When separation to the parameters of interest appears below resolution limit of the estimator, the ambiguity arises whether two parameter estimates relate to one source emitted twice or two close sources emitted once. In the paper, novel Bayes technique aimed to identify one/two sources below resolution limit by the pair of Gaussian estimates of a source(s) planar location as parameter is developed. Prior probabilities of the hypotheses on one/two sources are available from the analysis of physical characteristics of the emissions, assuming that they can be equally probable. The identifier recalculates a posteriori these probabilities subject to a distance between sources. It is applied to distinguish two location estimates obtained in the planar time difference of arrival mobile communication network. The work of the identifier is studied in the domain of closely spaced network users where covariance matrices of estimates are sufficiently approximated by the constant. The application gives an example of how the identifier revises the prior probabilities and can change thereby the initial preference of a hypothesis with a distance between users in some cases.

Seeing atomic configuration of single organic nanoparticles at sub-Å spatial resolution by transmission electron microscopy has been so far prevented by the high sensitivity of soft matter to radiation damage. The difficulty is related to the need to irradiate the particle with a total dose of few electrons/Å2, not compatible with the electron beam density necessary to search the low-contrast nanoparticle, to control its drift, to finely adjust the electro-optical conditions and particle orientation, and to finally acquire an effective atomic resolution image. On the other hand, the capability to study individual pristine nanoparticles, such as proteins, active pharmaceutical ingredients, and polymers, with peculiar sensitivity to the variation of the local structure, defects, and strain, would provide advancements in many fields, including materials science, medicine, biology, and pharmacology. Here we report direct sub-ångström resolution imaging at room temperature of pristine unstained crystalline polymer-based nanoparticles. This result is obtained by combining low-dose in-line electron holography and phase-contrast imaging on state-of-the-art equipment, providing an effective tool for the quantitative sub-angstrom imaging of soft matter.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

Axial resolution is one of the most important characteristics of a microscope. In all microscopes, a high axial resolution is desired in order to discriminate information efficiently along the longitudinal direction. However, when studying thick samples that do not contain laterally overlapping information, a low axial resolution is desirable as information from multiple planes can be recorded simultaneously from a single camera shot instead of plane-by-plane mechanical refocusing. In this study, we increased the focal depth of an infrared microscope non-invasively by introducing a binary axicon fabricated on barium fluoride substrate close to the sample. Preliminary results of imaging thick and sparse silk fibers showed an improved focal depth with a slight decrease in lateral resolution and increase in background noise.

DNA barcoding has emerged as a transformative tool in the realm of biodiversity assessment, facilitating rapid and precise species identification. This review article delves into the evolving landscape of DNA barcoding, with a particular focus on the emergence of mini-barcodes and alternative genes as innovative approaches. Traditional DNA barcoding, anchored in the COI gene, has significantly advanced our understanding of species diversity; however, inherent challenges persist, especially when dealing with degraded samples or taxonomic groups where COI falls short. The introduction of mini-barcodes, characterized by their reduced sequence length, has revolutionized DNA barcoding by expanding its applicability. We explore the utility of mini-barcodes in various scenarios, ranging from DNA extraction from ancient or fragmented specimens to in-field identification using portable sequencing technologies. Case studies and practical insights into their effectiveness are presented, emphasizing their role in overcoming traditional limitations. Alternative genes, a complementary facet of DNA barcoding, offer a novel avenue for enhancing taxonomic resolution and accuracy. This review showcases instances where researchers have harnessed alternative genes to overcome COI's constraints, particularly in diverse taxonomic groups, such as fungi, plants, and microorganisms. The selection of these genes is discussed, along with their potential to uncover hidden biodiversity and improve species delimitation. Examining the broader implications of mini-barcodes and alternative genes, we unveil their pivotal roles in advancing conservation efforts, forensic science, and citizen science initiatives. The adaptable nature of these approaches empowers a diverse range of stakeholders, from scientists conducting critical research to concerned citizens contributing to our collective understanding of the natural world. Looking ahead, we envision an exciting future for DNA barcoding, as these innovations continue to evolve. Integration with cutting-edge sequencing technologies, data analysis methods, and the harnessing of big data hold immense potential. Nonetheless, challenges related to standardization, scalability, and data sharing persist.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows to estimate direct, diffuse and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterisation techniques and allows the generation of high quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data and its resolution if two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions. This IRB approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. We used a General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) to acquire two DTI sequences of the left knee on each child at 3T: an in-plane 2.0 x 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel, neither had an inter-section gap. We used a multi-band DTI acquisi-tion with fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 sec/mm2). The MR vendor-provided a commercially available DL model applied with 75% noise reduction settings to same subject DTI sequences at different spatial res-olutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for femur and tibia, at each spatial resolution. Differences were evaluated using Wilcox-on-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffu-sion metrics in femur and tibia using the 2 mm x 2mm x 3 mm voxel dimension there were no significant differences between tract count (p = 0.1, p =0.14) tract volume (p = 0.14, p = 0.29), or tibial tract length (p=0.16); femur tract length exhibited a significant difference (p

Artistic image completion is fundamental for the preservation and restoration of invaluable art paintings and it has experienced significant progress through the implementation of deep learning methodologies. Despite these advancements, challenges persist, even with sophisticated approaches like Generative Adversarial Networks (GANs), particularly in achieving optimal results for high-resolution paintings. Small-scale texture synthesis and the inference of missing information from distant contexts present persistent issues, leading to distortions in lines and unnatural colors, especially in art paintings with complicated structures and textures. Concurrently, patch-based image synthesis has evolved by incorporating global optimization on the image pyramid to enhance structural coherence and details. However, methods relying on gradient-based synthesis encounter obstacles related to directionality, inconsistency, and the heavy computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a groundbreaking approach, integrating Weighted Similarity-Confidence Laplacian Synthesis, to comprehensively address these challenges and advance the field of artistic image completion. This proposal addresses challenges not only in high-resolution artistic image completion but also makes a significant contribution to the broader field of patch-based synthesis by utilizing the Laplacian pyramid for enhanced edge-aware correspondence search. Experimental result confirms the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with complicated details and irregular missing regions.

The water resources policy under climate change impacts water security, which should ensure enough water of good quality for humans and nature. Integrated water management is the state-of-the-art policy support model that, after 20 years in use, failed to achieve in many countries its primary goal of water bodies' good ecological status. Here, we show that this is due to the substantial externalities the anthropocentric character of the model generates. From the historical analysis, three main results are obtained: 1) the nature-human interplay is always composed of the coexistence of two opposite behaviors of conflict and cooperation; 2) this contradiction is a dialectical, general ontological attribute; 3) on the balance of power between nature and humans, three clusters are identified: i) naturalistic; ii) dualistic; iii) anthropocentric. A novel behaviorist dialectical conflict resolution model can resolve the differences between stakeholders and natural laws. The harmonic symbiosis of humans with nature removes environmental externalities and can lead to sustainable water security. Three case studies from the literature illustrate the merits of the new dialectical model.

(1) Background: There is a risk of Aflatoxin B1(AFB1) pollution in yellow rice wine[1],threatening human life and health, the risk is a major focus of government attention and social concern.The current detection technology cannot meet the needs of large-scale testing due to the disadvantages of high false positive, cumbersome operating procedures or high cost.Therefore, it is urgent to study and establish a high-sensitivity and high-fast detection method for AFB1 to provide technical support for yellow rice wine quality supervision and risk assessment.; (2) Methods: In this study, three-factor and three-level orthogonal experiments were carried out for oscillation time, reaction temperature and tomography time to determine the best reaction conditions for pretreatment.AFB1 detection was carried out by aflatoxin time-resolved fluorescence immunochromatography technology，Through the ratio of the T signal value of the detection line to the C signal value of the quality control line and the natural logarithmic value of the standard solution concentration[2], a rapid detection technology of time-resolution fluorescence immunochromatography is established.The technology was methodically assessed, the actual yellow rice wine samples were tested, and the results were compared with the HPLC method; (3) Results: The results show that the optimal reaction conditions for pretreatment are: the sample oscillation time is 20min, the sample reaction temperature is 37°C, and the sample chromatography time is 6min. In the actual sample detection of yellow rice wine, The time-resolved fluorescence immunochromatography detection technology established in this study detects AFB1. The detection limit is 0.3 μg·kg-1,The linear range is 0.8-12.0μg·kg-1,The linear equation of the corresponding standard curve y=-0.1913x+0.5206 (R2=0.9948),The standard deviation is 0.029.The results of the in-batch accuracy and precision of this method show that the addition and recovery rate is 77.9% - 105.7%,The coefficient of variation is in 4.5% - 10.3%; Inter-batch accuracy and precision test results show that the addition recovery rate is75.9% - 85.8%,the coefficient of variation is in 8.1% - 13.9%,explain that the detection technology has good accuracy and precision.Compared with HPLC, the relative error is less than 10%,It shows that aflatoxin time-resolution fluorescence immunochromatography technology is in good agreement with the test results of national standard methods; (4) Conclusions:he rapid detection technology of time-resolution fluorescence immunochromatography of AFB1 in yellow rice wine is easy to operate with fast detection speed, high sensitivity, good repeatability and stability, and has broad application prospects.

Claims arising frequently in the Architecture, Engineering and Construction (AEC) industry usually result in disputes, leading to all sorts of negative implications for a project. Claims need to be managed efficiently, and in case they evolve into disputes, they need to be resolved as soon as possible so that the construction process can resume. In this paper, a bibliometric review is carried out to explore the existing literature regarding construction claims and dispute resolution methods employed in construction projects. Using VOSviewer and the Scopus database, relevant literature is retrieved and analyzed using keyword searches, including "construction claim” and "construction management". The detected research themes provide future researchers with potential research directions. A gap in the research regarding the emergence of terms such as “blockchain”, “smart contracts”, and "building information modelling (BIM)" appearing only recently after 2020 in the literature regarding the construction claims and disputes research area was determined. Therefore, a content analysis of the most recent publications employing these and other novel technologies is conveyed. Ultimately, the main research trends and potential research directions for assisting researchers and construction professionals in their efforts to address construction claims and disputes in sustainable and efficient ways are discussed.

Diabetic kidney disease (DKD) is a major cause of morbidity and mortality of type 2 diabetes mellitus (T2DM). Aim of this study was to investigate whether albumin structural alterations correlate with DKD severity and evaluate whether native and reduced albumin concentrations could complement the diagnosis of DKD. To this aim, one hundred and seventeen T2DM patients without (n=42) and with (n=75) DKD (DKD I-III upon KDIGO classification) were evaluated; total albumin concentration (tHA) was quantified by bromocresol green assay, while structural alterations were profiled assessed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). The concentrations of albumin in native (eHA, effective albumin) and reduced (rHA) forms were then assessed. HRMS analyses showed a reduced relative amount of native albumin in DKD patients along with an increased abundance of altered forms, especially those bearing oxidative modifications. Accordingly, both eHA and rHA values varied along the stages of progressive renal failure, and alterations dose-dependently correlated with renal dysfunction. ROC curves analysis showed a significantly higher sensitivity and specificity of eHA and rHA with respect to tHA for the diagnosis of DKD. Importantly, at multivariable logistic regression analysis, eHA was identified as independent predictor of DKD.

In this study, the distribution patterns of southern horse mackerel are examined using commercial fine-scale resolution data. Using landings by size category and VMS data from the Portuguese commercial bottom trawl fishery, which consistently targets horse mackerel, this study provides a comprehensive analysis of horse mackerel age distributions spanning a decade (2010-2020). Importantly, the study addresses potential biases in commercial effort data and establishes the usefulness of commercial bottom-trawl gear as a suitable method for sampling and evaluate southern horse mackerel stock dynamics. Ordered regression models were applied to allow for the modelling of the distribution of multiple age categories and investigate spatio-temporal migrations off the Portuguese coast. Southern horse mackerel shows a widespread age distribution range and stable abundance with indications of seasonal and spatial patterns in the distribution of specific age groups. The insights derived from this research contribute valuable knowledge for understanding the dynamics and distribution patterns of fish populations.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.

We are introducing a highly efficient 48-channel ultrasound beamforming system ideal for ultrasound endoscopy applications. The system includes a transmitter and a receiver, which allow for low-area, high-resolution imaging acquisition. The transmitter uses a charge redistribution HV (high voltage) scheme to generate three-level pulses that actuate the transducer, implemented with the standard CMOS process for optimal cost and power savings. Meanwhile, the receiver features a sub-array structure and a delay generator that reduces area usage. To achieve high-resolution ultrasound imaging acquisition with low computational power, we developed the Shift Coherence Factor (SCF) algorithm, which is hardware-friendly. This approach delivers a lateral resolution over 20% better than the conventional delay and sum (DAS) algorithm, with a contrast ratio of over 30 dB. The system was implemented in a 180 nm standard CMOS process with an area of 24.98 mm2, power consumption of 8.23 mW per channel, achieving a delay resolution of 8.33 ns, and a low-area implementation of 0.52 mm2 per channel. The system offers high-quality imaging acquisition with minimal additional area and power consumption, which has great potential for 3D imaging or catheterized ultrasound systems.

This academic article examines the effectiveness of international humanitarian intervention in conflict zones. Humanitarian intervention refers to the use of military, diplomatic, or economic measures by external actors to protect civilians, promote human rights, and alleviate suffering in areas affected by armed conflict. The research employs a comprehensive approach, incorporating content analysis, case study, and historical analysis in its methodology. It explores the key challenges faced by international actors in implementing humanitarian interventions and evaluates their impact on conflict resolution and post-conflict reconstruction. Through this thorough analysis of case studies and scholarly literature, this article contributes to the ongoing debate surrounding the efficacy of international humanitarian intervention.

The mitochondrial network (MN) is a dynamic structure undergoing constant remodeling in the cell. It is assumed that the violation of MN may be associated with various pathologies, including Parkinson’s disease (PD). Using automatic image analysis and super-resolution microscopy, we have assessed the MN parameters in fibroblasts from patients with established hereditary PD mutations (associated with PINK1, LLRK2, α-synuclein, PINK1 and Parkin simultaneously proteins) under normal conditions and after hydrogen peroxide-induced stress. Fibroblasts with Pink1/Parkin mutation are the most different in morphology from fibroblasts obtained from conditionally healthy donors: MN is larger, contains longer mitochondria and accumulated individual mitochondria. In addition to MN, we evaluated other cellular parameters, such as: cytosolic and mitochondrial ROS production and mitochondrial membrane potential. It has been shown that mitochondria of fibroblasts with mutations in genes encoding PINK1, α-synuclein and Pink/Parkin tends to hyperpolarization and cytosolic ROS overproduction, while mitochondrial ROS production was higher only in fibroblasts with PINK1 and α-synuclein mutation.

Due to the lack of theoretical research on the amount of spatio-temporal information in the high-speed photography technologies, obtaining an optimized system with a best amount of spatio-temporal information remains a challenge, resulting in insufficient effective information and observation accuracy for ultrafast events. The paper presents an ultrafast raster imaging (URI) system with a large amount of spatio-temporal information based on the all-optical raster principle in single-shot. Specifically, we derive the optimal equation of spatial resolution and the expression for the maximum amount of spatio-temporal information that can achieve excellent performance for an URI system. It serves as a general guideline for getting a large amount of information design in the URI system. Compared to the existing URI systems, the advanced URI system exhibits an improvement of nearly 1 order of magnitude in the amount of spatio-temporal information, and more than twofold in spatial resolution. It shows a great potential for capturing intricate and non-repetitive ultrafast events on the femtosecond time scale.

For two-dimensional forward-looking sonar imaging, high sidelobes significantly degrade the quality of sonar images. The cosine window function weighting method is often applied to suppress the sidelobe levels in the angular and range dimensions at the expense of the main lobe resolutions. Therefore, the improved spatially variant apodization imaging method for forward-looking sonar is proposed to reduce the sidelobes without degrading the main lobe resolution in angular-range dimensions. The proposed method is a nonlinear post-processing operation in which the raw complex-valued sonar image produced by a conventional beamformer and matched filter is weighted by a spatially variant coefficient. To enhance the robustness of the spatially variant apodization approach, the array magnitude and phase errors are calibrated to prevent beam sidelobe heightening from occurring prior to beamforming operations. The analyzed results of numerical simulations and a lake experiment demonstrate that the proposed method can greatly reduce the sidelobes to approximately -40 dB, while the main lobe width remains unchanged. Moreover, this method has an extremely simple computational process.

Trio-based exome sequencing and high-resolution HLA typing were employed to analyze three patients with autoimmune adrenal insufficiency (AAI), including those with autoimmune polyglandular syndrome (APS) type 2, as well as their parents. Benign or likely benign variants of the AIRE gene were identified in all participants of the study. These variants, coupled with clinical data and the results of antibody studies to type I interferons, helped to exclude APS-1. Patients with APS-2, in contrast to patient with AAI, inherited distinct variants of unknown significance in the CLEC16A gene, which is associated with autoimmune diseases including AAI. Various risk alleles in other genes associated with autoimmunity were identified in all patients. HLA typing of second class loci revealed alleles related to APS types 2, 3, and 4. Nevertheless, the frequencies of the haplotypes identified are substantial in the healthy Russian population, precluding from regarding these alleles as genetic determinants linked to APS development. Immunological examination can detect antibody carriers and predict the risk of autoimmune disease development. In the future, to identify genetic predictors of autoimmune endocrinopathies, it is recommended to analyze the whole genome of patients and their relatives, examining clinically relevant variants in non-coding regions.

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1-5 min) but low spatial resolution (2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with Deep Learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

The work presented in this paper represents a preliminary study on the performance of the new Silicon tracker layer, the Layer 0 (L0), that will be installed on top of the Alpha Magnetic Spectrometer (AMS-02), at the end of 2024. AMS-02 is a cosmic rays (CR) detector that has been operating on the International Space Stations (ISS) since May 2011. Thanks to its 9-layer Silicon tracker, this apparatus can perform high energy CR measurements with an unprecedented level of statistics and precision. However, high Z (Z $\ge 15$) CR nuclei statistic is strongly affected by fragmentation along the detector: with the installation of the new Silicon layer will be possible to achieve new unique high energy (TeV region) measures for those nuclei along with increased statistics for all nuclei up to Zinc. To achieve this, a Silicon ladder prototype, that will be part of the final Silicon layer, has been exposed to an ions test beam at the super-proton-synchrotron (SPS) of the CERN to characterize its charge resolution and the readout electronics. Preliminary results have shown a charge resolution of 10 $\%$ for nuclei up to Z=7.

Multispectral image (MSI) and hyperspectral image (HSI) fusion (MHIF) aims to address the challenge of acquiring high-resolution (HR) HSI images. This field combines a low-resolution (LR) HSI with an HR-MSI to reconstruct HR-HSI. Existing methods directly utilize transformers to perform feature extraction and fusion. Despite the demonstrated success, there exist two limitations: 1) Employing the entire transformer model for feature extraction and fusion fails to fully harness the transformer’s potential in integrating the spectral of the HSI and spatial information of the MSI. 2) HSI has a strong spectral correlation and exhibits sparsity in the spatial domain. Existing transformer-based models do not optimize this physical property, which makes their methods prone to spectral distortion. To accomplish these issues, this paper introduces a novel framework for MHIF called Sparse Mix-Attention Transformer (SAMformer). Specifically, to fully harness the advantages of the Transformer architecture, we propose a Spectral Mix Attention Block (SMAB), which concatenates the keys and values extracted from LR-HSI and HR-MSI to create a new multi-head attention module. This design facilitates the extraction of detailed long-range information across spatial and spectral dimensions. Besides, to address the spatial sparsity inherent in HSI, we incorporated a sparse mechanism within the core of SMAB called Sparse Spectral Mix Attention Block (SSMAB). In the SSMAB, we compute attention maps from queries and keys and select the K highly correlated values as the sparse attention map. This approach enables us to achieve a sparse representation of spatial information while eliminating spatially disruptive noise. Extensive experiments conducted on three benchmark datasets, namely Cave, Harvard, and Pavia Center, demonstrate the SMAformer method outperforms state-of-the-art methods.

Single Image Super Resolution (SSIR) is a problem in computer vision where the goal is 1 to create high-resolution images from low-resolution ones. It has important applications in fields 2 such as medical imaging and security surveillance. While traditional methods such as interpolation 3 and reconstruction-based models have been used in the past, deep learning techniques have recently 4 gained attention due to their superior performance and computational efficiency. This article proposes 5 an Autoencoder based Deep Learning Model for SSIR, in particular, a light model that uses fewer 6 parameters without compromising performance. The down-sampling part of the Autoencoder 7 mainly uses 3 by 3 convolution and has no subsampling layers. The up-sampling part uses transpose 8 convolution and residual connections from the down sampling part. The model is trained using a 9 subset of the VILRC ImageNet database. The model is evaluated using quantitative metrics PSNR, 10 SSIM as well as qualitative measures such as perceptual quality. PSNR and SSIM figures as high as 11 76.06 and 0.93 are reported.

Super-resolution technology is important not only in bio-related fields but also in nanotechnology, particularly in the semiconductor industry, where fine patterning is required and super-resolution is essential. However, observing microstructures beyond the diffraction limit proposed by Abbe and Rayleigh is considered impossible because of diffraction in traditional optical microscopy ob-servation techniques. However, in recent years, it has been possible to observe microstructures beyond the Rayleigh criterion by analysing the phase distribution of light. This study investigated the physical reasons why phase analysis makes this new analysis technique possible using simula-tions. The results confirmed that the phase component of the zero-order diffracted light reflected from the microstructure and able to pass through the lens system contained the phase information related to the shape of the measured object. Analysis of this information demonstrates the possi-bility of realising super-resolution based on speckle interferometry.

The semantic segmentation of high-resolution remote sensing images (HR-RSI) is crucial for a wide range of applications, such as precision agriculture, urban planning, natural resource assessment, and ecological monitoring. However, accurately classifying pixels in HR-RSI faces challenges due to densely distributed small objects and scale variations. Existing techniques, including Convolutional Neural Networks (CNNs) and other methods for hierarchical feature extraction and fusion of remote sensing image, often do not achieve the desired accuracy. In this paper, we propose a novel approach called the Hierarchical Rich-scale Fusion Network (HRFNet) to address these challenges. HRFNet utilizes advanced information rating and image partition techniques to extract rich-scale features within image layers. This allows for the adaptive exploration of both local and global contextual information. Moreover, we introduce a structured intra-layer to inter-layer feature aggregation module, which enables the adaptive extraction of fine-grained details and high-level semantic information from multi-layer feature maps in a highly flexible manner. Extensive experimentation has been conducted to validate the effectiveness of our proposed method. Our results demonstrate that HRFNet outperforms existing techniques, achieving state-of-the-art (SOTA) results on benchmark datasets, specifically the ISPRS Potsdam and Vaihingen datasets.

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño-Southern Oscillation (ENSO) is considered a significant signal of global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, which month the low-resolution sea surface temperature data is from. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.

Omega-3 polyunsaturated fatty acids (PUFA) play a pivotal role in health, wellbeing, and inflammatory diseases in humans. Lack of omega-3 consumption is associated with diseases. Specialized Pro-resolving Mediators (SPM) are produced from Omega-3-PUFAs and accelerate resolution of inflammation. They are classified as resolvins, maresins, protectins, and lipoxins. SPMs actions in resolution consist in limiting neutrophilic infiltration, promote the clearance of apoptotic cell and cellular debris, enhancing efferocytosis and phagocytosis, counter regulate the production of pro-inflammatory mediators such as chemokines and cytokines, and promote pro-resolving macrophage phenotype. We identified temporal plasma and serum increases of SPMs after 6 g of oral supplementation of SPM-enriched emulsion in ten healthy subjects. We also described temporal increases of omega-3, as well as pharmacokinetics fundamentals in plasma and serum using LC-MS/MS lipid profiling. These results provide evidence of temporal increases of SPMs that together support the idea that given oral supplementation is a good source of essential bioactive SPMs.

Sustainable food systems imply the re-use of biowastes and water. In this paper we characterize 30 top soil improvers of anthropogenic, animal and green waste origin, and 11 irrigation waters from rivers, channels, and civil wastewater treatment plants (cWWTPs) for the presence of antimicrobials. Liquid chromatography coupled to hybrid High Resolution Mass Spectrometry (LC-HRMS/MS) was applied to determine 50 different drugs belonging to the classes of sulfonamides (11), tetracyclines (7), fluoroquinolones (10), macrolides (12), amphenicols (3), pleuromutilins (2), diaminopyrimidines (1), rifamycines (1) and lincosamides (1). Biowastes from cWWTPs and animal manure, slurry and litter revealed the highest loads for sulfonamides, tetracyclines, fluoroquinolones, and macrolides (80, 470, 885, and 4,487 ng/g wet weight, respectively) with nor- and cipro-floxacin as marker of anthropogenic sources. In composts and digestates antimicrobials were almost below the determination limits. Re-used waters for irrigation in open field lettuce production resulted contaminated in the range of 12-221 ng/L for sulfonamides, tetracyclines, and fluoroquinolones, against very few detects in channels and surface waters. Antimicrobials Hazard Index (HI) based on Predicted No Effect Concentration for Antimicrobial Resistance (PNEC_AMR) was largely >100 in contaminated top soil improvers from urban and animal sources. Accounting for worst-case inputs from top soil improvers and irrigation water and dilution factors of amended soil, fluoroquinolones only showed a HI around 1 in open fields for lettuce production. The origin of top soil improvers plays a pivotal role for safe and sustainable leafy vegetable productions, to abate the risk of AMR onset in food-borne diseases, as well the transfer of AMR elements to human gut flora.

Trichomonas vaginalis is an extracellular parasite protozoan that causes human trichomoniasis, a sexually transmitted infection (STI) that affects approximately 200 million people worldwide. The presence of T. vaginalis adhesion to inert substrates has been described in several reports. Still, very few studies on cluster formation and more detailed analyses of the contact regions between the membranes of the parasites in these aggregate formations have not been carried out. We analyzed the formation of parasite monolayers and clusters using high-resolution scanning electron microscopy, transmission electron microscopy, cytochemistry, TEM tomography, and dye injection. Here, we show the formation of a monolayer of tightly adherent cells, epithelium-like, when the parasites are in contact with an inert material. Based on this observation, we analyzed whether this monolayer behaves as an epithelium, analyzing cell junctions, cell communication, and ultrastructural aspects. We also analyzed the cluster formation and the morphological characteristics of parasite aggregation observed in the culture supernatant. We report that monolayer formation differs from cluster formation in many aspects. The monolayers form strong adhesion, whereas the clusters have fragile attachments. There is no fusion or passage of molecules between neighbor-attached cells; there is no need for different strains to form filopodia, cytonemes, and extracellular vesicles during cluster formation.

This paper describes the optimal design of a miniature fiber-optic linear displacement sensor. It is characterized by its ability to measure displacements along a millimetric range with sub-micrometric resolution. The sensor consists of a triangular reflective grating and two fiber-optic probes. The measurement principle of the sensor is presented. The design of the sensor's triangular grating has been geometrically optimized by considering the step angle of the grating to enhance the sensor's resolution. The optimization method revealed a global optimum at which the highest resolution is obtained

Abstract：Objective: To compare the effects of fermentation on the chemical composition of Gastrodia Tuder Halimasch Powder, and to provide a basis for the processing, processing and clinical application of this medicinal material. Methods: UHPLC-Q-Orbitrap HRMS was used to identify compound structure by comparing excimer ions, characteristic fragment ions and reference substances, and referring to relevant literature and database search. The main differential components were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA), and the differential components were quantitatively studied by high performance liquid chromatography, and the differences in the types and contents of chemical components before and after fermentation were compared. Results: A total of 77 compounds were identified or preliminarily derived after fermentation, including 21 terpenes, 18 organic acids, 9 flavonoids, 7 nucleosides, 6 amides, 4 amino acids, 2 pyrrolidone derivatives, 2 sterols, 2 alkaloids and 6 others.Through high performance liquid chromatography, it was found that the content of daidzein genistein and ergosterol was significantly increased after fermentation, and a new component ergosterol was produced. Compared with Gastrodia Tuder Halimasch Powder before fermentation, the content of genistin and other components was significantly reduced, and there may be a process of genistin conversion to genistein.Conclusion: The fermentation process will have a certain influence on the types and contents of the chemical components of Gastrodia Tuder Halimasch Powder, and ergosterol, genistein and .daidzein may be the main pharmacodynamic components, which can provide a useful reference for the clinical rational application, quality control and mechanism of action of Gastrodia Tuder Halimasch Powder.

Conclusions: HRCT is a valuable tool in preoperative assessment of cholesteatoma, helping in making surgical decisions. It can accurately predict the extent of disease and is helpful for detecting unapparent dangerous situations. However it is not very accurate in detecting Fallopian canal and stapes erosion.

Environmental nanoparticles have been shown to exist in most aquatic environments, and environmental nanoparticles in water distribution systems significantly affect water quality. However, little research has been conducted on the environmental nanoparticles in mineral water, which is a special water resource. Nanoparticle tracking analysis (NTA) and high-resolution transmission electron microscopy (HRTEM) were conducted. This study was the first to identify many environmental nanoparticles in natural mineral water in Zibo City, Shandong Province, China. The results of the NTA showed that the concentration of the particles was 5.5 ×106 particles/mL and the peak diameter of the size distribution was 180 nm. The HRTEM showed that the nanoparticles were granular, pinniform, rodlike, and flakey in shape, and some of the nanoparticles existed in aggregation. The energy-dispersive spectrometry results showed that most of the nanoparticles contained O, Mg, Ca, Si, Fe, Ti, and P, and some of them also contained F, V, S, and Mn. When combined with the characteristics of the selected area electron diffraction pattern, the nanoparticles were confirmed to be Ca-bearing nanoparticles, attapulgite nanorods, MnO2 nanosheets, and TiO2 nanoparticles. These results indicate a new form of elements occurring in mineral water. In addition, based on the chemical and physical characteristics of the nanoparticles and mineral water, these environmental nanoparticles in the mineral water are likely to be the weathering byproducts of minerals. These nanoparticles within mineral water provide a good opportunity for understanding the behavior of nanoparticles in varied systems. Moreover, environmental nanoparticle science is important for continuing efforts in water safety, treatment, and remediation.

As a high-quality forestry product with excellent performance, particleboard is widely used in many fields and has great market potential. At present, it is a research difficulty to accurately identify small, fine and different shapes of defects on the whole particleboard through machine vision technology. The application of image super-resolution reconstruction technology on particleboard can improve the surface image quality of particleboard, which is conducive to the subsequent improvement of defect detection accuracy. In this study, super-resolution dense attention generative adversarial network (SRDAGAN) model was improved to solve the problem that super-resolution generative adversarial network (SRGAN) reconstructed image would produce artifacts and its performance needed to be improved. The Batch Normalization (BN) layer was removed, the convolutional block attention module (CBAM) was optimized to construct dense block, and the dense blocks were constructed by densely skip connection. Then, the corresponding 52400 image blocks with high resolution and low resolution were trained, verified and tested according to the ratio of 3:1:1. The model was comprehensively evaluated from the effect of image reconstruction and the two indexes of PSNR and SSIM. It was found that compared with BICUBIC and SRGAN, PSNR index of SRDAGAN increased by 4.88dB and 3.25dB respectively, and SSIM increased by 0.0507 and 0.1122 respectively. The reconstructed images not only had clearer texture, but also had more realistic expression of various features, and the performance of the model had been greatly improved. At the same time, this study also emphatically discussed on the image reconstruction effect with defects. The result showed that the SRDAGAN proposed in this study can complete the super-resolution reconstruction of particleboard images with high quality. In the future, it can also be further combined with defect detection for actual production to improve the quality of forestry products and increase economic benefits.

There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the RGB value from just four specific loci. Relying on just four loci is insufficient as it leads to losing fine details from the neighboring region(s). We show that by taking into account the semi-local region leads to an improvement in performance. In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space. This extracted detail is used to predict the RGB value of a point. We illustrate the technique by applying the algorithm to the Optical Coherence Tomography-Angiography (OCT-A) images and show that it can upscale them to random resolution. This technique outperforms the existing state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images. The project page is available at https://rishavbb.github.io/ow-slr/index.html

We explicate a traumatized child's narcissistic defense against trauma. Following a review of psychoanalytic understandings of trauma we explore the invocation of narcissistic defenses against trauma. The phenomenon of Nachträglichkeit or après-coup is explored, and the paper concludes with clinical illustration drawn from the experience of a young boy who suffered significant separation trauma during the Holocaust.

The sensitivity of exercise ECG is marginally sufficient for detection of mild reduction of coronary blood flow in patients with early coronary atherosclerosis. Here we describe the application of new technique of ECG registration/analysis – ultrahigh resolution ECG (UHR ECG) – for early detection of myocardial ischemia (MIS). The utility of UHR ECG vs. conventional ECG (C ECG) was tested in anesthetized rats and pigs. Transmural MIS was induced in rats by the ligation of the left coronary artery (CA). In pigs, subendocardial ischemia of variable extent was produced by stepwise inflation of balloon within the right CA causing 25-100% reduction of its lumen. In rats, a reduction in power spectral density (PSD) in high-frequency (HF) channel of UHR ECG was registered at 60 s after ischemia (power 0.81±0.14 vs. 1.25±0.12 mW at baseline, p<0.01). This was not accompanied by any ST segment elevation on C ECG. In pigs, PSD in HF channel of UHR ECG was significantly decreased at 25% reduction of CA lumen, while ST segment on C ECG remained unchanged. In conclusion, UHR ECG enabled earlier detection of transmural MIS compared to C ECG. PSD in HF-channel of UHR ECG demonstrated greater sensitivity in the settings of subendocardial ischemia.

Aiming at the difficult problem of broadband oscillation localization in power systems, the intel-ligent localization method of oscillation source based on the digital twin is proposed and the os-cillation source localization system based on the digital twin is constructed. Firstly, a digital twin-based oscillation source localization method and system architecture are proposed. Fur-thermore, an intelligent positioning method of oscillation source based on data-driven and mechanism fusion is proposed. It includes three steps: oscillation signal preprocessing, oscillation modal analysis and oscillation source localization. For the oscillation signal preprocessing, the generative adversarial imputation network is used to repair the missing samples, and the su-per-resolution technique is used to realize the super-resolution measurement of broadband os-cillation. In the oscillation modal analysis, the spectrum of the oscillation signal is extracted using the fast Fourier transform. To accurately locate the oscillation source, the branch potential energy is used as input to the data-driven model, such as LSTM and CNN. Finally, an oscillation source localization system is developed based on the digital twin workshop CloudPSS-XStudio, which can locate the oscillation source quickly and accurately.

With increasing power and speed of laser welding, in-process monitoring has become even more crucial to ensure process stability and weld quality. Due to its low cost and installation flexibility, acoustic process monitoring is a promising method and has demonstrated its effectiveness. Since its feasibility has been the focus of existing studies, the temporal resolution of acoustic emissions (AE) has not yet been addressed despite its utmost importance for realizing real-time systems. Aiming to provide a benchmark for further development, this study investigates the relationship between duration and informativeness of AE signals during high power (3.5kW) and high speed (12m/min) laser beam butt welding. Specifically, the informativeness of AE signals is evaluated based on the accuracy of detecting and quantifying joint gaps for various time windows of signals, yielding numerical comparison. The obtained results show that signals can be shortened up to a certain point without sacrificing their informativeness, encouraging to optimize the signal duration. Our results also suggest that large gaps (> 0.3mm) induce unique signal characteristics in AE, which are clearly identifiable from 1 ms signal segments, equivalent to 0.2mm weld seam.

With the abundance of conversations happening everywhere, dialogue summarization plays an increasingly important role in the real world. However, dialogues inevitably involve lots of personal pronouns, which hinder the performance of existing dialogue summarization models. This work proposes a framework named WHORU to inject external personal pronoun resolution (PPR) information into abstractive dialogue summarization models. To reduce time and space consumption, we further propose a simple and effective PPR method for the dialogue domain. Experiments demonstrated the superiority of the proposed methods. More importantly, we achieve new SOTA results on SAMSum and AMI datasets.

The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.

This paper aims at trying to answer the question of whether a low cost drone equipped with a regular camera can serve as a simple device to estimate the size of objects. Ad-ditionally, it assesses if the accuracy of measurement is good enough for using a drone for measurements, relying solely on GPS position and camera. A review of the literature pertaining to ground object measurements was conducted. The availability of such in-struments was evaluated in terms of purchase and rental costs, the number of instru-ments on the market, and the time to obtain them. The most common measurement methods were considered and their unit costs were reviewed. The cost of professional surveying and measuring equipment was compared to the possibility of using low-cost, non-commercial drones to measure objects and surfaces. The quality of the images ob-tained was compared with high-resolution satellite systems. Is an ordinary drone with a standard camera suitable for length measurement without professional measuring equipment? Should the results be considered only in the category of size estimation?

The way humans use natural resources and especially freshwater, reflects their relationship with nature. It also influences the conceptual Water Resources Management (WRM) model. A historical review shows that the interplay between Humans and Nature is diachronically in constant change between two opposites: conflict and cooperation. Lessons from the past 20.000 years indicate that the WRM model is a function of two main parameters: (1) socio-economics, and (2) climate conditions. Three different Eras of the Humans-Nature relationship have been distinguished: (1) Naturalistic: Nature dominating Humans during the Last Glacial Period (100-10) kyr BC, (2) Dualistic: Nature-Humans cooperation and competition from 10 kyr BC to 1800 AD, and (3) Anthropocentric: Humans dominating Nature from 1800 AD to now. Since 2000, the Integrated WRM (IWRM) model is promoted as state-of-the-art and remains anthropocentric producing huge externalities. Its assessment during the last 20 years has given mixed results and needs to be reformulated. The new model we suggest is based on the dialectical tool for conflict resolution. It unifies Humans and Nature and enhances the social dimension of WRM. After identifying conflicts between stakeholders and the natural laws (eristic part), opposite objectives are unified to harmonize Humans with Nature (dialectical resolution). A case study of flood mitigation illustrates the eristic-dialectical methodology.

This article investigates the potential for carbon reduction in urban parks in Shangqiu City using high-resolution remote sensing imagery. The aim is to guide modern urban carbon neutrality strategies. The carbon reduction potential is estimated based on the mitigation of the urban heat island effect by park greenery, which reduces energy consumption. The parks are regarded as cool island centers, and 100 cooling gradients are gradually formed outward until the temperature reaches that of impermeable urban surfaces. The energy saved by the parks in mitigating the heat island effect is statistically calculated as the carbon reduction potential of urban park greenery. Additionally, this article classifies the sample parks into different categories and selects 26 landscape metrics to analyze their relationship with carbon-saving potential and driving factors.

Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in the Amazon. Images from medium-resolution optical satellites (mostly Landsat) combined with extensive field data have allowed researchers to assess patterns of tree mortality and monitor forest recovery over decades of subsequent succession in different regions. Although satellites with high spatial-resolution have become available for the Amazon in the last decade, they have not yet been employed for the mapping and quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November/2015. Remote sensing estimates of tree mortality were produced with Spectral Mixture Analysis and analyzed together with forest inventory data using Generalized Linear Models. Windthrow tree-mortality measured in 3 transects (30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Based on this ground truth, the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3% windthrow tree-mortality, p&lt;0.001). The mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of fit of models showed the opposite pattern, which may reflect the influence of crown damage not accounted for in our field study, and fast-growing regeneration of leaf area. Among the satellites studied, Landsat 8 most accurately captured field observations of variations in tree mortality across the disturbance gradient (i.e., lower under- and/or overestimation from undisturbed to extremely damaged forest). Although satellites with high spatial-resolution can refine estimates of windthrow severity by allowing the quantification of individual tree damage and mortality, our results validate the reliability of Landsat imagery for assessing patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Although high-resolution imagery may improve estimates of tree damage and mortality, these should be validated using field data at compatible scales.

Dawn of the omics revolution in biological sciences meant that we seek to understand more and in greater detail the molecular constituents of cells and biological systems. While we have gained significant insights from conventional omics tools, we now seek to understand the spatial dimensions of the data where subcellular localisation may impact on cellular physiology and phenotype. This review paper seeks to address current questions in the new field of image-based spatial proteomics as well as outline future challenges of the field. At first glance, spatial proteomics offers enormous potential to expand our understanding of different cell types in different disease and cell states. But limitations in types of fluorophores and issues with spectral overlap significantly hampers the practical implementation of the technique. On the other hand, while we have super-resolution microscopy techniques such as STED, PALM and STORM able to achieve 10 to 20 nm spatial resolution in single molecule localisation, problems with slow image acquisition limits high temporal resolution tracking of multiple protein targets in live cell imaging. Hence, the field of spatial proteomics is a mix of promises and challenges where we could image, in multi-colour, upwards of 10 well-chosen proteins that could inform on the molecular mechanisms of selected biological processes, but, at present, the method could not tackle larger scale questions. In essence, current implementation of image-based spatial proteomics is useful, but it is unable to fulfil the mission of large-scale projects such as the Human Protein Atlas or Human Cell Atlas. Future challenges in the field includes the development of more fluorophores (especially photoswitchable and photoactivable ones) for single molecule localisation microscopy, as well as seeking to improve temporal resolutions to the sub-millisecond range.

Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight GAN-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice.

Background: Children with anorectal malformation (ARM) require long-term follow-up, especially for fecal continence and main predictors of longer-term success are the type of ARM, associated anomalies and sacral integrity. Three-Dimensional High Resolution Anorectal Manometry (3D-HRAM) provides an image of the pressure profile of anal sphincter complex. Aim of study was to evaluate the anal sphincter function in ARM patients with 3D HRAM correlating this analysis to clinical outcome and to associated malformations. Methods: Forty ARM patients, were submitted to 3D HRAM: manometric, anatomical and clinical score were correlated to response to bowel management (BM). Results: A positive correlation between all scores and type of ARM was found: in high ARM and in patients with spinal anomalies (regardless to ARM type) lower scores were reported and even after BM they do not achieve good continence. Conclusions: 3D-HRAM provides information on the functional anatomy of the sphincter complex. Our study shows good correlation between the manometric results and clinical outcome, confirming spinal malformations and ARM type as most important prognostic risk factors for poor outcome. Specific sphincteric defects can be explored with manometry, allowing for tailored bowel management strategies.

Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an urgent need for rapid, accurate and precise analytical techniques to detect VCO adulteration. In this study, the use of Fourier Transform Infrared (FTIR) spectroscopy combined with Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) methodology was evaluated to verify purity or adulteration of VCO with reference to several low-cost commercial oils such as sun-flower, maize and peanut oils. Control charts were developed to assess the purity of oil samples using MCR-ALS scores values calculated from a data set of pure and adulterated oils. In addition, quantification models were developed using MCR-ALS with correlation constraint for adulterated coconut oil to assess the blend composition. Different data pre-treatment strategies were tested in order to best extract the information contained in the sample fingerprints, and the calibration models were optimised using a genetic algorithm (GA) to select the most important variables. The models gave satisfactory results in external validation procedure, with absolute errors of less than 4.6 % for samples adulterated with sunflower, maize and peanut oils.

Imaging of the ocular vasculature can provide new insights into the pathophysiology of ocular diseases. This study proposes a novel high-frequency super-resolution ultrasound localization microscopy (SRULM) technique and evaluates its ability to measure in vivo perfusion changes in the rat eye at elevated intraocular pressure (IOP). A 38.4 MHz center frequency linear array transducer on a VisualSonics Vevo F2 imaging platform was used to collect high frame rate (1 kHz) radiofrequency data of the posterior rat eye following systemic microbubble contrast injection. Following clutter and spatiotemporal non-local means filtering, individual microbubbles were localized and tracked. The microbubble tracks were accumulated over 10,000 frames to generate vascular images quantifying perfusion velocity and direction. Experiments were performed using physiologic relevant controlled flow states for algorithm validation and subsequently performed in vivo on the rat eye at 10 mm Hg IOP increments from 10 to 60 mm Hg. The posterior vasculature of the rat eye, including the ophthalmic artery, long posterior ciliary arteries and their branches, central retinal artery and retinal arterioles and venules were successfully visualized, and velocities quantified at each IOP level. Significant reductions in arterial flow were measured as IOP was elevated. High-frequency SRULM can be used to visualize and quantify perfusion velocity of the rat eye in both the retrobulbar and intraocular vasculature simultaneously. The ability to detect ocular perfusion changes throughout the depth of the eye may help elucidate the role ischemia has in the pathophysiology of ocular diseases such as glaucoma.

Economic valuations of ecosystem services often transfer previously estimated global unit values to the geographical setting of interest. While this approach produces quick results, its reliability depends on how representative the large-scale average unit values are for the given local context. Here, we estimate the values of three ecosystem services (ES) – water filtration, nutrient cycling and carbon sequestration – in the Grand River watershed (GRW) of southern Ontario, Canada. The watershed covers nearly 7000 km2, has a humid continental climate and a population of close to one million people. Land cover is dominated by agriculture. We compare ES valuations using locally derived (i.e., GRW-specific) unit values to valuations based on unit values from a regional database and those compiled in the global Ecosystem Services Valuation Database (ESVD). The regional database includes mean unit values from three case studies within southern Ontario and one boreal watershed in British Columbia. As expected, the regional database yields average monetary values for the three ES that are close to those obtained with the local unit values but with larger associated uncertainties. Using the ESVD, however, results in significantly higher monetary values for the ES. For water filtration, the ESVD value is more than five times higher than the regional and local estimates. We further illustrate the effect of the extent of aggregation of forested and agricultural land categories on the ES values. For example, by subdividing the forest category into three sub-categories (deciduous, coniferous, and mixed forest), the estimated value of the carbon sequestration ES of forested areas within the GRW increases by 15%. Overall, our results emphasize the importance of critically assessing the origin of unit values and the land cover resolution in ES valuation, especially when the latter is used as a policy-guiding tool.

Accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understand their structure-function relationships and electrochemical properties. Current method of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users’ defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly / partially resolved and unresolved spectra, which is a common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper-nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.

Is it possible to develop a meaningful measure for the complexity of a simulation model? Algorithmic information theory provides concepts that have been applied in other areas of research for the practical measurement of object complexity. This article offers an overview of complexity from a variety of perspectives and provides a body of knowledge with respect to the complexity of simulation models. Key terms of model detail, resolution, and scope are defined. An important concept from algorithmic information theory, Kolmogorov complexity, and an application of this concept, normalized compression distance, are used to indicate the possibility of measuring changes in model detail. Additional research in this area can advance the modeling and simulation body of knowledge toward the practical application of measuring simulation model complexity. Examples show that KC and NCD measurements of simulation models can detect changes in scope and detail.

Spatial biology is a rapidly growing research field which focuses on the transcriptomic or proteomic profiling of single cells within tissues with preserved spatial information. Imaging-based spatial transcriptomics uses epifluorescence microscopy, which has shown remarkable results for identification of multiple targets in situ. Nonetheless, the number of genes that can be reliably visualized is limited by the diffraction of light. Here, we investigate the effect of structured illumination (SIM), a super-resolution microscopy approach, over the performance of single gene transcript detection in spatial transcriptomics experiments. We performed direct mRNA-targeted hybridization in situ sequencing for multiple genes in mouse coronal brain tissue sections. We evaluated spot detection performance in widefield and confocal images versus those with SIM in combination with 20X, 25X and 60X objectives. In general, SIM increases the detection efficiency of gene transcripts spots compared to widefield and confocal modes. For each case, the specific fold increase in localizations is dependent on gene transcripts density and the numerical aperture of the objective used, which showed to play an important role especially for densely clustered spots. Taken together, our results suggest that SIM has the capacity to improve spot detection and overall data quality in spatial transcriptomics.

To sustain and advance the development of our laboratory, it is imperative for the laboratory 1 personnel to consistently deliver high-quality products and services that meet customer expectations. 2 The progress of productivity and the pace of technological advancements result in constant evolution 3 of measurement procedures, analytical techniques, and overall work methods. Our research within 4 the Neutron Activation Analysis (NAA) laboratory is aimed at contributing to the establishment of 5 quality assurance that complies with the ISO 17025 standard. As such, the laboratory implements 6 quality-oriented approaches to foster a culture of continuous improvement in its detector operations 7 and enhance customer satisfaction.

Deep learning technologies for skin cancer detection have been dramatically advanced based on high resolution dermoscopic images. The low-cost approach based on clinical skin images of low resolution is promising but remains technically challenging due to their undermined image quality. In this paper, we propose a coarse-to-fine efficient super resolution transformer (CF-ESRT) network to reconstruct the dermoscopy-level high resolution skin image from a low resolution clinical image. By connecting the refinement network to the original super resolution transformers and applying perceptual and gradient losses, our framework noticeably improves the finer texture details of skin lesions in the super resolution (SR) images, and is effective to elevate the perceptual quality of the SR images. Quantitative and qualitative evaluations show that our method outperforms ESRT the basis model as well as the other state-of-the-art SR models.

This paper shows that by applying the Nyquist-Shannon sampling theorem, the spatial and temporal resolution of a simulation can be no more than half the resolution of the simulating reality. This has significant implications for not only the values of the observables in the simulation but also its physical laws. This progressive halving of nested simulations coupled with the minimum resolution compatible with the production of a simulation also sets a limit to the nestedness of a lineage of simulations (it is by no means infinite). The limit of nestedness is then used to calculate the probability that we are living in a simulation assuming a single base reality. This will be shown to be significantly lower than popular expectation. A Kardashev-like scale, with three variants, is also developed to gauge the technological advancement of a civilization in relation to the extent to which it can extract information from space and time.

With the development of deep learning technology, various structures and research methods for super-resolution restoration of natural images and document images have been introduced. In particular, a number of recent studies have been conducted and developed in image restoration using generative adversarial network. Super-resolution restoration is ill-posed problem because of some complex restraints such as a lot of high-resolution images being restored for the same low-resolution image and also difficulty in restoring noises like edges, light smudging, and blurring. In this study, we utilized the spatially adaptive de-normalization (SPADE) structure for document image restoration to solve previous problems such as edge unclearness, hardness to catch features of texts, and the image color transition. Consequently, it can be confirmed that the edge of the character and the ambiguous stroke are restored more clearly when contrasting with the other previously suggested methods. Also, the proposed method’s PSNR and SSIM scores are geting 8% and 15% higher, respectively, compared to the previous methods.

A system of wavelet collocation upwind schemes is constructed for solving hyperbolic conserva-tion laws based on a class of interpolation wavelets. The bias magnitude and symmetry factor are defined to depict the asymmetry of the adopted scaling basis function in wavelet theory. Effects of characteristics of the scaling functions on the schemes are explored based on numerical tests and Fourier analysis. The numerical results reveal that the stability of the constructed scheme is af-fected by the smoothness order, N, and the asymmetry of the scaling function. The dissipation analysis suggests that schemes with Neven have negative dissipation coefficients, leading to unstable behaviors. Only scaling functions with Nodd and bias magnitude of 1 can be used to construct stable upwind schemes due to the non-negative dissipation coefficients. Resolution of the wavelet scheme tends to the spectral resolution as the order of accuracy of the scheme in-creases.

In the context of the Atmospheric Observing System (AOS) international program, a new generation spaceborne lidar is expected to be in polar orbit for deriving new observations of aerosol and clouds. In this work, we analyze the added values of these new observations for characterizing aerosol vertical distribution. For this, synthetic observations are simulated using the BLISS lidar simulator in terms of backscatter coefficient at 532 nm. We consider two types of lidar instruments, an elastic backscatter lidar instrument and a high spectral resolution lidar (HSRL). These simulations are performed with atmospheric profiles from a Nature Run (NR) modeled by the MOCAGE Chemical Transport Model. In three case studies involving large events of different aerosol species, the added value of the HSRL channel for measuring aerosol backscatter profiles with respect to simple backscatter measurements is shown. Observations independent from an a-priori lidar Ratio assumption, as done typically for simple backscattering instruments, allows probing the vertical structure of aerosol layers without divergence, even in case of intense episodes. Relative error in the backscatter coefficient profiles are observed to lay between +40% and -40% for low abudancies, with mean biases between +5% and -5%. A 5-day study in the case of desert dust completes the study of the added value of the HSRL channel with relative mean bias from the NR of the order of 1.5%.

High resolution melting (HRM) analysis is a powerful detection method for fast, high-throughput post-PCR analysis. A two-step HRM marker system was developed for identification of the N-, S-, R- and T-cytoplasms of onion. In the first step for identification of N-, S-, and R-cytoplasms, one forward primer was designed to the identical sequences of both cox1 and orf725 genes and two reverse primers specific to the polymorphic sequences of cox1 and orf725 genes were used. For the second step breeding lines with N-cytoplasm were evaluated with primers developed from the orfA501 sequence to distinguish between N- and T-cytoplasms. An amplicon with primers to the mitocondrial atp9 gene was used as an internal control. The two-step HRM marker system was tested using 246 onion plants. HRM analysis showed that the most common source of CMS, often used by Russian breeders, is S-cytoplasm, the rarest type of CMS is R-cytoplasm, and the proportion of T-cytoplasm among the analyzed breeding lines was 20.5%.

These days, numerous organizations and Analysis Centers (AC) offer various Ambiguity Resolution (AR) products using various methodologies. To use it for time-frequency transfer, there is no associated study. This paper chooses 16 MGEX stations with external high-precision atomic clocks to constitute 15 international time comparison links and uses AR products data from CNES, SGG, CODE, and PRIDE laboratory, using three ambiguity-fixed strategies, to thoroughly evaluate the effects of various strategies and AR products for high-precision time-frequency transfer. We reach the following results by using the IGS final clock product as a reference and comparing it to ambiguity-float. With various ambiguity-fixed procedures, the time stability STD of time transfer is increased for a single GPS, and the improvement ranges from 10% to 40%. The frequency stability has barely improved; up to 40%, the most notable improvement comes from FCB with GRM products. The time stability STD of combinations has improved after the addition of the Galileo system compared to the single GPS, and the improvement ranges from 2% to 9%. Most strategies have improved, while a few techniques have weakened with the GEC (GPS+Galileo+BDS) combination. We feel that the stability has not significantly increased with the system's increase within short-term stability after comparing multiple groups of linkages.

Diffuse interstitial lung diseases (DILD) are a heterogeneous group of over 200 entities, some with dramatical evolution and poor prognostic. Because of their overlapping clinical, physiopathological and imagistic nature, successful management requires early detection and proper progression evaluation. This paper tests a complex networks (CN) algorithm for imagistic aided diagnosis fitness for the possibility of achieving relevant and novel DILD management data. 65 DILD and 31 normal high resolution computer tomography (HRCT) scans were selected and analyzed with the CN model. The algorithm is showcased in two case reports and then statistical analysis on the entire lot shows that a CN algorithm quantifies progression evaluation with a very fine accuracy, surpassing functional parameters’ variations. The CN algorithm can also be successfully used for early detection, mainly on the ground glass opacity Hounsfield Units band of the scan.

Event-based vision is an emerging field of computer vision that offers unique properties such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution, object detection and tracking approach, that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per each detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze the tracking performance at different temporal rates. Our approach shows promising results with minimal performance deterioration at higher temporal resolutions (48 – 384 Hz) when compared with the baseline frame-based performance at 24 Hz.

Boosted by the progress in deep learning, Single Image Super-Resolution (SISR) has gained a lot of interest in the Remote Sensing community, who sees it as an oportunity to compensate for satellite's ever-limited spatial resolution with respect to end users needs. While there has been a great amount of work on network architures in the latest years, deep learning based SISR in remote sensing is still limited by the availability of the large training sets it requires. The lack of publicly available large datasets with the required variability in terms of landscapes and seasons pushes researchers to simulate their own dataset by means of downsampling. This may impair the applicability of the trained model on real world data at the target input resolution. In this paper, we propose an open-data licenced dataset composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meter resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations on earth with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring the spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.

The unicellular eukaryote S. cerevisiae is an invaluable resource for the study of basic eukaryotic cellular and molecular processes. However, due to its small size compared to other eukaryotic organisms the study of subcellular structures is challenging. Expansion microscopy (ExM) holds great potential to study the intracellular architecture of yeast, especially when paired with pan-labelling techniques visualising the full protein content inside cells. ExM allows to increase imaging resolution by physically enlarging a fixed sample that is embedded and cross- linked to a swellable gel followed by isotropic expansion in water. The cell wall present in fungi – including yeast – and Gram-positive bacteria is a resilient structure that resists denaturation and conventional digestion processes usually used in ExM protocols, resulting in uneven expansion. Thus, the digestion of the cell wall while maintaining the structure of the resulting protoplasts are crucial steps to ensure isotropic expansion. For this reason, specific experimental strategies are needed, and only a few protocols are currently available. We have developed a modified ExM protocol for S. cerevisiae, with 4x expansion factor, which allows the visualisation of the ultrastructure of the cells. Here, we describe the experimental procedure in detail, focusing on the most critical steps required to achieve isotropic expansion for ExM of S. cerevisiae.

Diarylethene is a prototypical molecular switch that can be reversibly photoisomerized between its open and closed forms. Ligands bpy-DAE-bpy, consisting of a phenyl-diarylethene-phenyl (DAE) central core and bipyridine (bpy) terminal substituents, are able to self-organize. They are investigated by scanning tunneling microscopy at the solid-liquid interface. Upon light irradiation, cooperative photochromic switching of the ligands is recognized down to the sub-molecular level. The closed isomers show different electron density of states (DOS) contrasts, attributed to the HOMO or LUMO molecular orbitals observed. More importantly, the LUMO images show remarkable differences between the open and closed isomers, attributed to combined topographic and electronic contrasts mainly on the DAE moieties. The electronic contrasts from multiple HOMO or LUMO distributions, combined with topographic distortion of the open or closed DAE, are interpreted by density functional theory (DFT) calculations.

The quantification of sea surface temperature (SST) through space platforms has revolutionized how we obtain information at a global level. However, the main disadvantage of obtaining SST with satellite images consists of its inherent coarse spatial resolution. One solution could be the use of downscaling algorithms to create sequences of matrices at a higher resolution. We used the same SST data source from the MODIS-Aqua sensor at three spatial resolutions of 9 km, 4.5 km, and 1 km in the Gulf of California. Based on an open-source algorithm, the original SST images were downscaled to 4.5 km, 1 km, 500 m, 250 m, and 125 m per pixel scales. Results indicate a strong linear relationship between the original SST-MODIS data and the modeled data for all spatial resolutions. This study demonstrates the feasibility of an open-source downscaling algorithm to enhance the spatial resolution of SST images in a marginal sea.

Currently available nationwide prediction models for fine particulate matter (PM_2.5) lack prediction confidence intervals and usually do not describe cross validated model performance at different spatiotemporal resolutions and extents. We used 41 different spatiotemporal predictors, including data on land use, meteorology, aerosol optical density, emissions, wildfires, population, traffic, and spatiotemporal indicators to train a machine learning model to predict daily averages of PM_2.5 concentrations at 0.75 sq km resolution across the contiguous United States from 2000 through 2020. We utilized a generalized random forest model that allowed us to generate asymptotically-valid prediction confidence intervals and took advantage of its usefulness as an ensemble learner to quickly and cheaply characterize leave-one-location-out CV model performance for different temporal resolutions and geographic regions. Using a variable importance metric, we selected 8 predictors that were able to accurately predict daily PM_2.5, with an overall leave-one-location-out cross validated median absolute error of 1.20 ug/m³, an R² of 0.84, and confidence interval coverage fraction of 95%. When considering aggregated temporal windows, the model achieved leave-one-location-out cross validated median absolute errors of 0.99, 0.76, 0.63, and 0.60 ug/m³ for weekly, monthly, annual, and all-time exposure assessments, respectively. We further describe the model’s cross validated performance at different geographic regions in the United States, finding that it performs worse in the Western half of the country where there are less monitors. The code and data used to create this model are publicly available and we have developed software packages to be used for exposure assessment. This accurate exposure assessment model will be useful for epidemiologists seeking to study the health effects of PM across the continental United States, while possibly considering exposure estimation accuracy and uncertainty specific to the the spatiotemporal resolution and extent of their study design and population.

The salt marshes, lying at the land-sea temperate interface, furnish a plethora of ecosystems services such as biodiversity niche support, ocean-climate change regulation, ornithology recreo-tourism or plant gathering by hand. They undergo significant worldwide losses due to their conversion into crop fields and to their spatial compression between the rising sea-level and the armoring shoreline. Their monitoring however requires to use a suite of remote sensing sensors to embrace the regional scale while capturing the plant details. This research innovatively adopts a multiscale approach using a cascading spaceborne and airborne process, from the 10-m Sentinel-2, through the 3-m Dove, to the 0.03-m unmanned airborne vehicle (UAV) imageries. The high to very high temporal resolution of the Sentinel-2 and Dove enabled to cover twenties and tens of km2 over five and four years, respectively, in the form of normalized difference vegetation index (NDVI) classes, associated with microphytobenthos, low, medium and high salt marsh vegetation, including the opportunistic Elyma genus. The NDVI was then modelled at the UAV scale (a few km2) using a three-layered NN prediction, providing the final near-infrared (NIR), and the intermediate red, green and blue reflectance imageries, calibrated/validated/tested with the Dove reflectance imageries (R2NIR=0.98, R2red=0.88, R2green=0.84, and R2blue=0.90). The 100fold increase in pixel size allowed to detect the decimeter-scale objects of the tidal flats and salt marshes, to enlarge the NDVI class ranges, and hold great promise to model other spectral bands at the UAV scale for further deeply enhancing the salt marsh mapping.

Three-dimensional reconstruction plays an important role in assisting doctors and surgeons in diagnosing bone defects’ healing progress. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, volume rendering is omitted. Many improvements have been made on surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images, and studying the effects of different parameter settings for the improvements. Hence, in this study, an attempt towards near real-time surface rendering for large medical images is made. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 0.5 and mesh simplification reduction factor of 0.1, is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased the reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61% respectively.

Cyanobacteria are microorganisms able to adapt to a wide variety of environmental conditions and abiotic stresses. They produce a very large number of metabolites that can participate in the adaptation of cyanobacteria to a large range of resources such as light, temperature, or nutrient. The metabolites variation is one way to understand the physiological status and adaptation of cells. In this study, we aim to understand how the diversity and the dynamics of the whole metabolome is dependent of the growth phases and under control of abiotic factors (e.g. light intensity and temperature). The cyanobacteria Aliinostoc sp. PMC 882.14 was selected for its large number of biosynthetic gene clusters. Metabolomes were analyzed by using mass spectrometry (qTOF-MS/MS) combined with untargeted analysis to investigate the metabolite dynamics. Significant variations were characterized between exponential and stationary phases, whatever the culture conditions (“control”, “higher light”, or “higher temperature”). ”Higher light” and “higher temperature” favored the synthesis of metabolites belonging to the same molecular families. Among highly regulated metabolites, we observe the presence of mycosporine-like amino acids (MAAs), and various variants of somamides, microginins, and microviridins. Through Aliinostoc sp. PMC 882.14, this study shows the importance of knowing the physiological state of cyanobacteria for comparative global metabolomics and questions the regulation processes involve into metabolite families production. Our results also open up new perspectives in the context of the production of targeted bioactive metabolites.

The cellular cytoskeleton provides the cell with a mechanical rigidity which allows mechanical interaction between cells and the extracellular environment. The actin structure plays a key role in mechanical events like motility, or establishment of cell polarity. From the earliest stages of development, as represented by ex vivo expansion of naïve embryonic stem cells (ESCs), the critical mechanical role of the actin structure is becoming recognized as a vital cue for correct segregation and lineage control of cells and as a regulatory structure that controls several transcription factors. Naïve ESCs have a characteristic morphology and the ultrastructure that underlies this condition remains to be further investigated. Here, we investigate the 3D actin cytoskeleton of naïve mouse ESCs using super resolution optical reconstruction microscopy (STORM). We investigate the morphological, cytoskeletal and mechanical changes in cells cultured in 2i or Serum/LIF media reflecting a homogenous preimplantation cell state and a state that is closer to embarking on differentiation. STORM imaging showed that the peripheral actin structure undergoes a dramatic change between the two media conditions. We also detected micro-rheological differences in the cell periphery between the cells cultured in these two media correlating well with the observed nano-architecture of the ESCs in the two different culture conditions. These results pave the way for linking physical properties and cytoskeletal architecture to cell morphology during early development.

Automated extraction of buildings from earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, policy making. Combining data from different sensors such as high-resolution multispectral (HRI) and light detection and ranging (LiDAR) has shown great potential in building extraction. Deep learning (DL) is increasingly used in multimodal data fusion and urban object extraction. However, DL-based multimodal fusion networks may underperform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multimodal features. Recently, an hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset including co-located Very-High-Resolution (VHR) optical images and Light Detection And Ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model overparametrization, which inevitably leads to long training times and heavy computational load. In this paper the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation.

Existing methods enhance mid-low resolution remote sensing ship detection by feeding super-resolved images to the detectors. Although these methods marginally improve the detection accuracy, the correlation between image super-resolution (SR) and ship detection is under-exploited. In this paper, we propose a simple but effective ship detection method called ShipSR-Det, in which both the output image and the intermediate features of the SR module are fed to the detection module. Using the super-resolved feature representation, the potential benefit introduced by image SR can be fully used for ship detection. We apply our method to the SSD and Faster-RCNN detectors and develop ShipSR-SSD and ShipSR-Faster-RCNN, respectively. Extensive ablation studies validate the effectiveness and generality of our method. Moreover, we compare ShipSR-Faster-RCNN with several state-of-the-art ship detection methods. Comparative results on the HRSC2016, DOTA and NWPU VHR-10 datasets demonstrate the superior performance of our proposed method.

Existing methods for building extraction from remotely sensed images strongly rely on aerial or satellite-based images with very high resolution, which are usually limited by spatiotemporally accessibility and cost. In contrast, relatively low-resolution images have better spatial and temporal availability but cannot directly contribute to fine- and/or high-resolution building extraction. In this paper, based on image super-resolution and segmentation techniques, we propose a two-stage framework (SRBuildingSeg) for achieving super-resolution (SR) building extraction using relatively low-resolution remotely sensed images. SRBuildingSeg can fully utilize inherent information from the given low-resolution images to achieve high-resolution building extraction. In contrast to the existing building extraction methods, we first utilize an internal pairs generation module (IPG) to obtain SR training datasets from the given low-resolution images and an edge-aware super-resolution module (EASR) to improve the perceptional features, following the dual-encoder building segmentation module (DES). Both qualitative and quantitative experimental results demonstrate that our proposed approach is capable of achieving high-resolution (e.g. 0.5 m) building extraction results at 2×, 4× and 8× SR. Our approach outperforms 8 other methods with respect to the extraction result of mean Intersection over Union (mIoU) values by a ratio of 9.38%, 8.20% and 7.89% with SR ratio factors of 2, 4, and 8, respectively. The results indicate that the edges and borders reconstructed in super-resolved images serve a pivotal role in subsequent building extraction and reveal the potential of the proposed approach to achieve super-resolution building extraction. Our code is available at https://github.com/xian1234/SRBuildSeg.

In Acropora, the complex canals in a coral colony connect all polyps into a holistic network to collaborate in performing biological processes. There are various types of canals, including calice, axial canals, and other internal canals, with structures that are dynamically altered during different coral growth states due to internal calcium transport. However, few studies have considered the regulation of calcium transport in Acropora. In this study, we investigated the morphological changes of the axial canal in six Acropora muricata samples by high resolution micro-computed tomography, observing the patterns of the axial canal during the processes of new branch formation and truncated branch rebuilding. We visualized the formation of a new branch from a calice and deposition of the iconic hexactin skeletons in the axial canal. Furthermore, the diameter and volume changes of the axial canal in truncated branches during rebuilding processes were calculated, revealing that the volume ratio of calcareous deposits in the axial canal exhibit significant increases within the first three weeks, returning to levels in the initial state in the following week. This work indicates that the axial canal can transport calcium to form hexactin skeletons in a new branch and rebuild the tip of a truncated branch. The calcium transport along canal network regulates various growth processes, including budding, branching, skeleton forming, and self-rebuilding of an Acropora colony. Understanding the changes in canal function under normal and extreme conditions will provide theoretical guidance for restoration and protection of coral reefs.

In Acropora, the complex canals in a coral colony connect all polyps into a holistic network to collaborate in performing biological processes, while axial canal is the largest canal amongst the network and distributes at the center of a coral branch. However, previous studies indicated that, in the non-radial symmetry transport system of Acropora, axial canal do not play a major role in the transport of hydroplasm, and the action of axial canal in coral growth is still obscure. In this study, we reconstructed six Acropora muricata samples by high resolution micro-computed tomography to investigate the growth patterns of axial canals during the processes of new branch forming and truncated branch rebuilding. We found that the axial canal of a new branch is transformed from a calice and the polyps in the new branch are budded from the polyp in the axial canal. Meanwhile, the axial canal can transport the calcareous skeletons to rebuild the tip of a truncated branch, which represents as the change in the diameter of axial canal and calcareous deposition/reduction in it. This work indicate the regulation of axial canal in the growth processes including budding, branching, and mineralising of an Acropora colony.

Classical models of gene expression were built using genetics and biochemistry. Although these approaches are powerful, they have very limited consideration of the spatial and temporal organization of gene expression. Although the spatial organization and dynamics of RNA polymerase II (RNAPII) transcription machinery has fundamental functional consequences for gene expression, its detailed studies have been for long time abrogated by the limits of classical light microscopy. The advent of super-resolution microscopy (SRM) techniques allowed for the visualization of the RNAPII transcription machinery with nanometer resolution and millisecond precision. In this review, we summarize the recent methodological advances in SRM, focus on its application for studies of the nanoscale organization in space and time of RNAPII transcription, and discuss its consequences for the mechanistic understanding of gene expression.

The maker movement has reached the optics labs, empowering researchers to actively create and modify microscope designs and imaging accessories. 3D printing has especially had a disruptive impact on the field, as it entails an accessible new approach in fabrication technologies, namely additive manufacturing, making prototyping in the lab available at low cost. Examples of this trend are taking advantage of the easy availability of 3D printing technology. For example, inexpensive microscopes for education have been designed, such as the FlyPi. Also, the highly complex robotic microscope OpenFlexure represents a clear desire for the democratisation of this technology. 3D printing facilitates new and powerful approaches to science and promotes collaboration between researchers, as 3D designs are easily shared. This holds the unique possibility to extend the open-access concept from knowledge to technology, allowing researchers from everywhere to use and extend model structures. Here we present a review of additive manufacturing applications in microscopy, guiding the user through this new and exciting technology and providing a starting point to anyone willing to employ this versatile and powerful new tool.

Apoptosis, the programmed and intentional death of senescent, damaged, or otherwise superfluous cells, is the natural end-point for most cells within multicellular organisms. Apoptotic cells are not inherently damaging, but if left unattended they can lyse through secondary necrosis. The resulting release of intracellular contents drives inflammation in the surrounding tissue and can lead to autoimmunity. These negative consequences of secondary necrosis are avoided by efferocytosis—the phagocytic clearance of apoptotic cells. Efferocytosis is a product of both apoptotic cell and efferocyte mechanisms, which cooperate to ensure the rapid and complete removal of apoptotic cells. Herein, the processes used by apoptotic cells to ensure their timely removal, and the receptors, signaling, and cellular processes used by efferocytes to identify, remove, and process the apoptotic cells, are reviewed.

Sentinel-2 can provide multi-spectral optical remote sensing images in RGBN bands with a spatial resolution of 10m, but the spatial details provided are not enough for many applications. WorldView can provide HR multi-spectral images less than 2m, but it is a commercial paid resource with relatively high usage costs. In this paper, without any available reference images, Sentinel-2 images at 10m resolution are improved to a resolution of 2.5m through super-resolution (SR) based on deep learning technology. Our model, named DKN-SR-GAN, uses degradation kernel estimation and noise injection to construct a dataset of near-natural low-high-resolution (LHR) image pairs, with only low-resolution (LR) images and no high-resolution (HR) prior information. DKN-SR-GAN uses the Generative Adversarial Networks (GAN) combined of ESRGAN-type generator, PatchGAN-type discriminator and the VGG-19-type feature extractor, using perceptual loss to optimize the network, so as to obtain SR images with clearer details and better perceptual effects. Experiments demonstrate that in the quantitative comparison of the non-reference image quality assessment (NR-IQA) metrics like NIQE, BRISQUE and PIQE, as well as the intuitive visual effects of the generated images, compared with state-of-the-art models such as EDSR8-RGB, RCAN and RS-ESRGAN, our proposed model has obvious advantages.

This article presents answers to the questions on superresolution and structured illumination microscopy as raised in the editorial of a recent publication [K. Prakash et al. arXiv, 2102.13649, 2021]. The answers are based on my personal views on superresolution in light microscopy, supported by reasoning. Discussed are the definition of superresolution, Abbe’s resolution limit and the classification of superresolution methods into non-linear-, prior-knowledge- and near-field-based superresolution. A further focus is put on capabilities and technical aspects of present and future structured illumination microscopy (SIM) methods.

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aims of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant Arabis alpina in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs rivalled the accuracy of LiDAR, putting the current paradigm of LiDAR being the more accurate of the two methods into question. We obtained DEMs at spatial resolutions of 6.25cm-8m for PHOTO and 50cm-32m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, we found that SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50cm in such studies.

Plasma and serum are the most widely used blood-derived biofluids for metabolomics and lipidomics assays, but the isolation of these products from blood may introduce additional bias as indicated by the fact that many analytes that are present at high concentrations in blood cells cannot be measured and evaluated in those samples. Of particular concern, variable hemolysis during the pre-processing of blood products could compromise accurate and reproducible quantification. Compared with plasma or serum, whole blood may be a better alternative due to simplicity of processing. In this study, we provide a comprehensive method for quantification of the whole blood sphingolipidome and the concentrations were compared with those from plasma. Combining a single-phase extraction method with liquid-chromatography high resolution mass spectrometry (R=120, 000), assisted by alkaline hydrolysis, we were able to identify and simultaneously quantify more than 150 sphingolipids. Furthermore, most of sphingolipids remained stable after a freeze/thaw cycle. Whole blood contained a higher concentration of most sphingolipids than corresponding plasma. Moreover, individual variations in the levels of sphingolipids were lower for whole blood than plasma. These findings demonstrate that whole blood could be a better alternative to plasma, and potentially guide the evaluation of sphinglipidome for biomarker discovery.

Gwosch et al. (2020) and Balzarotti et al. (2017) purport MINFLUX as the next revolutionary fluorescence microscopy technique claiming a spatial resolution in the range of 1-3 nm in fixed and living cells. Though the claim of molecular resolution is attractive, I am concerned whether true 1 nm resolution has been attained. Here, I compare the performance with other super-resolution methods focussing particularly on spatial resolution claims, atypical image rendering, visualisation enhancement, subjective filtering of localizations, detection vs labelling efficiency and the possible limitations when imaging biological samples containing densely labelled structures. I hope the analysis and evaluation parameters presented here are not only useful for future research directions but also microscope users, developers and core facility managers when deciding on an investment for the next 'state-of-the-art' instrument.

This paper presents an advanced microstructural analysis of the AlSiMg, Ti64 and N700 powders used for additive manufacturing. The internal microstructure of the regular and irregular powder grains were characterized down to atomic resolution by using scanning electron microscopy and high resolution scanning transmission electron microscopy.The accretionary forms on top of the irregular AlSiMg powder grains exhibit a slightly coarse microstructure with a network of eutectic Si consisting of nano-crystallites, suggestinga slower cooling than the grain itself that contain a predominately amorphous Si network. A nm thin amorphous C layer on the surface of some Ti64 plasma atomized powder grains promoted the attachment of satellites and growth of envelopes. In case of gas atomized N700 powder grains, we identified thin oxide and carbon amorphous layers as well as metal segregations at the interface between the grain body and the accretionary forms.

Abstract: Imaging devices of less than 300,000 pixels are mostly used for sewage conduit exploration due to the petty nature of the survey industry in Korea. P articular ly , devices of less than 100,000 pixels are still widely used, and the environment for image processing is very bitter . Since the sewage conduit image s covered in this study ha ve a very low resolution (240 × 320 = 76,800 pixels), it is very difficult to detect cracks. Because most of the resolution of the sewe r conduit images are very low in Korea, this problem of low resolution was selected as the subject of study. Cracks were detected through a total of six steps of improving the crack in Step 2, finding the optimal threshold value in Step 3, and applying an algorithm to detect cracks in Step 5. Cracks were effectively detected by the optimal parameters in Steps 2 and 3 and the user algorithm in Step 5. Desp ite the very low resolution, the cracked image s showed 96.4% accuracy of detection, and the non cracked image s showed 94.5% accuracy . Moreover, the analysis was excellent in quality , also . It is believed that the findings of this study can be effectively u sed for crack detection with low resolution images.

With an estimated 3 to 5 million human cases annually and the potential to infect domestic and wild animal populations, influenza viruses are one of the greatest health and economic burdens to our society [1] and pose an ongoing threat of large-scale pandemics. Despite our knowledge of many important aspects of influenza virus biology, there is still much to learn about how influenza viruses replicate in infected cells, for instance how they use entry receptors or exploit host cell trafficking pathways. These gaps in our knowledge are due, in part, to the difficulty of directly observing viruses in living cells. In recent years, advances in light microscopy, including super-resolution microscopy and single-molecule imaging, have enabled many viral replication steps to be visualised dynamically in living cells. In particular, the ability to track single virions and their components, in real time, now allows specific pathways to be interrogated providing new insights to various aspects of the virus-host cell interaction. In this review, we discuss how state-of-the-art imaging technologies, notably quantitative live-cell and super-resolution microscopy, are shedding new nanoscale and molecular insights into influenza virus replication and revealing new opportunities for developing antiviral strategies.

Monitoring crown closure evolution using multi-temporal Light Detection and Ranging (LiDAR) surveys is a method that we expect to be increasingly adopted given the availability of LiDAR sensors and the accumulating survey archives. However, little attention was devoted to comparing crown closure estimates from independent surveys. Although survey parameters cannot be modified after the data collection, we speculate that the error associated to crown closure estimates comparison can be reduced by selecting optimal post-survey parameters. In this study, we compared crown closure estimates of three airborne LiDAR surveys from 2018 (40 pt/m²) used as a reference, and two lower-density surveys from 2016 (4.5 pt/m²) and 2018 (2 pt/m²). We studied the effect of the height threshold used to separate canopy points and the grid resolution, using skewness and variance of lagged difference of crown closure. Crown closure estimates using low height thresholds were more different across surveys, resulting in higher root mean squared error (RMSE), bias and more different variograms. Results show that optimal height threshold was 3 m and grid resolution was 25 m, although there was room for decision (RMSE of 7% and 5%, and bias of 4% and 0% for 2016 and 2018 low-density surveys).

The oral administration of (R)-3-hydroxybutyl-(R)-3-hydroxybutyrate, allows inducing a beneficial level of blood ketone bodies without the adverse effects due to the adhesion to a ketogenic diet. Several studies documented the therapeutic effectiveness of the (R)-3-hydroxybutyl (R)-3-hydroxybutyrate in treating neurodegenerative diseases as well as its boosting activity of athletic and cognitive performances during prolonged physical exercises. Further studies considering this ketone body ester for therapy of other pathologies are also underway. In the present work, we describe the synthesis of (R)-3-hydroxybutyl-(R)-3-hydroxybutyrate through the enantioselective transesterification of racemic ethyl 3-hydroxybutyrate with (R)-1,3-butanediol catalyzed by immobilized Candida antarctica lipase B (CAL-B). The enantiopure (R)-1,3-butanediol was in turn obtained from the kinetic resolution of the racemate by CAL-B catalyzed acetylation with vinyl acetate. The economy of the synthetic procedure has been improved by recycling the unreacted (S) enantiomers of the ethyl 3-hydroxybutyrate and 1,3-buatnediol after stereochemical inversion achieved by tosylation and SN2 with ammonium acetate. The overall procedure allows to incorporate up to 70% of the starting racemic reagents into the final product.

Panchromatic (PAN) images contain abundant spatial information that is useful for earth observation, but always suffer from low-resolution due to the sensor limitation and large-scale view field. The current super-resolution (SR) methods based on traditional attention mechanism have shown remarkable advantages but remain imperfect to reconstruct the edge details of SR images. To address this problem, an improved super-resolution model which involves the self-attention augmented WGAN is designed to dig out the reference information among multiple features for detail enhancement. We use an encoder-decoder network followed by a fully convolutional network (FCN) as the backbone to extract multi-scale features and reconstruct the HR results. To exploit the relevance between multi-layer feature maps, we first integrate a convolutional block attention module (CBAM) into each skip-connection of the encoder-decoder subnet, generating weighted maps to enhance both channel-wise and spatial-wise feature representation automatically. Besides, considering that the HR results and LR inputs are highly similar in structure, yet cannot be fully reflected in traditional attention mechanism, we therefore design a self augmented attention (SAA) module, where the attention weights are produced dynamically via a similarity function between hidden features, this design allows the network to flexibly adjust the fraction relevance among multi-layer features and keep the long-range inter information, which is helpful to preserve details. In addition, the pixel-wise loss is combined with perceptual and gradient loss to achieve comprehensive supervision. Experiments on benchmark datasets demonstrate that the proposed method outperforms other SR methods in terms of both objective evaluation and visual effect.

The spatiotemporal variability of vegetation in the Middle East was investigated for the period 2001–2019 using the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day/500 m composites of the Normalized Difference Vegetation Index (NDVI; MOD13A1). The results reveal a strong increase in the NDVI coverage in the Middle East during the study period (R = 0.75, p-value = 0.05). In Egypt, the annual coverage exhibits the strongest positive trend (R = 0.99, p-value = 0.05). In Turkey, both the vegetation coverage and density increased from 2001 to 2019, which can be attributed to the construction of some of the biggest dams in the Middle East, such as the Atatürk and Ilisu dams. Significant increases in the annual coverage and maximum and average NDVI in Saudi Arabia are due to farming in the northern part of the country for which groundwater and desalinated seawater are used. The results of this study suggest that the main factors affecting the vegetation coverage in the Middle East are governmental policies. These policies can have a positive effect on the vegetation coverage in some countries such as Egypt, Saudi Arabia, Qatar, Kuwait, Iran, and Turkey.

ATLID (ATmospheric LIDar) is the atmospheric backscatter LIDAR (Light Detection and Ranging) on board of the EarthCARE (Earth Cloud, Aerosol and Radiation Explorer) mission, the sixth Earth Explorer Mission of the ESA (European Space Agency) Living Planet Programme [1-5]. ATLID’s purpose is to provide vertical profiles of optically thin cloud and aerosol layers, as well as the altitude of cloud boundaries [6-10]. In order to achieve this objective ATLID emits short duration laser pulses in the UV, at a repetition rate of 51 Hz, while pointing in a near nadir direction along track of the satellite trajectory. The atmospheric backscatter signal is then collected by its 620 mm aperture telescope, filtered through the optics of the instrument focal plane assembly, in order to separate and measure the atmospheric Mie and Rayleigh scattering signals. With the completion of the full instrument assembly in 2019, ATLID has been subjected to an ambient performance test campaign, followed by a successful environmental qualification test campaign, including performance calibration and characterization in thermal vacuum conditions. In this paper the design and operational principle of ATLID is recalled and the major performance test results are presented, addressing the main key receiver and emitter characteristics. Finally, the estimated instrument, in-orbit, flight predictions are presented; these indicate compliance of the ALTID instrument performance against its specification and that it will meet its mission science objectives for the EarthCARE mission, to be launched in 2023.

Video super-resolution, which utilizes the relevant information of several low-resolution frames to generate high-resolution images, is a challenging task. One possible solution called sliding window method tries to divide the generation of high-resolution video sequences into independent sub-tasks, and only adjacent low-resolution images are used to estimate the high-resolution version of the central low-resolution image. Another popular method named recurrent algorithm proposes to utilize not only the low-resolution images but also the generated high-resolution images of previous frames to generate the high-resolution image. However, both methods have some unavoidable disadvantages. The former one usually leads to bad temporal consistency and requires higher computational cost while the latter method always can not make full use of information contained by optical flow or any other calculated features. Thus more investigations need to be done to explore the balance between these two methods. In this work, a bidirectional frame recurrent video super-resolution method is proposed. To be specific, a reverse training is proposed that the generated high-resolution frame is also utilized to help estimate the high-resolution version of the former frame. With the contribution of reverse training and the forward training, the idea of bidirectional recurrent method not only guarantees the temporal consistency but also make full use of the adjacent information due to the bidirectional training operation while the computational cost is acceptable. Experimental results demonstrate that the bidirectional super-resolution framework gives remarkable performance that it solves the time-related problems when the generated high-resolution image is impressive compared with recurrent-based video super-resolution method.

The Capon algorithm can be applied to reprocess SAR images, resulting in super-resolution super-resolution reconstructed scenes with lower sidelobe levels. Base on this idea, we have proposed a processing chain of Super-Resolution Persistent Scatterer Interferometry (SR-PSI), to increase PS density. In this paper, we review the main aspect of SR-PSI. We also propose a revised robust Capon algorithm. The result of real-life Sentinel-1 (S-1) data is shown.

The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super-Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as data augmentation better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly from a personal laptop.

The use of Light Amplification by Stimulated Emission of Radiation (i.e., LASERs or lasers) by the U.S. Department of Defense is not new and includes laser weapons guidance, laser-aided measurements, even lasers as weapons (e.g., Airborne Laser). Lasers in support of telecommunications is also not new. The use of laser light in fiber optics shattered thoughts on communications bandwidth and throughput. Even the use of lasers in space is no longer new. Lasers are being used for satellite-to-satellite crosslinking. Laser communication can transmit orders-of-magnitude more data using orders-of-magnitude less power and can do so with minimal risk of exposure to the sending and receiving terminals. What is new is using lasers as the uplink and downlink between the terrestrial segment and the space segment of satellite systems. More so, the use of lasers to transmit and receive data between moving terrestrial segments (e.g., ships at sea, airplanes in flight) and geosynchronous satellites is burgeoning. This manuscript examines the technological maturation of employing lasers as the signal carrier for satellite communications linking terrestrial and space systems. The purpose of the manuscript is to develop key performance parameters (KPPs) to inform U.S. Department of Defense initial capabilities documents (ICDs) for near-future satellite acquisition and development. By appreciating the history and technological challenges of employing lasers rather than traditional radio frequency sources for satellite uplink and downlink signal carrier, this manuscript recommends ways for the U.S. Department of Defense to employ lasers to transmit and receive high bandwidth, large-throughput data from moving platforms that need to retain low probabilities of detection, intercept, and exploitation (e.g., carrier battle group transiting to a hostile area of operations, unmanned aerial vehicle collecting over adversary areas). The manuscript also intends to identify commercial sector early-adopter fields and those fields likely to adapt to laser employment for transmission and receipt.