic sulfides that were available and suggests that a broader range of arsenic pigments could have been available in Amsterdam in the seventeenth century than previously thought. This is supported by the use of a very similar mixture of pigments by Willem Kalf (1619–1693), a contemporary artist based in Amsterdam. Together with the condition of the particles in the paint cross sections, this brings us to the conclusion that Rembrandt intentionally used pararealgar and semi-amorphous pararealgar, together with lead–tin yellow and vermilion, to create an orange paint.

A Label-free Optical Biosensor-Based Point-of-Care Test for the Rapid Detection of Monkeypox Virus.

Diagnostic approaches that combine the high sensitivity and specificity of laboratory-based digital detection with the ease of use and affordability of point-of-care (POC) technologies could revolutionize disease diagnostics. This is especially true in infectious disease diagnostics, where rapid and accurate pathogen detection is critical to curbing the spread of disease. We have pioneered an innovative label-free digital detection platform that utilizes Interferometric Reflectance Imaging Sensor (IRIS) technology. IRIS leverages light interference from an optically transparent thin film, eliminating the need for complex optical resonances to enhance the signal by harnessing light interference and the power of signal averaging in shot-noise-limited operation to achieve virtually unlimited sensitivity. In our latest work, we have further improved our previous 'Single-Particle' IRIS (SP-IRIS) technology by allowing the construction of the optical signature of target nanoparticles (whole virus) from a single image. This new platform, 'Pixel-Diversity' IRIS (PD-IRIS), eliminated the need for z-scan acquisition, required in SP-IRIS, a time-consuming and expensive process, and made our technology more applicable to POC settings. Using PD-IRIS, we quantitatively detected the Monkeypox virus (MPXV), the etiological agent for Monkeypox (Mpox) infection. MPXV was captured by anti-A29 monoclonal antibody (mAb 69-126-3) on Protein G spots on the sensor chips and were detected at a limit-of-detection (LOD) - of 200 PFU/ml (~3.3 attomolar). PD-IRIS was superior to the laboratory-based ELISA (LOD - 1800 PFU/mL) used as a comparator. The specificity of PD-IRIS in MPXV detection was demonstrated using Herpes simplex virus, type 1 (HSV-1), and Cowpox virus (CPXV). This work establishes the effectiveness of PD-IRIS and opens possibilities for its advancement in clinical diagnostics of Mpox at POC. Moreover, PD-IRIS is a modular technology that can be adapted for the multiplex detection of pathogens for which high-affinity ligands are available that can bind their surface antigens to capture them on the sensor surface.

Confirming size-exclusion chromatography as a clinically relevant extracellular vesicles separation method from 1mL plasma through a comprehensive comparison of methods

BackgroundExtracellular vesicles (EVs) are amongst the most promising candidates for developing blood-based biomarkers. However, patient sample availability is a key barrier to translational research whilst most biobanks store samples of 1.5mL volume or less. To date, there is no consensus on the most suitable method of EV separation and current techniques frequently require large volumes of biofluids, complicated technology, technical expertise, or significant operating costs, which prevents their widespread adoption by less EV-focussed laboratories. Therefore, there is a need for an easy and reproducible method that separates representative EVs from clinically relevant 1mL volumes of plasma prior to subsequent biomarker identification.MethodsIn this study, EVs were separated from a clinically relevant 1mL volume of human plasma using four different separation techniques: size exclusion chromatography (SEC), differential ultracentrifugation, precipitation, and immunoaffinity magnetic bead capture. The EVs were characterised using several orthogonal techniques (protein quantification, nanoparticle tracking analysis, transmission electron microscopy, Western blot, single particle interferometric reflectance imaging sensing, and mass spectrometry-based proteomics) to comprehensively compare the separated samples.ResultsWe provide examples of anticipated results highlighting that SEC-processed samples have greater protein quantification yield, greater particle yield of the expected size for EVs, and sufficient EV purity, which facilitates effective EV cargo assessment by proteomics. Moreover, we confirm significant overlap with known EV-related proteins within the Vesiclepedia database. Additionally, using single particle interferometric reflectance imaging sensing (Leprechaun®), we identify that SEC has the most representative surface tetraspanin distribution of the separated EV population compared to unprocessed plasma.DiscussionGiven that SEC requires minimal expertise, no complicated technology and can separate EVs within 90 min, this comparison reinforces SEC as a clinically relevant EV separation method from 1mL of plasma making it suitable for widespread implementation.

Analyzing Cooling Island Effect of Urban Parks in Zhengzhou City: A Study on Spatial Maximum and Spatial Accumulation Perspectives

As a result of urbanization, cities worldwide are experiencing urban heat island (UHI) challenges. Urban parks, which are essential components of urban blue and green landscapes, typically have lower temperatures in providing outdoor comfort than their surroundings with impervious surfaces. This phenomenon, known as the park cooling island effect (PCIE), has been recognized as an effective approach to mitigate the negative effects of the UHI in the context of sustainable development of urban environment. To cope with the serious UHI challenge and to guide urban park planning and design for Zhengzhou City, which is one of the China’s new first-tier cities, 35 urban parks in the city were analyzed in this study. Remotely sensed land surface temperature (LST) and reflectance images by Landsat 9 and Sentinel-2 were selected as data sources. A cubic polynomial model that depicts the relationship between the LST and the distance from the park edge was first built for each park. Based on this model, the spatial maximum perspective metrics (including the park cooling area (PCA) and park cooling efficiency (PCE)) and the spatial accumulation perspective metrics (including park cooling intensity (PCI) and park cooling gradient (PCG)) were calculated to quantify the PCIE of each park. The 35 parks were divided into three groups using the hierarchical clustering method for further analysis. For each group, the metrics of the PCIE were statistically analyzed, and the main factors influencing the PCIE were identified by the Spearman correlation coefficient. The results indicate the following: (1) The 35 urban parks exhibit an obvious PCIE. The maximum cooling distance is 133.95 ± 41.93 m. The mean LST of the park is 3.01 ± 1.23 °C lower than that within the maximum cooling distance range. (2) The PCIE varies among different types of parks. Parks with large areas and covered by certain water bodies generally exhibit higher PCA, PCI, and PCG values. However, parks with small areas and mainly covered by vegetation show higher PCE values, which makes them more economical in exerting the PCIE. (3) Park area and landscape shape index (LSI) were positively correlated with PCA, PCI, and PCG. However, there is a threshold in the relationship between the park area and the PCI. A park area of approximately 19 ha can produce a higher PCI than a smaller one. In central urban areas with limited space, parks with small areas, complex shapes, and predominant vegetation coverage can be designed to achieve higher cooling efficiency.

Characterization of Receptor Binding Affinity for Vascular Endothelial Growth Factor with Interferometric Imaging Sensor.

Wet Age-related macular degeneration (AMD) is the leading cause of vision loss in industrialized nations, often resulting in blindness. Biologics, therapeutic agents derived from biological sources, have been effective in AMD, albeit at a high cost. Due to the high cost of AMD treatment, it is critical to determine the binding affinity of biologics to ensure their efficacy and make quantitative comparisons between different drugs. This study evaluates the in vitro VEGF binding affinity of two drugs used for treating wet AMD, monoclonal antibody-based bevacizumab and fusion protein-based aflibercept, performing quantitative binding measurements on an Interferometric Reflectance Imaging Sensor (IRIS) system. Both biologics can inhibit Vascular Endothelial Growth Factor (VEGF). For comparison, the therapeutic molecules were immobilized on to the same support in a microarray format, and their real-time binding interactions with recombinant human VEGF (rhVEGF) were measured using an IRIS. The results indicated that aflibercept exhibited a higher binding affinity to VEGF than bevacizumab, consistent with previous studies using ELISA and SPR. The IRIS system's innovative and cost-effective features, such as silicon-based semiconductor chips for enhanced signal detection and multiplexed analysis capability, offer new prospects in sensor technologies. These attributes make IRISs a promising tool for future applications in the development of therapeutic agents, specifically biologics.

Visualization of Keratopathy Associated With the Antibody-Drug Conjugate Belantamab Mafodotin Using Infrared Imaging in Patients With Multiple Myeloma.

The treatment of patients with relapsed/refractory multiple myeloma (RRMM) with the antibody-drug conjugate belantamab mafodotin is affected by ocular adverse effects, most frequently keratopathy with corneal microcyst-like epithelial changes (MECs). To assess ocular side effects, the "Keratopathy and Visual Acuity (KVA) scale," based on the extent of keratopathy subjectively graded on slit-lamp examination and the change in best corrected visual acuity from baseline, was created. Advanced corneal imaging techniques have been explored to further characterize MECs and identify objective imaging biomarkers. We examined whether infrared reflectance imaging of the anterior segment (AS-IR) could contribute to the assessment, monitoring, and documentation of corneal toxicity in patients treated with belantamab mafodotin. In addition to the KVA examination, AS-IR imaging was performed. AS-IR images were evaluated for presence of visible hyporeflective lesions and their spatial and temporal distribution between visits and compared with keratopathy identified on slit-lamp examination. To standardize the assessment, a scoring system for lesions on AS-IR was implemented for additional analysis. Nine patients undergoing treatment with belantamab mafodotin for up to 9 months were examined. All patients exhibited hyporeflective lesions on AS-IR imaging, indicative of corneal toxicity corresponding to MECs observed on slit-lamp examination. AS-IR lesions showed early occurrence, variable quantity and size, and distinct distribution patterns, correlating with clinical findings during treatment. As shown for belantamab mafodotin, AS-IR imaging represents a fast, noninvasive, supplemental method for documentation, monitoring, and assessment of corneal adverse effects during treatment with antibody-drug conjugates, which may enable more standardized analyses.

Structured-illumination reflectance imaging for the evaluation of microorganism contamination in pork: Effects of spectral and imaging features on its prediction performance

Structured-illumination reflectance imaging for the evaluation of microorganism contamination in pork: Effects of spectral and imaging features on its prediction performance

Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia

Soil erosion is a major environmental problem in Ethiopia, reducing topsoil and agricultural land productivity. Soil loss estimation is a critical component of sustainable land management practices because it provides important information about soil erosion hotspot areas and prioritizes areas that require immediate management interventions. This study integrates the Revised Universal Soil Loss Equation (RUSLE) with Google Earth Engine (GEE) to estimate soil erosion rates and map soil erosion in the Upper Tekeze Basin, Northern Ethiopia. SoilGrids250 m, CHIRPS-V2, SRTM-V3, MERIT Hydrograph, NDVI from sentinel collections and land use land cover (LULC) data were accessed and processed in the GEE Platform. LULC was classified using Random forest (RF) classification algorithm in the GEE platform. Landsat surface reflectance images from Landsat 8 Operational land imager (OLI) sensors (2021) was used for LULC classification. Besides, different auxiliary data were utilized to improve the classification accuracy. Using the RUSLE-GEE framework, we analyzed the soil loss rate in different agroecologies and LULC types in the upper Tekeze basin in Waghimra zone. The results showed that the average soil loss rate in the Upper Tekeze basin is 25.5 t ha−1 yr−1. About 63 % of the basin is experiencing soil erosion above the maximum tolerable rate, which should be targeted for land management interventions. Specifically, 55 % of the study area, which is covered by unprotected shrubland is experiencing mean annual soil loss of 34.75 t ha−1 yr−1 indicating the need for immediate soil conservation intervention. The study also revealed evidence that this high mean soil loss rate of the basin can be reduced to a tolerable rate by implementing integrative watershed management and exclosures. Furthermore, this study demonstrated that GEE could be a good source of datasets and a computing platform for RUSLE, in particular for data scarce semi-arid and arid environments. The results from this study are reliable for decision-making for rapid soil erosion assessment and intervention prioritization.

Non-invasive spectroscopic analyses of M. E. Chevreul’s colour plates: shedding new light on 19th century colour theory representation

During the early 19th century, colour’s theories flourished. In this context, Michel-Eugène Chevreul (1786–1889) developed a three-dimensional hemispherical colour space, reproduced and used for several decades by manufacturers, scientists, artists, architects, gardeners and designers, among others, to easily identify and classify colours. Despite the major influence and variety of chromatic spaces created by M. E. Chevreul, they have been the subject of a few scientific studies until now. This study will provide new information on the evolution of the printing technique, the materiality and the visual appearance of six different colour circles printed in 1847, 1855, 1861 and 1864. A dedicated analytical methodology was implemented combining visible and near-infrared reflectance imaging, X-ray fluorescence, Raman, short-wave infrared fibre optic reflectance and mid-infrared Fourier transform spectroscopies. This material identification revealed the coexistence of newly synthetized inorganic pigments, such as lead chromate, Prussian blue or Emerald/Scheele green, with established organic and inorganic pigments, such as cochineal lake, vermilion or minium. This study revealed a temporal evolution of the printing technique that leads to an improved colour representation.

Spatial analysis of recurrent glioblastoma reveals perivascular niche organization.

Tumor evolution is driven by genetic variation; however, it is the tumor microenvironment (TME) that provides the selective pressure contributing to evolution in cancer. Despite high histopathological heterogeneity within glioblastoma (GBM), the most aggressive brain tumor, the interactions between the genetically distinct GBM cells and the surrounding TME are not fully understood. To address this, we analyzed matched primary and recurrent GBM archival tumor tissues with imaging-based techniques aimed to simultaneously evaluate tumor tissues for the presence of hypoxic, angiogenic, and inflammatory niches, extracellular matrix (ECM) organization, TERT promoter mutational status, and several oncogenic amplifications on the same slide and location. We found that the relationships between genetic and TME diversity are different in primary and matched recurrent tumors. Interestingly, the texture of the ECM, identified by label-free reflectance imaging, was predictive of single-cell genetic traits present in the tissue. Moreover, reflectance of ECM revealed structured organization of the perivascular niche in recurrent GBM, enriched in immunosuppressive macrophages. Single-cell spatial transcriptomics further confirmed the presence of the niche-specific macrophage populations and identified interactions between endothelial cells, perivascular fibroblasts, and immunosuppressive macrophages. Our results underscore the importance of GBM tissue organization in tumor evolution and highlight genetic and spatial dependencies.

Single-photon lidar depth imaging based on sparsity adaptive matching pursuit method

Photon counting lidar has the ability to detect weak echo photons. However, in imaging applications, a small number of echo signals obtained in a very short acquisition time cannot get an accurate image estimation through the photon arrival time distribution histogram. Therefore, high-precision and fast imaging of an object using a small amount of weak echo photon signals is urgent to be solved. In this work, a sparse adaptive matching pursuit (SAMP) algorithm is proposed to process the echo photons without prior information to obtain the effective photon interval, which improves the reconstruction quality and imaging speed. By constructing a non-coaxial scanning imaging system to detect the face model, it has been experimentally proven that the proposed algorithm can achieve depth and reflectance image estimation under the condition of low power and low acquisition time of 4µs per pixel on average, and the mean absolute error (MAE) value is reduced by about 10 times compared to conventional reconstruction algorithm.