High-resolution Reconstruction Research Articles

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

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

A high contrast and resolution reconstruction algorithm in quantitative photoacoustic tomography

A framework for reconstruction of optical diffusion and absorption coefficients in quantitative photoacoustic tomography is presented. This framework is based on a Tikhonov-type functional with a regularization term promoting sparsity of the absorption coefficient and a prior involving a Kubelka–Munk absorption–diffusion relation that allows to obtain superior reconstructions. The reconstruction problem is formulated as the minimization of this functional subject to the differential constraint given by a photon-propagation model. The solution of this problem is obtained by a fast and robust sequential quadratic hamiltonian algorithm based on the Pontryagin maximum principle. Results of several numerical experiments demonstrate that the proposed computational strategy is able to obtain reconstructions of the optical coefficients with high contrast and resolution for a wide variety of objects.

Asteroid-NeRF: A deep-learning method for 3D surface reconstruction of asteroids

Asteroids preserve important information about the origin and evolution of the Solar System. Three-dimensional (3D) surface models of asteroids are essential for exploration missions and scientific research. Regular methods for 3D surface reconstruction of asteroids, such as stereo-photogrammetry (SPG), usually struggle to reconstruct textureless areas and can only generate sparse surface models. Stereo-photoclinometry (SPC) can reconstruct pixel-wise topographic details but its performance depends on the availability of images obtained under different illumination conditions and suffers from uncertainties related to surface reflectance and albedo. This paper presents Asteroid-NeRF, a novel deep-learning method for 3D surface reconstruction of asteroids that is based on the state-of-the-art neural radiance field (NeRF) method. Asteroid-NeRF uses a signed distance field (SDF) to reconstruct a 3D surface model from multi-view posed images of an asteroid. In addition, Asteroid-NeRF incorporates appearance embedding to adapt to different illumination conditions and to maintain the geometric consistency of a reconstructed surface, allowing it to deal with the different solar angles and exposure conditions commonly seen in asteroid images. Moreover, Asteroid-NeRF incorporates multi-view photometric consistency to constrain the SDF, enabling optimised reconstruction. Experimental evaluations using actual images of asteroids Itokawa and Bennu demonstrate the promising performance of Asteroid-NeRF, complementing traditional methods such as SPG and SPC. Furthermore, due to the global consistency and pixel-wise training of Asteroid-NeRF, it produces highly detailed surface reconstructions. Asteroid-NeRF offers a new and effective solution for high-resolution 3D surface reconstruction of asteroids that will aid future exploratory missions and scientific research on asteroids.

Mitigating the Blurring Effect of CryoEM Averaging on a Flexible and Highly Symmetric Protein Complex through Sub-Particle Reconstruction.

Many macromolecules are inherently flexible as a feature of their structure and function. During single-particle CryoEM processing, flexible protein regions can be detrimental to high-resolution reconstruction as signals from thousands of particles are averaged together. This "blurring" effect can be difficult to overcome and is possibly more pronounced when averaging highly symmetric complexes. Approaches to mitigating flexibility during CryoEM processing are becoming increasingly critical as the technique advances and is applied to more dynamic proteins and complexes. Here, we detail the use of sub-particle averaging and signal subtraction techniques to precisely target and resolve flexible DARPin protein attachments on a designed tetrahedrally symmetric protein scaffold called DARP14. Particles are first aligned as full complexes, and then the symmetry is reduced by alignment and focused refinement of the constituent subunits. The final reconstructions we obtained were vastly improved over the fully symmetric reconstructions, with observable secondary structure and side-chain placement. Additionally, we were also able to reconstruct the core region of the scaffold to 2.7 Å. The data processing protocol outlined here is applicable to other dynamic and symmetric protein complexes, and our improved maps could allow for new structure-guided variant designs of DARP14.

Changes in monsoon precipitation in East Asia under a 2°C interglacial warming.

Past intervals of warming provide the unique opportunity to observe how the East Asia monsoon precipitation response happened in a warming world. However, the available evaluations are primarily limited to the last glacial-to-interglacial warming, which has fundamental differences from the current interglacial warming, particularly in changes in ice volume. Comparative paleoclimate studies of earlier warm interglacial periods can provide more realistic analogs. Here, we present high-resolution quantitative reconstructions of temperature and precipitation from north-central China over the past 800 thousand years. We found that the average precipitation increase, estimated by the interglacial data, was only around one-half of that estimated for the glacial-to-interglacial data, which is attributed to the amplification of climate change by ice volume variations. Analysis of the interglacial data suggests an increase in monsoon precipitation of ~100 mm for a warming level of 2°C on the Chinese Loess Plateau.

A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data

The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research.

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations

Background The accurate provision of weather information holds immense significance to many disciplines. One example corresponds to the field of air traffic management, in which one basis for weather detection is set upon recordings from sparse weather stations on ground. The scarcity of data and their lack of precision poses significant challenges to achieve a detailed description of the atmosphere state at a certain moment in time. Methods In this article, we foster the use of physics-informed neural networks (PINNs), a type of machine learning (ML) architecture which embeds mathematically accurate physics models, to generate high-quality weather information subject to the regularization provided by the Navier-Stokes equations. Results The application of PINNs is oriented to the reconstruction of dense and precise wind and pressure fields in areas where only a few local measurements provided by weather stations are available. Our model does not only disclose and regularize such data, which are potentially corrupted by noise, but is also able to precisely compute wind and pressure in target areas. Conclusions The effect of time and spatial resolution over the capability of the PINN to accurately reconstruct fluid phenomena is thoroughly discussed through a parametric study, concluding that a proper tuning of the neural network’s loss function during training is of utmost importance.

MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction.

Magnetic particle imaging (MPI) is a recently developed, non-invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X-space are two commonly used MPI reconstruction methods, where the former is extremely time-consuming and the latter usually produces blurry images. Currently, we proposed an end-to-end machine learning framework to reconstruct high-resolution MPI images from 1-D voltage signals directly and efficiently. The proposed framework, which we termed "MPIGAN", was trained on a large MPI simulation dataset containing 291597 pairs of high-resolution 2-D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high-resolution MPI image reconstruction. Experiment results showed that, MPIGAN exhibited remarkable abilities in high-resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X-space in recovering the fine-scale structure of magnetic nanoparticles' spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high-quality MPI images. Our study provides a promising AI solution for end-to-end, efficient, and high-resolution magnetic particle imaging reconstruction.

Noise reduction of axial piston motors using multi-objective optimization of reinforcing ribs based on identification of acoustic sources

The optimization of the housing for reducing vibration and noise in axial piston motors is facing challenges related to inaccurate locations of acoustic sources and inappropriate optimization parameters. In this study, a multi-objective optimization of the thicknesses of the reinforcing ribs for reducing the noise of axial piston motors is investigated by using the compression sensing (CS) method and response surface methodology (RSM). Combined with the CS method, the high-resolution reconstruction of sound-intensity images is used to guide the structural optimization areas through locations of acoustic sources. To reduce vibration and noise, the variable rib structure model (VRSM) is established in structural optimization areas. The thicknesses of reinforcing ribs in different areas are considered optimization variables, while the motor housing's peak vibration acceleration obtained by coupling the motor's dynamic model with the hydraulic model is utilized as the objective function. The mathematical relationship between these variables and the objective function is established through the RSM. Utilizing these functions, a multi-objective optimization model is formulated for discretizing the thicknesses of reinforcing ribs to reduce noise, employing a multi-objective genetic algorithm (MOGA) for optimization calculations. Finally, comparing the sound pressure levels of the axial piston motor with and without the optimized reinforcing ribs, the experimental results demonstrate a notable reduction in noise with the optimized reinforcing ribs. Specifically, there's a reduction of 2.7 dB at the peak frequency of 610 Hz.

New Vacuum Solar Telescope Achieves Narrowband Infrared Solar Imaging Observation at He i 10830 Å

The near-infrared imaging channel constitutes a crucial component of the multichannel high-resolution imaging system of the New Vacuum Solar Telescope (NVST). We have successfully achieved high-resolution, narrowband imaging of the chromosphere using He i 10830 Å triplet within this channel, which significantly enhances the imaging observation capabilities of NVST. This paper provides a concise overview of the optical system associated with the near-infrared imaging channel, detailing data processing procedures and presenting several observed images. Leveraging a high-resolution image reconstruction algorithm, we were able to generate a narrowband image near the diffraction limit at 10830 Å with a temporal resolution of less than 10 s.

High Resolution Reconstruction of the Proximal Tubule Apical Endocytic Pathway

Kidney proximal tubule (PT) cells are specialized for effcient apical uptake of albumin and other proteins that escape the glomerular filtration barrier. The apical endocytic pathway is uniquely organized to maintain highly dynamic retrieval of these proteins via binding to the multiligand receptors megalin and cubilin. Ultrastructural studies following the fate of internalized markers have provided a temporal model for the delivery of receptor-bound ligands and fluid phase markers to lysosomes. In this model, internalization from the apical membrane occurs via budding of irregular clathrin-coated invaginations that form at the base of microvilli. After uncoating, budded vesicles fuse with apical early endosomes (AEEs) where acidification triggers dissociation of ligands from their receptors. AEEs further mature and expand into apical vacuoles (AVs) that ultimately deliver ligands and fluid to lysosomes. Receptor recycling to the apical membrane is thought to occur via dense apical tubules (DATs) that have a uniform diameter and membrane thickness and which appear to bud from both AEEs and AVs. We used Zeiss Arivis Pro technology to annotate and reconstruct apical membrane invaginations and endocytic compartments in a 3D volume of mouse kidney with isotropic voxels of 4 nm, acquired using focused ion-beam scanning electron microscopy (FIB-SEM). Strikingly, 3D reconstruction of DATs revealed a large and highly interconnected network of tubules in the subapical region of PT cells. Connections were observed between the DAT network and endosomal compartments, as well as between DATs and apical membrane invaginations. Our data suggest the possibility that DATs exist as a stable compartment that interacts transiently with the apical membrane to enable receptor recycling. Current studies are underway to better understand how PT cells modulate endocytic entry and recycling via the same portals. National Institute of Health: 5T32GM133353-03, R01 DK118726, R01 DK125049, U54 DK137329. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Deep learning-enhanced ghost imaging through dynamic and complex scattering media with supervised corrections of dynamic scaling factors

Ghost imaging (GI) through dynamic and complex scattering media remains challenging. The existence of dynamic scattering gives rise to a failure of GI schemes. Here, we report a deep learning-enhanced GI scheme with supervised corrections (SCGI) of dynamic scaling factors to realize high-resolution ghost reconstruction through dynamic and complex scattering media. The SCGI scheme is developed to approximate the variation of dynamic scaling factors in an optical channel and correct the recorded light intensities with a Gaussian prior. An untrained neural network powered by regularization by denoising for the SCGI scheme (SCGI-URED) is developed to further recover high-visibility ghost images. Experimental results demonstrate that high-resolution and high-visibility GI can be realized in dynamic and complex scattering media. The proposed method provides a reliable tool for implementing high-resolution and high-visibility GI through dynamic and complex scattering media and could give an impetus to developing dynamic scattering imaging in real-world scenarios.

Asian monsoon variations over the past 21 ka: An introduction

The Asian monsoon influences the lives of more than a third of the world's population, and understanding its spatial and temporal variability is fundamental to sustainable development. The complex dynamics of this climatic system are therefore of broad scientific and societal interest. In this virtual special issue (VSI), we present 19 papers dealing with the Asian monsoon since the Last Glacial Maximum (LGM, ∼21 kyr BP), with a focus on understanding variability and forcing mechanisms. Findings use recently developed quantitative reconstructions combined with numerical simulations, and present novel insights as follows: (1) analyses of comprehensive datasets of pollen, diatom, brGDGTs, oxygen and hydrogen isotopes and frequency magnetic susceptibility, as well as an innovative algorithm model for the lake energy-water balance, improve the reliability of quantitative reconstructions of Asian monsoon climate variations; (2) studies of quantitative climate parameters in central East Asia show that mean annual temperature (MAT) was ∼5 °C (or ∼ 7.9 °C using brGDGTs) lower during the LGM, ∼1.5 °C (±41% using brGDGTs) higher around the Holocene thermal optimum (HO) and mean annual precipitation (MAP) varied between 30 and 150% with marked seasonality during 8–5 ka; (3) dynamical downscaling palaeoclimatic simulations improve the accuracy of past monsoon variations, revealing strong variability in monsoon precipitation across the LGM and HO. The holistic study of Asian monsoon variations at orbital to centennial timescales in these VSI papers presents reliable observations of monsoon variability and its driving mechanisms, providing high-resolution quantitative paleoclimate reconstructions that fill existing gaps in spatial coverage, as well as offering an improved understanding, thorough numerical simulation, of coupled sea-level change and vegetation feedbacks.

Physics-informed deep-learning applications to experimental fluid mechanics

High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. mass and momentum conservation. Physics-informed deep learning provides frameworks for integrating data and physical laws for learning. In this study, we apply physics-informed neural networks (PINNs) for super-resolution of flow-field data both in time and space from a limited set of noisy measurements without having any high-resolution reference data. Our objective is to obtain a continuous solution of the problem, providing a physically-consistent prediction at any point in the solution domain. We demonstrate the applicability of PINNs for the super-resolution of flow-field data in time and space through three canonical cases: Burgers’ equation, two-dimensional vortex shedding behind a circular cylinder and the minimal turbulent channel flow. The robustness of the models is also investigated by adding synthetic Gaussian noise. Furthermore, we show the capabilities of PINNs to improve the resolution and reduce the noise in a real experimental dataset consisting of hot-wire-anemometry measurements. Our results show the adequate capabilities of PINNs in the context of data augmentation for experiments in fluid mechanics.

Reactive UAV-based automatic tunnel surface defect inspection with a field test

This work addresses the problem of automatic tunnel surface defect inspection using unmanned aerial vehicles (UAVs). The research aims at proposing a robust and efficient monitoring method for image data acquisition and processing in complex and dark tunnel environments. A method, called Proximity Move-Pause-Photo for Surface Defect Inspection (PMPP-SDI), is proposed by combining reactive flying control strategies with a grid scanning pattern to capture high-quality image data from multiple views and angles. The image data is then used to generate a 3D point cloud model of the tunnel surface for structural condition assessment. The method is tested in a field experiment in a railway tunnel in Ireland, and the results show that it can achieve stable navigation, high-resolution reconstruction, and accurate defect detection. The paper discusses the advantages and limitations of the method, and suggests improving the control/navigation intelligence, data quality, and defect analysis as the future research directions.

Dual-branch feature encoding framework for infrared images super-resolution reconstruction

Infrared thermal imaging is a passive non-contact detection and identification technology, which is not subject to electromagnetic infection and good concealment, is widely used in military and commercial fields. However, due to the limitations of the existing infrared imaging system mechanisms, the spatial resolution of the acquired infrared images is low and the edge details are blurred, which in turn leads to poor performance in downstream missions based on infrared images. In this paper, in order to better solve the above problems, we propose a new super-resolution reconstruction framework for infrared images, called DBFE, which extracts and retains abundant structure and textual information for robust infrared image high-resolution reconstruction with a novel structure-textual encoder module. Extensive experiment demonstrates that our proposed method achieves significantly superior contraband high-resolution reconstruction results on the multiple dataset compared to progressive methods for high resolution infrared image reconstruction, effectively proving the practicability of the method proposed in this paper.

Automated Camera Pose Generation for High-Resolution 3D Reconstruction of Bridges by Unmanned Aerial Vehicles

This work explores the possibility of automating the aerial survey of bridges to generate high-resolution images necessary for digital damage inspection. High-quality unmanned aerial vehicle (UAV) based 3D reconstruction of bridges is an important step towards autonomous infrastructure inspection. However, the calculation of optimal camera poses remains challenging due to the complex structure of bridges and is therefore often conducted manually. This process is time-consuming and can lead to quality losses. Research in this field to automate this process is yet sparse and often requires high informative models of the bridge as the base for calculations, which are not given widely. Therefore, this paper proposes an automated camera pose calculation method solely based on an easily accessible polygon mesh of the bridge. For safe operation, point cloud data of the environment are used for automated ground detection and obstacle avoidance including vegetation. First, an initial set of camera poses is generated based on a voxelized mesh created in respect to the quality requirements for 3D reconstruction using defined camera specification. Thereafter, camera poses not fulfilling safety distances are removed and specific camera poses are added to increase local coverage quality. Evaluations of three bridges show that for diverse bridge types, near-complete coverage was achieved. Due to the low computational effort of the voxel approach, the runtime was kept to a minimum, even for large bridges. The subsequent algorithm is able to find alternative camera poses even in areas where the optimal pose could not be placed due to obstacles.

Simplified matching pursuits applied to 3D nuclear reactor temperature distribution construction

Autonomous control systems can enhance the economic viability of complex systems such as nuclear microreactors. Advanced forms of these control systems benefit from techniques for high-resolution field data reconstruction from sensor data. Basis projection methods present a useful set of methodologies for nonparametric reconstruction of these field data. In this work, high fidelity multiphysics simulations are used to generate microreactor temperature distributions that are then used to evaluate the viability of a basis projection method which employs a generalized set of basis functions, or dictionaries. Additionally, metrics for evaluating the accuracy of the temperature distribution are presented that are motivated both from the fields of traditional signal processing and statistics. The performance of a method based on matching pursuits is compared to a method that uses the sequential ordering of basis functions. The matching pursuits-based method gives improved performance for highly sparse representations of temperature distributions. Additionally, accuracy is analyzed for multiple temperature distributions which represent different potential reactor operation configurations. Overall, the results from this work should be considered as an upper bound on the performance of basis projection methods which employ generalized basis functions for real time reactor control.

Reconstruction of warm-season temperatures in central Europe during the past 60 000 years from lacustrine branched glycerol dialkyl glycerol tetraethers (brGDGTs)

Abstract. Millennial-scale climate variations during the last glacial period, such as Dansgaard–Oeschger (DO) cycles and Heinrich events, have been extensively studied using ice core and marine proxy records. However, there is a limited understanding of the magnitude of these temperature fluctuations in continental regions, and questions remain about the seasonal signal of these climate events. This study presents a 60 000-year-long temperature reconstruction based on branched glycerol dialkyl glycerol tetraethers (brGDGTs) extracted from lake sediments from the Eifel Volcanic Field, Germany. brGDGTs are bacterial membrane-spanning lipids that are known to have a strong relationship with temperature, making them suitable for temperature reconstructions. We test several temperature calibration models on modern samples taken from soils and multiple maar lakes. We find a negative bias in brGDGT-based temperature estimates associated with water depth and anoxic conditions that can be corrected for by accounting for a brGDGT isomer that is only produced in anoxic conditions. The corrected temperature reconstruction correlates with proxy and climate model estimates of temperature spanning the same time period, validating the calibration approach we selected. However, millennial-scale variability is significantly dampened in the brGDGT record, and in contrast to other Northern Hemisphere climate records, during several Heinrich stadials, temperatures actually increase. We demonstrate that these apparent discrepancies can be explained by the unique seasonal response of the brGDGT paleothermometer to temperatures of months above freezing (TMAF). Our data support the view that warm-season temperatures in Europe varied minimally during the last glacial period and that abrupt millennial-scale events were defined by colder, longer winters. Our continuous high-resolution temperature reconstruction provides important information about the magnitude of seasonal climate variability during the last glacial period that can be used to test climate models and inform studies of paleoecological change.