High-resolution Reconstruction Research Articles

Segmentation-Aided Upsampling for High-Resolution Reconstruction of 3-D MMW Images

Segmentation-Aided Upsampling for High-Resolution Reconstruction of 3-D MMW Images

High-resolution multimodal profiling of human epileptic brain activity via explanted depth electrodes.

The availability and integration of electrophysiological and molecular data from the living brain is critical to understand and diagnose complex human disease. Intracranial stereo electroencephalography (SEEG) electrodes used for identifying the seizure focus on epilepsy patients could enable the integration of such multimodal data. Here, we report MoPEDE (Multimodal Profiling of Epileptic Brain Activity via Explanted Depth Electrodes), a method that recovers extensive protein-coding transcripts, including cell-type markers, DNA methylation and short variant profiles from explanted SEEG electrodes matched with electrophysiological and radiological data allowing for high-resolution reconstructions of brain structure and function. We find gene expression gradients that correspond with the neurophysiology-assigned epileptogenicity index but also outlier molecular fingerprints in some electrodes, potentially indicating seizure generation or propagation zones not detected during electroclinical assessments. Additionally, we identify DNA methylation profiles indicative of transcriptionally permissive or restrictive chromatin states and SEEG-adherent differentially expressed and methylated genes not previously associated with epilepsy. Together, these findings validate that RNA profiles and genome-wide epigenetic data from explanted SEEG electrodes offer high-resolution surrogate molecular landscapes of brain activity. The MoPEDE approach has the potential to enhance diagnostic decisions and deepen our understanding of epileptogenic network processes in the human brain.

Asymmetric Large Kernel Distillation Network for efficient single image super-resolution

Recently, significant advancements have been made in the field of efficient single-image super-resolution, primarily driven by the innovative concept of information distillation. This method adeptly leverages multi-level features to facilitate high-resolution image reconstruction, allowing for enhanced detail and clarity. However, many existing approaches predominantly emphasize the enhancement of distilled features, often overlooking the critical aspect of improving the feature extraction capabilities of the distillation module itself. In this paper, we address this limitation by introducing an asymmetric large-kernel convolution design. By increasing the size of the convolution kernel, we expand the receptive field, which enables the model to more effectively capture long-range dependencies among image pixels. This enhancement significantly improves the model's perceptual ability, leading to more accurate reconstructions. To maintain a manageable level of model complexity, we adopt a lightweight architecture that employs asymmetric convolution techniques. Building on this foundation, we propose the Lightweight Asymmetric Large Kernel Distillation Network (ALKDNet). Comprehensive experiments conducted on five widely recognized benchmark datasets-Set5, Set14, BSD100, Urban100, and Manga109-indicate that ALKDNet not only preserves efficiency but also demonstrates performance enhancements relative to existing super-resolution methods. The average PSNR and SSIM values show improvements of 0.10 dB and 0.0013, respectively, thereby achieving state-of-the art performance.

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. The results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

High-Resolution Reconstruction of Oxidation–Reduction Conditions: Raman Spectroscopy and μ-XRF Analysis of Manganese Nodule and Crust on Tabletop of Western Pacific Magellan Seamounts

Ferromanganese (Fe-Mn) deposits are widely used in paleoenvironmental reconstructions owing to their mineralogical and geochemical properties. We analyzed Fe-Mn deposits using micro-X-ray fluorescence and Raman spectroscopy to study the paleo-ocean environment. Samples were collected from the OSM-XX seamount in the western Pacific. The Fe-Mn crust was divided into three parts: phosphatized, massive non-phosphatized, and porous non-phosphatized. Vernadite was identified in all layers. Furthermore, in the nodule, high values of Mn, Ni, and Cu were observed near the nucleus, with vernadite and todorokite, and these values decreased outward. A high Mn/Fe ratio near the nucleus indicates early diagenetic processes. Formation of Fe-Mn nodules began around 19–16 Ma, and this period corresponded to a minor phosphatization event and persistent reducing conditions. From 11–10 Ma, the Mn/Fe and Co/Mn ratios decreased due to the formation of a western Pacific warm pool during this period. Subsequently, with the opening of the Indonesian seaway and global cooling, the Mn/Fe and Co/Mn ratios in the Fe-Mn deposits increased again. The comparative analysis of variations in Mn/Fe ratio and vernadite crystallinity in the Fe-Mn deposits confirmed that it is possible to reconstruct paleo-productivity and redox condition changes in the western Pacific Magellan Seamount.

Generative 3D Reconstruction of Martian Surfaces using Monocular Images

Abstract. High-resolution digital terrain models (DEM) are crucial for Mars scientific exploration and engineering demands, such as mission route planning, landing site selection, and topography research. However, DEMs generated based on photogrammetry or photometry suffer from limited stereo coverage and complex generative processes. Therefore, we propose a GAN-based monocular 3D reconstruction method, which utilizes high-resolution monocular orbital images to achieve pixel-level Martian terrain reconstruction. We preprocess the image data and then invert the elevation using the trained generative model. Finally, we recover the absolute scale through post-processing. In this work, we use HiRISE orthorectified images and DEMs with a resolution of 2 m and 0.25 m to validate the effectiveness of our method. We evaluated our method in four areas with different landforms and found that the predicted DEM and HiRISE DEM have height consistency with the root mean square error of about 2 m. This indicates that the proposed GAN-based method has certain effectiveness and generalization and great potential in high-resolution monocular Martian DEM reconstruction.

Advances in giant clam (Tridacnidae spp.) sclerochronology and sclerochemistry

Understanding past environmental change through high-resolution palaeoenvironmental proxy reconstructions provides crucial baselines for understanding global environmental changes, particularly during the Quaternary Period. Such data are crucial for testing and refining climate models to enable better adaptations to current and future climate changes. Giant clams (Tridacnidae spp.), the largest bivalve mollusc in the Indo-Pacific region, possess distinctive features such as long lifespan, rapid growth, high-resolution growth patterns, wide distribution habitat, and stable shell geochemical composition. These attributes make giant clams a promising and dependable novel palaeoenvironmental archive, offering numerous available geochemical proxies. In this review, we examined preview studies on giant clams conducted for sclerochronology research, spanning publications from 1986 to 2023. The previous studies have proven that isotopic and trace elements proxies of the giant clam, including δ18O, δ13C, Sr/Ca, Mg/Ca, Ba/Ca, and Fe/Ca, can faithfully reconstruct palaeoclimate records including sea surface temperature, dissolved inorganic carbon, insolation, extreme weather events and primary productivity at ultra-high resolution. These proxies have been successfully employed for palaeoclimatic and palaeoenvironmental reconstructions from the Miocene to the present across a range of locations from South Japan to Northern Australia to the Red Sea. These studies have illuminated changes in interannual climate cycles, climate variation, and human migrations over thousands to millions of years. They have generated quantitative records that are valuable for testing and refining numerical climate models, understanding the relationship between past climate cycles and future climate variability, and providing insights into human-environment interactions over time. In the end, the review also addresses the challenges and provided recommendations for future research, highlighting persisting gaps in understanding the influence of microstructure on the shell, age dating of fossil samples, uncertainty in proxies, the need for tank experiments, and access to ultra-high resolution palaeoenvironmental records.

Enhancing high-resolution reconstruction of flow fields using physics-informed diffusion model with probability flow sampling

The application of artificial intelligence (AI) technology in fluid dynamics is becoming increasingly prevalent, particularly in accelerating the solution of partial differential equations and predicting complex flow fields. Researchers have extensively explored deep learning algorithms for flow field super-resolution reconstruction. However, purely data-driven deep learning models in this domain face numerous challenges. These include susceptibility to variations in data distribution during model training and a lack of physical and mathematical interpretability in the predictions. These issues significantly impact the effectiveness of the models in practical applications, especially when input data exhibit irregular distributions and noise. In recent years, the rapid development of generative artificial intelligence and physics-informed deep learning algorithms has created significant opportunities for complex physical simulations. This paper proposes a novel approach that combines diffusion models with physical constraint information. By integrating physical equation constraints into the training process of diffusion models, this method achieves high-fidelity flow field reconstruction from low-resolution inputs. Thus, it not only leverages the advantages of diffusion models but also enhances the interpretability of the models. Experimental results demonstrate that, compared to traditional methods, our approach excels in generating high-resolution flow fields with enhanced detail and physical consistency. This advancement provides new insights into developing more accurate and generalized flow field reconstruction models.

A Feature-Driven Inception Dilated Network for Infrared Image Super-Resolution Reconstruction

Image super-resolution (SR) algorithms based on deep learning yield good visual performances on visible images. Due to the blurred edges and low contrast of infrared (IR) images, methods transferred directly from visible images to IR images have a poor performance and ignore the demands of downstream detection tasks. Therefore, an Inception Dilated Super-Resolution (IDSR) network with multiple branches is proposed. A dilated convolutional branch captures high-frequency information to reconstruct edge details, while a non-local operation branch captures long-range dependencies between any two positions to maintain the global structure. Furthermore, deformable convolution is utilized to fuse features extracted from different branches, enabling adaptation to targets of various shapes. To enhance the detection performance of low-resolution (LR) images, we crop the images into patches based on target labels before feeding them to the network. This allows the network to focus on learning the reconstruction of the target areas only, reducing the interference of background areas in the target areas’ reconstruction. Additionally, a feature-driven module is cascaded at the end of the IDSR network to guide the high-resolution (HR) image reconstruction with feature prior information from a detection backbone. This method has been tested on the FLIR Thermal Dataset and the M3FD Dataset and compared with five mainstream SR algorithms. The final results demonstrate that our method effectively maintains image texture details. More importantly, our method achieves 80.55% mAP, outperforming other methods on FLIR Dataset detection accuracy, and with 74.7% mAP outperforms other methods on M3FD Dataset detection accuracy.

Subsistence strategies and climate change as key factors in the vicinity of the Great Wall in northern China from 500 BCE

Whether, and in what ways, the association between changes in climate and the shifting of imperial frontiers (i.e. the Great Wall) could be mediated by the social responses of major ethnic groups is a question that remains unanswered. Ulanqab is a region in Inner Mongolia associated with a long history of Great Wall construction from the Warring States to the Ming dynasties (~500 BCE–1644 CE) and the Great Wall is often regarded as the boundary of the Central Plains dynasty. This paper uses Ulanqab as a case study to investigate the historical inter-relationship between changes in climate and subsistence strategies on the one hand, and the positioning of the Great Wall on the other. We determined the precise geographical coordinates of the Great Wall in this region during each dynastic period of Chinese history, and compared its location to (1) the empire’s boundaries in each dynasty, (2) evidence that is available on climate change from high-resolution paleo-climate reconstructions and (3) subsistence strategies (whether agriculture, pastoralism, or nomadism) that people adopted as recorded in historical documents. Geo-referenced information from associated archaeological sites was also used to determine the extent of habitation in different areas. We found that during the period of agriculturalist empires (the Han and Ming dynasties), the Great Wall was a frontier between farming and animal husbandry, and was located to the south of the Daqing Mountains. In contrast, during the period of pastoralist empires (the Zhao Kingdom [of the Warring States], Northern Wei and Jurched Jin periods), the Great Wall was the frontier between animal husbandry and nomadism, and was located to the north of the Daqing Mountains. These mountains (42.5°N) seem to have been the northern limit of human settlements that were based on animal husbandry and farming. We observed that, when precipitation and temperature increased, this resulted in an augmentation of agricultural production that most likely facilitated the northward expansion of the agriculturalist peoples. A decrease in precipitation, on the other hand, resulted in the shrinkage of pastures and food, triggering a southward expansion that was characteristic of the pastoralist peoples. This study attempts to provide new insights into the interrelationships among changes in climate, human societies and geopolitics in Chinese history.

Impact of neonatal hyperoxia on the development of the coronary vascular bed: an experimental model for the study of cardiomyopathy associated with premature birth

Abstract Introduction Very preterm birth (PT; <32 weeks of gestation), nearly 2% of all births, leads to short-term complications such as bronchopulmonary dysplasia and retinopathy of prematurity that are driven by compromised organ vascular development. Adults born preterm display an increased risk of ischaemic cardiomyopathy and heart failure. Our group has shown that neonatal rats exposed to hyperoxia, simulating the PT neonatal conditions, develop cardiomyocytes hypertrophy, fibrosis and cardiac dysfunction, or Oxygen-Induced Cardiomyopathy (OIC). Whether cardiac changes also associate with alterations in vascular development in the left ventricle (LV) remains unknown. Purpose We hypothesized that neonatal exposure to hyperoxia impact the development of the LV vascular network. Methods Male pups were exposed to 80% O2 (OIC) or room air (CTRL) from day 3 (P3) to P10 of life. Hearts were collected at P10, 4- and 16 weeks. Arteriolar length and capillary density per cardiomyocyte were measured using stereology (elastin) and immunofluorescence (WGA, α-SMA, CD31). High-resolution 3D reconstruction of the vascular network was achieved by combining fluorescent labelling of coronaries (anti-α-SMA) and capillaries (anti-CD31) with a tissue-cleaning protocol (iDisco+), allowing detailed morphometric characterization of the vascular network. The expression of vascular endothelial growth factors and receptors (VEGFA, VEGFB, VEGFR 1 and 2, NRP1, and PIGF) were quantified by RT-qPCR and Western blots. Differences between groups were analyzed with Student's t-test (P < 0.05) (N = 6 per group). Results At P10, we observed a notable rise in the number of capillaries per cardiomyocyte (0.88 ± 0.05 vs. 0.62 ± 0.02 mm2) and a significant increase in VEGFA (+76%) and NRP1 (+38%) expression in the LV from OIC vs. CTRL rats. By 4 weeks, the OIC group showed a significant reduction in arteriolar length vs. CTRL (6.51 ± 0.76 vs. 9.77 ± 1.12 m/cm3), coupled with an increase in the number of capillaries per cardiomyocyte (1.11 ± 0.05 vs. 0.85 ± 0.03 mm2). VEGFA was decreased (-14%) and PLGF increased (+24%) in the LV of OIC rats. At 16 weeks, the OIC condition displayed a significant increase in the number of capillaries per cardiomyocyte (1.37 ± 0.03 vs. 0.98 ± 0.07 mm2) and a decrease in VEGFA (-23%), along with reduction in its receptors VEGFR1 (-27%), VEGFR2 (-20%) and NRP1 (-17%). Conclusion OIC resulting from neonatal hyperoxia exposure associates with vascular changes in the LV that persist to adulthood. These changes are characterized by a reduction in arteriolar length and an increase in the number of capillaries, accompanied by modulation of the expression of pro-angiogenic VEGFA and receptors. The impact of these vascular alterations on the susceptibility of the LV to ischemic or hypertension-related damage is to be explored. These findings suggest a crucial mechanistic pathway for understanding the risk of cardiac pathologies associated with preterm birth.

Coronary CT angiography using photon counting CT for the detection of coronary artery stenoses and coronary atherosclerotic plaque: comparison of reconstructions with 0.2 mm and 0.4 mm slice thickness

Abstract Photon Counting CT allows the acquisition of high-resolution data sets with 0.2 mm reconstructed slice thickness. It is unknown whether the detection of coronary artery stenoses, of coronary plaque or of vulnerable plaque characteristics is improved in data sets that employ maximum resolution. We therefore compared the identification of above characteristics in data sets reconstructed with 0.2 mm and 0.4 mm slice thickness In 30 patients with suspected coronary artery disease, sinus rhythm, a heart rate ≤65 beats/min, and body weight ≤ 100 kg, high-resolution data sets were acquired by photon counting CT (60 ml contrast i.v. at 6 ml/s) after s.c. application of nitrates in inspiratory breathhold. Data were reconstructed with 0.2 mm slice thickness at 0.1 mm increment and 0.4 mm slice thickness at 0.2 mm increment. Next to quantitative analysis of image quality parameters, all data sets were evaluated by two independent expert reviewers concerning subjective image quality as well as the ability to determine the presence of coronary artery stenoses, calcified and non- calcified plaque, as well as parameters of plaque vulnerability (positive remodeling, necrotic core, density < 30 HU and napkin-ring sign) using a 5-point Likert scale (-2 to +2) and a dedicated 11-segment coronary model. Of the 30 patients, 47% were male (mean age 58±12 years, mean heart rate 53±6 bpm, mean body weight 80±15 kg). The median dose length product was 508 mGy*cm. Mean image noise was 42±7 HU vs. 34±7 HU for 0.2 mm vs. 0.4 mm reconstructions (p < 0.001). The median image quality score was significantly higher for 0.2 mm reconstructions (3.8±0.8 vs 3.6±0.7). Of 335 coronary segments, none were classified as "unevaluable" while 87% vs. 76% were classified as "perfect" in 0.2 mm vs. 0.4 mm reconstructions (p < 0.001). Full confidence to rule-in or rule out significant coronary stenoses was achieved in 93% of segments in both reconstructions. The number of non-calcified plaques identified in 0.2 mm reconstructions was numerically higher than in 0.4 mm reconstructions (77 vs. 71, p = n.s.). On the Likert scale, the confidence to identify parameters of plaque vulnerability was significantly higher for 0.2 mm reconstructions (1.93 vs. 1.85, p < 0.001). In a selected patient group, photon counting CT coronary angiography demonstrated excellent image quality and an extremely low rate of unevaluable coronary segments. High-resolution reconstructions improve subjective image quality as was as the assessment of vulnerable plaque characteristics.

Overcoming artificial structures in resolution-enhanced Hi-C data by signal decomposition and multi-scale attention.

Computational enhancement is an important strategy for inferring high-resolution features from genome-wide chromosome conformation capture (Hi-C) data, which typically have limited resolution. Deep learning has been highly successful in this task but we show that it creates prevalent artificial structures in the enhanced data due to the need to divide the large contact matrix into small patches. In addition, previous deep learning methods largely focus on local patterns, which cannot fully capture the complexity of Hi-C data. Here we propose Smooth, High-resolution, and Accurate Reconstruction of Patterns (SHARP) for enhancing Hi-C data. It uses the novel approach of decomposing the data into three types of signals, due to one-dimensional proximity, contiguous domains, and other fine structures, and applies deep learning only to the third type of signals, such that enhancement of the first two is unaffected by the patches. For the deep learning part, SHARP uses both local and global attention mechanisms to capture multi-scale contextual information. We compare SHARP with state-of-the-art methods extensively, including application to data from new samples and another species, and show that SHARP has superior performance in terms of resolution enhancement accuracy, avoiding creation of artificial structures, identifying significant interactions, and enrichment in chromatin states.

An adaptive parameter decoupling algorithm-based image reconstruction model (ADAIR) for rapid golden-angle radial DCE-MRI

Objective. The acceleration of magnetic resonance imaging (MRI) acquisition is crucial for both clinical and research applications, particularly in dynamic MRI. Existing compressed sensing (CS) methods, despite being effective for fast imaging, face limitations such as the need for incoherent sampling and residual noise, which restrict their practical use for rapid MRI.Approach. To overcome these challenges, we propose a novel image reconstruction framework that integrates the MRI physical model with a flexible, self-adjusting, decoupling data-driven model. We validated this method through experiments using both simulated andin vivodynamic contrast-enhanced MRI datasets.Main results. The experimental results demonstrate that the proposed framework achieves high spatial and temporal resolution reconstructions. Additionally, when compared to state-of-the-art image reconstruction approaches, our method significantly enhances acceleration capabilities, enabling sparse and rapid imaging with high resolution.Significance. Our proposed framework offers a promising solution for real-time imaging and image-guided radiation therapy applications by providing superior performance in achieving high spatial and temporal resolution reconstructions, thus addressing the limitations of existing CS schemes.

Palaeodrainages of the Sunda Shelf detailed in new maps

The Sunda Shelf is a prime biodiversity hotspot where some of the planet's most endemic species can be found. Much of the diversity in this important bioregion has been shaped by sea-level fluctuations that took place during the Pleistocene. Using depth contours obtained from the General Bathymetric Chart of the Oceans Grid 2023, we provide high-resolution reconstruction of land areas and palaeo-drainages on the Sunda Shelf at the 50, 75, 100, and 120 m isobaths, as well as palaeo-catchments at the 120 m isobath, in order to elucidate the history of this region. The maps presented here aim to reconstruct the connection between palaeo-rivers in the Sunda Shelf and contemporary rivers and identify possible dispersal routes and barriers for the floras and faunas/biotas associated with riverine systems found here.

Real-time equilibrium reconstruction by multi-task learning neural network based on HL-3 tokamak

Abstract A neural network model, EFITNN, has been developed capable of real-time magnetic equilibrium reconstruction based on HL-3 tokamak magnetic measurement signals. The model processes inputs from 68 channels of magnetic measurement data gathered from 1159 HL-3 experimental discharges, including plasmas current, loop voltage, and the poloidal magnetic fields measured by probes. The outputs of the model feature eight key plasmas parameters, alongside high-resolution ( 129 × 129 ) reconstructions of the toroidal current density J P and poloidal magnetic flux distributions Ψ rz . Moreover, the network’s architecture employs a multi-task learning structure, which enables the sharing of weights and mutual correction among different outputs, and leads to increase the model’s accuracy by up to 32%. The performance of EFITNN demonstrates remarkable consistency with the offline EFIT, achieving average R 2 = 0.941, 0.997 and 0.959 for eight plasmas parameters, Ψ rz and J P , respectively. The model’s robust generalization capabilities are particularly evident in its successful predictions of quasi-snowflake (QSF) divertor configurations and its adept handling of data from shot numbers or plasmas current intervals not previously encountered during training. Compared to numerical methods, EFITNN significantly enhances computational efficiency with average computation time ranging from 0.08 ms to 0.45 ms, indicating its potential utility in real-time isoflux control and plasmas profile management.

High-Resolution Reconstruction of Temperature Fields Based on Improved ResNet18.

High-precision measurement of temperature value distributions in production scenarios is of great significance for industrial production, but traditional temperature field reconstruction algorithms rely on the design of manual feature extraction methods with high computational complexity and poor generalization ability. In this paper, we propose a high-precision temperature field reconstruction algorithm based on deep learning, using an efficient adaptive feature extraction method for temperature field reconstruction. We design an improved temperature field reconstruction algorithm based on the ResNet18 neural network; introduce the CBAM attention mechanism in the model; and design a feature pyramid, using M-FPN, a multi-scale feature aggregation network fusing PAN and FPN, to make the extracted feature information propagate multi-dimensionally among different layers to improve the feature characterization ability. Finally, the mean square error is used to guide the model to optimize the training so that the model pays more attention to the data and reduces the large error to ensure that the gap between the predicted value and the real value is small. The experimental results show that the reconstruction accuracy of the improved algorithm presented in this paper is significantly better than that of the original algorithm in the case of typical peaked temperature field distributions.

An approach for coherent periodogram averaging of tilt-series data for improved CTF estimation.

Cryo-electron microscopy (cryo-EM) has become an indispensable technique for determining three-dimensional structures of biological macromolecules. A critical aspect of achieving high-resolution cryo-EM reconstructions is accurately determining and correcting for the microscope's contrast transfer function (CTF). The CTF introduces defocus-dependent distortions during imaging; if not properly accounted for, the CTF can distort features in and limit the resolution of 3D reconstructions. For tilt-series data used in cryo-electron tomography (cryo-ET), CTF estimation becomes even more challenging due to the tilt of the specimen, which introduces a defocus gradient across the field of view, as well as the low dose and signal in individual tilt images. Here, we describe a simple algorithm to improve the accuracy of CTF estimation of tilted images by leveraging the tilt-series alignment parameters determined for tomographic reconstruction to explicitly account for the tilted specimen geometry. In brief, each tilt image is divided into patches, each of which are then stretched according to their defocus shift. These are then summed to provide a coherent power spectra at the tilt axis, which can then be used in standard CTF estimation algorithms. This uses all the data in each image to enhance the visibility of Thon rings, thereby improving high-resolution CTF estimation and subsequent enhancements in the resolution of subtomogram averages.

Microplastics deposition in Arctic sediments of Greenland increases significantly after 1950

Marine sediments are archives of environmental change with pollutants potentially acting as chronographic markers of the Anthropocene. Particularly, the vertical transport and burial, as well as slow degradation rate of microplastics, indicate an eventual incorporation into the geological record. A high-resolution reconstruction of microplastics records requires high sedimentation rates, and in Disko Bay (Greenland), this quality coincides with a need for plastic pollution data. Here, a Greenlandic sediment core dated back to 1930 ± 2 was reconstructed for microplastics accumulation via micro-Fourier transform infrared spectroscopy. We show 85 years of fluctuating microplastics accumulation (987 − 16,645 particles Kg−1) down to 20 µm and diversified into eight polymers. Polyethylene (47%) and polypropylene (32%) were more abundantly present through time. Microplastics accumulation increases significantly after 1950 along with major socio-economic development in the area, suggesting an influence from regional stressors. Regional microplastics reconstructions must therefore be considered in the pursuit of an Anthropocene global plastic horizon.

High-resolution ocean color reconstruction and analysis focusing on Kd490 via machine learning model integration of MODIS and Sentinel-2 (MSI)

Oceanic water quality monitoring is essential for environmental protection, resource management, and ecosystem vitality. Optical remote sensing from space plays a pivotal role in global surveillance of oceanic water quality. However, the spatial resolution of current ocean color data products falls short of scrutinizing intricate small-scale marine features. This study introduces a hybrid model that fuses MODIS (Moderate Resolution lmaging Spectroradiometer) ocean color products with Sentinel-2 ‘s remote sensing reflectance data to generate high-resolution ocean color imagery, specifically investigating the diffuse attenuation coefficient at a wavelength of 490 nm (Kd490). To address the intricacies of coastal environments, we propose two complementary strategies to improve the accuracy of inversion. The first strategy leverages MODIS ocean color products alongside a geographic segmentation model to perform distinct inversions for separate marine zones, enhancing spatial resolution and specificity in coastal regions. The second strategy bolsters model interpretability during training by integrating predictions from conventional physical models into a Random Forest-based Regression Ensemble (RFRE) model. This study focuses on the coastal regions surrounding the Beibu Gulf, near Hainan Island in China. Our findings exhibit a strong concordance with MODIS products, achieving a monthly average coefficient of determination (R²) of 0.90, peaking at 0.97, and sustaining a monthly average root-mean-square error (RMSE) of less than 0.02. These results substantiate the model’s efficacy. Moreover, the annual trend analysis and localized assessment of the reconstructed Kd490 offer nuanced insights that surpass MODIS data, establishing a robust foundation for high-resolution water quality monitoring in coastal zones.