High Spatial Resolution Images Research Articles

Highly Efficient Fluorescent Probe to Visualize Protein Interactions at the Superresolution.

Superresolution microscopy (SR microscopy) of protein-protein interactions (PPIs) occurring in subcellular structures is essential for understanding cellular functions. However, a powerful and useful technology for SR microscopy of PPIs remains elusive. Here, we develop a highly efficient photoconvertible fluorescent probe, named split-Dendra2, for SR microscopy of PPIs in the cell. We found that split-Dendra2 enables a highly efficient detection of PPIs, making it possible to perform SR microscopy of PPIs with high spatial resolution and high image reconstruction fidelity. We demonstrate the utility of split-Dendra2 by visualizing PPIs occurring in small subcellular structures at the superresolution, such as clathrin-coated pits and focal adhesions, which cannot be visualized by the existing tools. Split-Dendra2 offers a powerful and useful tool that greatly expands the possibility of SR microscopy and can contribute to revealing the function of PPIs at the nanoscale resolution.

Uncertainty quantification for velocity measurement with 2D2C PIV

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

Bacterial imaging in tumour diagnosis.

Some bacteria, such as Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium), have an inherent ability to locate solid tumours, making them a versatile platform that can be combined with other tools to improve the tumour diagnosis and treatment. In anti-cancer therapy, bacteria function by carrying drugs directly or expressing exogenous therapeutic genes. The application of bacterial imaging in tumour diagnosis, a novel and promising research area, can indeed provide dynamic and real-time monitoring in both pre-treatment assessment and post-treatment detection. Different imaging techniques, including optical technology, acoustic imaging, magnetic resonance imaging (MRI) and nuclear medicine imaging, allow us to observe and track tumour-associated bacteria. Optical imaging, including bioluminescence and fluorescence, provides high-sensitivity and high-resolution imaging. Acoustic imaging is a real-time and non-invasive imaging technique with good penetration depth and spatial resolution. MRI provides high spatial resolution and radiation-free imaging. Nuclear medicine imaging, including positron emission tomography (PET) and single photon emission computed tomography (SPECT) can provide information on the distribution and dynamics of bacterial population. Moreover, strategies of synthetic biology modification and nanomaterial engineering modification can improve the viability and localization ability of bacteria while maintaining their autonomy and vitality, thus aiding the visualization of gut bacteria. However, there are some challenges, such as the relatively low bacterial abundance and heterogeneously distribution within the tumour, the high dimensionality of spatial datasets and the limitations of imaging labeling tools. In summary, with the continuous development of imaging technology and nanotechnology, it is expected to further make in-depth study on tumour-associated bacteria and develop new bacterial imaging methods for tumour diagnosis.

A painting authentication method based on multi-scale spatial-spectral feature fusion and convolutional neural network

The scientific authentication of paintings holds significant importance within the realm of art collection. Employing convolutional neural networks for the classification of authentic and counterfeit painting images based on color images is a viable but suboptimal choice. This study investigates the potential for authenticating paintings by incorporating high-spectral images alongside high-resolution spatial images. High-resolution and high-spectral images of genuine and counterfeit paintings were acquired using a push-broom digital scanning system. The processing methods presented in this approach for the acquired images are: 1) The study utilized the circular local binary pattern (LBP) and principal component analysis (PCA) to extract surface texture and spectral data from Chinese character images in paintings, encompassing both spatial and spectral dimensions. 2) The technique utilizing non-subsampling Shearlet transform (NSST) and pulse-coupled neural network (PCNN) was employed to integrate the spatial and spectral characteristics of the images into a pseudo-color image, producing a dataset of feature data for genuine and counterfeit paintings. 3) The experiments aimed to achieve the authenticity of artworks using EfficientNet v2-s, the hyperparameters of which were fine-tuned accordingly. The experimental findings demonstrate that this approach attained a 90.8 % accuracy on the test dataset, representing a 3.5 % enhancement over the existing top-performing three-dimensional convolutional neural network (3D-CNN).

Examining the Percent Canopy Cover and Health of Winter Wheat in No-Till and Conventional Tillage Plots Using a Drone

The percent canopy cover and health of winter wheat are important crop performance indicators. Thus, understanding how tillage management practices affect these indicators is beneficial for improving crop performance and consequently yield. The availability of high-resolution drone data with spectral characteristics provides an opportunity to examine the percent canopy cover and health of winter wheat in different tillage systems. This is because the use of drones provides real-time high spatial resolution and temporal images to effectively monitor winter wheat conditions throughout the growing season. Nonetheless, very limited studies have utilized drone data for assessing the percent canopy cover and health conditions of winter wheat for different tillage practices. This study aimed to examine the percent canopy cover and health of winter wheat in no-till and conventional tillage plots using a drone. We used the mean Normalized Difference Vegetation Index (NDVI) ± Standard Deviation (SD) (0.89 ± 0.04) of winter wheat for the growth stages of tillering, jointing, and boot/heading to generate the percent wheat canopy cover. The Normalized Difference Red-Edge (NDRE) produced for winter wheat at the middle and late growth stages was used as a proxy for wheat health condition. We found that the mean percentage canopy cover of winter wheat was about 4% higher in no-till compared to conventional tillage plots in most of the growing season. The mean NDRE ± standard error (SE) of winter wheat was about 0.44 ± 0.01 and 0.43 ± 0.01 for no-till and conventional tillage plots, respectively, during the mid- and late growth stages. There was no significant difference in either the percent canopy cover or health of winter wheat between no-till and conventional tillage plots. The results generated in this study could be used to support farmers’ decision-making process regarding tillage practices and wheat crop performance.

A new method of high-resolution NPP estimation and its implementation in caragana shrublands on the Loess Plateau, China

ABSTRACT Accurate estimation for vegetation net primary production (NPP) at high resolution is essential for ecosystem management and at local to global scales. However, no reliable methods are available to achieve fine-resolution estimation. In this study, a new NPP estimation system was developed by integrating high spatial resolution remote sensing images, tree ring measurements, and process-based model CASA. The system was illustrated by estimating the historical NPP of caragana shrublands from 2011 to 2021 in Dingbian County, China. The results showed that: (1) by employing the biomass model and tree ring measurements, an NPP-crown width model was established to enable a large number of samples for calibrating the CASA model on high spatial resolution images without continuous field measurements. The calibrated parameters, including maximum NDVI, minimum NDVI, and maximum light use efficiency, were determined to be 0.415, 0.022 and 0.025, respectively; (2) according to the estimation by the calibrated CASA model, the NPP in Dingbian County revealed an increasing trend from 2011 to 2021, and the NPP in southern hilly and gully regions was significantly higher than the other regions; (3) among the different indices, max temperature of warmest month had a significant negative effect on the NPP of cagarana shrublands, whereas annual precipitation had a positive effect. In the northern and central regions, air temperature predominantly influenced NPP, while in the southern regions, both temperature and precipitation played a joint role. The implementation in this study illustrated that the NPP estimation system could provide reliable NPP with high resolution and efficient to monitor and detect dominant factors affecting NPP for ecosystem management.

Nanoplastics Detected in Commercial Sea Salt.

People of all ages consume salt every day, but is it really just salt? Plastic nanoparticles [nanoplastics (NPs)] pose an increasing environmental threat and have begun to contaminate everyday salt in consumer goods. Herein, we developed a combined surface enhanced Raman scattering (SERS) and stimulated Raman scattering (SRS) approach that can realize the filtration, enrichment, and detection of NPs in commercial salt. The Au-loaded (50 nm) anodic alumina oxide substrate was used as the SERS substrate to explore the potential types of NP contaminants in salts. SRS was used to conduct imaging and quantify the presence of the NPs. SRS detection was successfully established through standard plastics, and NPs were identified through the match of the hydrocarbon group of the nanoparticles. Simultaneously, the NPs were quantified based on the high spatial resolution and rapid imaging of the SRS imaging platform. NPs in sea salts produced in Asia, Australasia, Europe, and the Atlantic were studied. We estimate that, depending on the location, an average person could be ingesting as many as 6 million NPs per year through the consumption of sea salt alone. The potential health hazards associated with NP ingestion should not be underestimated.

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

Mitochondrial Ca2+-coupled generation of reactive oxygen species causes endothelial dysfunction in Cantú syndrome

Cantú syndrome is a multisystem disorder caused by gain-of-function (GOF) mutations in KCNJ8 and ABCC9, the genes encoding the pore-forming inward rectifier Kir6.1 and regulatory sulfonylurea receptor SUR2B subunits of vascular ATP-sensitive K+ channels (KATP). In this study, we investigated the effects of Cantú syndrome on the vascular endothelium using mutant mice that model the disease. We found that endothelium-dependent dilation was impaired in small mesenteric arteries from Cantú mice. Loss of endothelium-dependent vasodilation led to increased vasoconstriction in response to intraluminal pressure and to the adrenergic receptor agonist phenylephrine. We also found that KATP GOF and acute activation of KATP channels with pinacidil increased the amplitude and frequency of wave-like Ca2+ events that were generated in the endothelium in response to the vasodilator agonist carbachol (CCh). Elevated cytosolic Ca2+ signaling activity in arterial endothelial cells from Cantú mice was associated with high mitochondrial [Ca2+], reactive oxygen species (ROS) generation, and peroxynitrite levels. Scavenging intracellular and mitochondrial ROS restored endothelium-dependent vasodilation in the arteries of mice with KATP GOF mutations. We conclude that mitochondrial Ca2+ overload and ROS generation cause endothelial dysfunction in mice with Cantú syndrome. This study was supported by grants from NHLBI (R35HL155008) and NIGMS (P20GM130459) to S.E., and NIH (R35HL140024) to C.G.N., and NIH (R00HL150277) to C.M. The Transgenic Genotyping and Phenotyping Core and the High Spatial and Temporal Resolution Imaging Core at the COBRE Center for Molecular and Cellular Signaling in the Cardiovascular System, University of Nevada, Reno is maintained by grants from NIH/NIGMS (P20GM130459). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Precise regulation of surface plasmonic hotspots in an Au split nanoring coupled system

The signal enhancement in plasmon-enhanced spectroscopy is primarily contributed by hotspots generated through the surface plasmon resonance effect. Precise control of hotspot positions within nanostructures is of great significance in surface-enhanced Raman spectroscopy (SERS) and high spatial resolution imaging. However, predicting the positions of hotspots is challenging due to the small nanogaps and complex plasmonic interactions. In this study, we theoretically investigate the localized surface plasmonic properties of split ring nanostructure. The effects of variations in the inner radius, gap distance and thickness of the split ring on the hotspot distribution at the nanogaps are systematically analyzed using a three-dimensional finite element method. The results demonstrate that precise control over the positions and quantities of hotspots can be achieved by choosing the excitation wavelength and the gap size of the split rings. The calculated maximum Raman enhancement obtained at the hotspots can reach nine orders of magnitude. Our findings not only contribute to a deeper understanding of the SERS enhancement mechanism, but also provide theoretical insights for further development of surface-enhanced fluorescence, plasmonic sensors, and super-resolution imaging in the field of interface spectroscopy.

Fully dense generative adversarial network for removing artifacts caused by microwave dielectric effect in thermoacoustic imaging

Microwave-induced thermoacoustic (TA) imaging (MTAI) combines pulsed microwave excitation and ultrasound detection to provide high contrast and spatial resolution images through dielectric contrast, which holds great promise for clinical applications. However, artifacts caused by microwave dielectric effect will seriously affect the accuracy of MTAI images that will hinder the clinical translation of MTAI. In this work, we propose a deep learning-based method fully dense generative adversarial network (FD-GAN) for removing artifacts caused by microwave dielectric effect in MTAI. FD-GAN adds the fully dense block to the generative adversarial network (GAN) based on the mutual confrontation between generator and discriminator, which enables it to learn both local and global features related to the removal of artifacts and generate high-quality images. The practical feasibility was tested in simulated, experimental data. The results demonstrate that FD-GAN can effectively remove the artifacts caused by the microwave dielectric effect, and shows superiority in denoising, background suppression, and improvement of image distortion. Our approach is expected to significantly improve the accuracy and quality of MTAI images, thereby enhancing the diagnostic accuracy of this innovative imaging technique.

The AstraLux-TESS high spatial resolution imaging survey. Search for stellar companions of 215 planet candidates from TESS

Chance-aligned sources or blended companions can cause false positives in planetary transit detections or simply bias the determination of the candidate properties. In the era of high-precision space-based photometers, the need for high spatial resolution images has been demonstrated to be critical for validating and confirming transit signals. This already applied to the Kepler mission, is now applicable to the TESS survey, and will be critical for the PLATO mission. In this paper we present the results of the AstraLux-TESS survey, a catalog of high spatial resolution images obtained with the AstraLux instrument at the Calar Alto observatory (Almer\'ia, Spain) in the context of the TESS Follow-up Observing Program. We used the lucky imaging technique to obtain high spatial resolution images from planet candidate hosts included mostly in two relevant regimes: exoplanet candidates belonging to the level one requirement of the TESS mission (planets with radii $R<4 R_ oplus $) and TESS planet candidates around intermediate-mass main-sequence stars. Among the 185 planet host candidate stars observed, we found 13 (7<!PCT!>) to be accompanied by additional sources within a separation of 2.2 arcsec . Among them, six are not associated with sources in the DR3 catalog, thus contaminating the TESS light curve. Even if no contaminants have been detected, we can provide upper limits and probabilities to the possible existence of field contaminants through the sensitivity limits of our images. Among the isolated hosts, we can discard hazardous companions (bright enough to mimic a planetary transit signals) with an accuracy below 1<!PCT!> for all their planets. The results from this catalog are key to the statistical validation of small planets (prime targets of the TESS mission) and planets around intermediate-mass stars in the main sequence. These two populations of planets are difficult to confirm with the radial velocity technique because of the shallow amplitude of small planets and the high rotational velocities and low number of available spectral lines in the intermediate stellar mass regime. Our results also demonstrate the importance of this type of follow-up observation for future transit missions such as PLATO, even in the era.

Refinement analysis of landslide risk assessment for wide area based on UAV-acquired high spatial resolution images

Refinement analysis of landslide risk assessment for wide area based on UAV-acquired high spatial resolution images

Hyperspectral Image Sharpening Using Fusion Techniques -A Case Study at Salah Al-Din Province/Iraq-

The data fusion process includes merging two or more pieces of information obtained from different sensors. Satellite image fusion research aims to create a new image by combining two images captured by different sensors using various methodologies. In this research, image sharpening tools were used to combine a hyperspectral image with a low spatial resolution captured by a Hyperion sensor mounted on the Earth Observation 1 (EO-1) satellite with a grayscale high spatial resolution image captured by Enhanced Thematic Mapper Plus (ETM +) sensor mounted on Landsat-8 (resampling first one to ensure equal spatial resolution of both images). In addition, three techniques were adopted for implementing the Fusion mechanism: the Principal Component Analysis PCA, the Nearest Neighbor Diffusion NNDifuse, and the Gram-Schmidt method; these were used to sharpen hyperspectral data using high spatial resolution. The result showed that the Gram-Schmidt method could give Hyperspectral images with higher spectral and spatial resolution in panchromatic image data more accurately than the other methods.

Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead University Agriculture Research Station, Thunder Bay, Canada

Remote sensing technology currently facilitates the monitoring of crop development, enabling detailed analysis and monitoring throughout the crop’s growing stages. This research analyzed the winter wheat growth dynamics of experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada using high spatial and temporal resolution remote sensing images. The spectral signatures for five growing stages were prepared. NIR reflectance increased during the growing stages and decreased at the senescence, indicating healthy vegetation. The space–time cube provided valuable insight into how canopy height changed over time. The effect of nitrogen treatments on wheat did not directly influence the plant count (spring/autumn), and height and volume at maturity. However, the green and dry weights were different at several treatments. Winter wheat yield was predicted using the XGBoost algorithm, and moderate results were obtained. The study explored different techniques for analyzing winter wheat growth dynamics and identified their usefulness in smart agriculture.

Efficient Representation Learning of Satellite Image Time Series and Their Fusion for Spatiotemporal Applications

Satellite data bolstered by their increasing accessibility is leading to many endeavors of automated monitoring of the earth's surface for various applications. Such applications demand high spatial resolution images at a temporal resolution of a few days which entails the challenge of processing a huge volume of image time series data. To overcome this computing bottleneck, we present PatchNet, a bespoke adaptation of beam search and attention mechanism. PatchNet is an automated patch selection neural network that requires only a partial spatial traversal of an image time series and yet achieves impressive results. Satellite systems face a trade-off between spatial and temporal resolutions due to budget/technical constraints e.g., Landsat-8/9 or Sentinel-2 have high spatial resolution whereas, MODIS has high temporal resolution. To deal with the limitation of coarse temporal resolution, we propose FuSITSNet, a twofold feature-based generic fusion model with multimodal learning in a contrastive setting. It produces a learned representation after fusion of two satellite image time series leveraging finer spatial resolution of Landsat and finer temporal resolution of MODIS. The patch alignment module of FuSITSNet aligns the PatchNet processed patches of Landsat-8 with the corresponding MODIS regions to incorporate its finer resolution temporal features. The untraversed patches are handled by the cross-modality attention which highlights additional hot spot features from the two modalities. We conduct extensive experiments on more than 2000 counties of US for crop yield, snow cover, and solar energy prediction and show that even one-fourth spatial processing of image time series produces state-of-the-art results. FuSITSNet outperforms the predictions of single modality and data obtained using existing generative fusion models and allows for monitoring of dynamic phenomena using freely accessible images, thereby unlocking new opportunities.

Imaging Mass Spectrometry of Isotopically Resolved Intact Proteins on a Trapped Ion-Mobility Quadrupole Time-of-Flight Mass Spectrometer.

In this work, we demonstrate rapid, high spatial, and high spectral resolution imaging of intact proteins by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) on a hybrid quadrupole-reflectron time-of-flight (qTOF) mass spectrometer equipped with trapped ion mobility spectrometry (TIMS). Historically, untargeted MALDI IMS of proteins has been performed on TOF mass spectrometers. While advances in TOF instrumentation have enabled rapid, high spatial resolution IMS of intact proteins, TOF mass spectrometers generate relatively low-resolution mass spectra with limited mass accuracy. Conversely, the implementation of MALDI sources on high-resolving power Fourier transform (FT) mass spectrometers has allowed IMS experiments to be conducted with high spectral resolution with the caveat of increasingly long data acquisition times. As illustrated here, qTOF mass spectrometers enable protein imaging with the combined advantages of TOF and FT mass spectrometers. Protein isotope distributions were resolved for both a protein standard mixture and proteins detected from a whole-body mouse pup tissue section. Rapid (∼10 pixels/s) 10 μm lateral spatial resolution IMS was performed on a rat brain tissue section while maintaining isotopic spectral resolution. Lastly, proof-of-concept MALDI-TIMS data was acquired from a protein mixture to demonstrate the ability to differentiate charge states by ion mobility. These experiments highlight the advantages of qTOF and timsTOF platforms for resolving and interpreting complex protein spectra generated from tissue by IMS.

Abstract 2486: Dissecting tumor-immune interaction in response and resistance to immune checkpoint blockade in metastatic melanoma

Abstract Background: Immune checkpoint blockade (ICB) has revolutionized the treatment and prognosis of various cancers, including melanoma. Nevertheless, intrinsic or acquired resistance is common, and the interactions between tumor-intrinsic features and the microenvironment underlying these resistances remain unclear for most patients. Methods: In this study, we developed a framework to analyze response and resistance, both intrinsic and acquired, via tumor-intrinsic programs, immune features, and tumor-stromal-immune interactions in a standardized, uniformly processed, and deeply clinically annotated cohort of metastatic melanoma patients (n=61) treated with ICB as part of the NCI funded human tumor atlas network (HTAN) initiative. From the same tumor samples (pre-treatment (n=33) and post-progression (n=28)), we conducted bulk whole-exome sequencing (WES), single-nucleus RNA sequencing, and for a subset of the samples high-resolution spatial imaging (including protein mIHC, CODEX, and transcriptomics MERFISH). Standardized processing and data pipelines allowed for integration of genomic, transcriptomic, and spatial features to elucidate shared tumor and microenvironmental states and their relationships with resistance. Results: Our preliminary analysis on a subset of 28 samples identified an enrichment of B cells, plasma cells, and T follicular helper-like cells in the tumor microenvironment (TME) of patients sensitive to treatment. The TME of non-responders was predominantly characterized by macrophage/monocyte cell populations. Post-treatment responder samples showed an enrichment of CD4+ T cells. Genomic heterogeneity, assessed through WES, was confirmed as a feature of intrinsic resistance across different ICB treatment settings, while ploidy was associated with treatment response in pre-treatment, ipilimumab-naïve patients treated with PD-1 inhibitors. Comparing the immune compartments of samples with high and low heterogeneity revealed NK cell enrichment in samples with a high proportion of subclonal mutations. Conclusions and Future Directions: Overall, our work provides a high-resolution understanding of the tumor-stromal-immune interaction in metastatic melanoma, shedding light on the factors contributing to therapy response and resistance, and contributing a repository of data from deeply clinically annotated, multimodally characterized and uniformly processed patient samples. Ongoing spatial analysis will further enhance our understanding of these compartments at a spatial level. The results of our study have the potential to guide more personalized and effective treatment strategies for metastatic melanoma in the future. Citation Format: Giuseppe Tarantino, Yiwen He, Priyanka Solanky, Aaron Thorner, Tyler Aprati, Ryan Sullivan, Emily Robitschek, Timothy Blosser, Xiaowei Zhuang, Xiaowei Zhuang, Elliot Boblitt, Allison Frangieh, Marta Holovatska, Aleigha Lawless, Michael Manos, Kathleen Pfaff, Karla Helvie, Tatyana Sharova, Dennie Frederick, James Liam Fahey, Diego Villamarin, Sami Farhi, Scott Rodig, Bruce Johnson, Alex K. Shalek, Eliezer Van Allen, Stephen Hodi, Genevieve M. Boland, David Liu. Dissecting tumor-immune interaction in response and resistance to immune checkpoint blockade in metastatic melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2486.

Image Quality Assessment of Multi-Satellite Pan-Sharpening Approach: A Case Study using Sentinel-2 Synthetic Panchromatic Image and Landsat-8

INTRODUCTION: The satellite's physical and technical capabilities limit high spectral and spatial resolution image acquisition. In Remote Sensing (RS), when high spatial and spectral resolution data is essential for specific Geographic Information System (GIS) applications, Pan Sharpening (PanS) becomes imperative in obtaining such data. 
 OBJECTIVES: Study aims to enhance the spatial resolution of the multispectral Landsat-8 (L8) images using a synthetic panchromatic band generated by averaging four fine-resolution bands in the Sentinel-2 (S2) images. 
 METHODS: Evaluation of the proposed multi-satellite PanS approach, three different PanS techniques, Smoothed Filter Intensity Modulation (SFIM), Gram-Schmidt (GS), and High Pass Filter Additive (HPFA) are used for two different study areas. The techniques' effectiveness was evaluated using well-known Image Quality Assessment Metrics (IQAM) such as Root Mean Square Error (RMSE), Correlation Coefficient (CC), Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), and Relative Average Spectral Error (RASE). This study leveraged the GEE platform for datasets and implementation.
 RESULTS: The promising values were provided by the GS technique, followed by the SFIM technique, whereas the HPFA technique produced the lowest quantitative result. 
 CONCLUSION: In this study, the spectral bands of the MS image’s performance show apparent variation with respect to that of the different PanS techniques used.

ІНФОРМАЦІЙНА ТЕХНОЛОГІЯ ПОПЕРЕДНЬОЇ ОБРОБКИ СУПУТНИКОВИХ ЗОБРАЖЕНЬ З ВИКОРИСТАННЯМ ЗГОРТКОВОЇ НЕЙРОННОЇ МЕРЕЖІ

With urbanization and the growing interest in geographic information systems, preci-sion mapping, and environmental monitoring, there is a need to improve methods of fusing high-resolution digital satellite images to obtain more detailed and informative data. Satellite images with high spatial resolution hold significant promise for addressing a variety of is-sues. However their utility is frequently constrained by limited contrast dynamics and re-stricted spectral data. Pansharpening methods are effective means of increasing the spatial resolution of multichannel images using panchromatic ones. Although there are a significant number of proposed pansharpening algorithms, none can be universal for all types of sensors and demonstrate different results. The paper reviews the fusion methods of high spatial reso-lution satellite images and proposes an information technology for preprocessing digital sat-ellite images using a convolutional neural network. It made it possible to effectively extract texture details and spatial dependencies for local and non-local information. The paper com-pares the results of the proposed technology with existing pan-sharpening methods, such as Gram-Shmidt, HSV, Wav+HSV, ATWT, AWLP, BDSD, Brovey, and HPF. The results of test-ing conducted on eight-channel images acquired by the WorldView-2 satellite confirm that the proposed technology (ERGAS=0.32, RMSE=0.28b SSIM=0.71, PSNR=37.60) allows for achieving high spectral and spatial quality of multichannel images and outperforms existing methods.