Low-resolution Data Research Articles

GA-NIFS: Multiphase analysis of a star-forming galaxy at z∼5.5

In this study, we present a detailed multiphase analysis of HZ4, a main-sequence star-forming galaxy at z ∼ 5.5, known for being a turbulent rotating disk and having a detection of a outflow in the ALMA observations. We exploited JWST/NIRSpec observations in the integral field spectroscopy mode with low- and high-spectral resolution which allow us, for the first time, to spatially resolve the rest-frame UV and optical emission of the galaxy to investigate the galaxy properties. In particular, the high-resolution dataset allowed us to study the kinematics of the ionized gas phase, and the conditions of the interstellar medium, such as the excitation mechanism, dust attenuation, and metallicity. The lower spectral-resolution observations allowed us to study the continuum emission and infer the stellar populations' ages and properties. Our findings suggest that HZ4 is a galaxy merger rather than a rotating disk as previously inferred from lower-resolution data. The merger is associated with an extended broad, blueshifted emission, potentially indicative of an outflow originating from a region of intense star formation and extending up to 4 kpc. In light of these new observations, we reanalyzed the ALMA data to compare the multiphase gas properties. If we interpret the broad components seen in and as outflows, the neutral and ionized components are co-spatial, and the mass loading factor of the ionized phase is significantly lower than that of the neutral phase, aligning with trends observed in multiphase systems at lower redshifts. Nonetheless, additional observations and larger statistical samples are essential to determine the role of mergers and outflows in the early Universe and to clarify the origin of the broad emission components observed in this system.

Development of data-driven algal bloom alert models with low temporal resolution data and application to Hong Kong rivers

Development of data-driven algal bloom alert models with low temporal resolution data and application to Hong Kong rivers

Calculation of pair distribution functions from small-angle X-ray scattering protein data by direct transform

In a small-angle X-ray scattering analysis of protein molecules in solution the calculation of the pair distribution function, P(r), is invariably performed by an indirect Fourier transform. This approach models a P(r) to fit the available intensity data, I(q). The determination of P(r) via a direct transform from I(q) has been dismissed as unworkable since the range of q that is experimentally measured is necessarily incomplete. Here, it is shown that, provided suitable measures are taken to estimate unmeasured low-resolution data and avoid a sharp data truncation at the high-resolution data limit, the appearance of significant artifacts in the resulting P(r) may be circumvented. Using several examples taken from the Small Angle Scattering Biological Data Bank, it is demonstrated that the P(r) obtained by a direct transform are in close agreement with the P(r) obtained using the popular indirect transform program GNOM.

Advances and Mechanisms of RNA-Ligand Interaction Predictions.

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA's specific recognition of other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA-ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA-ligand complexes by examining RNA's sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA-ligand interaction prediction tools.

Rapid Acquisition of High-Pixel Fluorescence Lifetime Images of Living Cells via Image Reconstruction Based on Edge-Preserving Interpolation.

Fluorescence lifetime imaging (FLIM) has established itself as a pivotal tool for investigating biological processes within living cells. However, the extensive imaging duration necessary to accumulate sufficient photons for accurate fluorescence lifetime calculations poses a significant obstacle to achieving high-resolution monitoring of cellular dynamics. In this study, we introduce an image reconstruction method based on the edge-preserving interpolation method (EPIM), which transforms rapidly acquired low-resolution FLIM data into high-pixel images, thereby eliminating the need for extended acquisition times. Specifically, we decouple the grayscale image and the fluorescence lifetime matrix and perform an individual interpolation on each. Following the interpolation of the intensity image, we apply wavelet transformation and adjust the wavelet coefficients according to the image gradients. After the inverse transformation, the original image is obtained and subjected to noise reduction to complete the image reconstruction process. Subsequently, each pixel is pseudo-color-coded based on its intensity and lifetime, preserving both structural and temporal information. We evaluated the performance of the bicubic interpolation method and our image reconstruction approach on fluorescence microspheres and fixed-cell samples, demonstrating their effectiveness in enhancing the quality of lifetime images. By applying these techniques to live-cell imaging, we can successfully obtain high-pixel FLIM images at shortened intervals, facilitating the capture of rapid cellular events.

Amplicon Sequencing Reveals Diversity in Spatially Separated Microbial Communities in the Icelandic Mars Analog Environment Mælifellssandur.

Exploration missions to Mars rely on landers or rovers to perform multiple analyses over geographically small sampling regions, while landing site selection is done using large-scale but low-resolution remote-sensing data. Utilizing Earth analog environments to estimate small-scale spatial and temporal variation in key geochemical signatures and biosignatures will help mission designers ensure future sampling strategies meet mission science goals. Icelandic lava fields can serve as Mars analog sites due to conditions that include low nutrient availability, temperature extremes, desiccation, and isolation from anthropogenic contamination. This work reports analysis of samples collected using methods analogous to those of planetary missions to characterize microbial communities at different spatial scales in Mælifellssandur, Iceland, an environment with homogeneity at "remote imaging" resolution (overall temperature, apparent moisture content, and regolith grain size). Although microbial richness did not vary significantly among samples, the phylogenetic composition of the sediment microbiome differed significantly among sites separated by 100 m, which suggests distinct microbial signatures despite apparent homogeneity from remote observations. This work highlights the importance of considering microenvironments in future life-detection missions to extraterrestrial planetary bodies.

Machine and Deep Learning Methods for Predicting 3D Genome Organization.

Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers and transcription factor binding site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, and TAD boundaries) and analyze their pros and cons. We also point out obstacles to the computational prediction of 3D interactions and suggest future research directions.

Cell2Cell: Explorative Cell Interaction Analysis in Multi-Volumetric Tissue Data.

We present Cell2Cell, a novel visual analytics approach for quantifying and visualizing networks of cell-cell interactions in three-dimensional (3D) multi-channel cancerous tissue data. By analyzing cellular interactions, biomedical experts can gain a more accurate understanding of the intricate relationships between cancer and immune cells. Recent methods have focused on inferring interaction based on the proximity of cells in low-resolution 2D multi-channel imaging data. By contrast, we analyze cell interactions by quantifying the presence and levels of specific proteins within a tissue sample (protein expressions) extracted from high-resolution 3D multi-channel volume data. Such analyses have a strong exploratory nature and require a tight integration of domain experts in the analysis loop to leverage their deep knowledge. We propose two complementary semi-automated approaches to cope with the increasing size and complexity of the data interactively: On the one hand, we interpret cell-to-cell interactions as edges in a cell graph and analyze the image signal (protein expressions) along those edges, using spatial as well as abstract visualizations. Complementary, we propose a cell-centered approach, enabling scientists to visually analyze polarized distributions of proteins in three dimensions, which also captures neighboring cells with biochemical and cell biological consequences. We evaluate our application in three case studies, where biologists and medical experts use Cell2Cell to investigate tumor micro-environments to identify and quantify T-cell activation in human tissue data. We confirmed that our tool can fully solve the use cases and enables a streamlined and detailed analysis of cell-cell interactions.

Frontiers in integrative structural modeling of macromolecular assemblies

Abstract Integrative modeling enables structure determination for large macromolecular assemblies by combining data from multiple experiments with theoretical and computational predictions. Recent advancements in AI-based structure prediction and cryo electron-microscopy have sparked renewed enthusiasm for integrative modeling; structures from AI-based methods can be integrated with in situ maps to characterize large assemblies. This approach previously allowed us and others to determine the architectures of diverse macromolecular assemblies, such as nuclear pore complexes, chromatin remodelers, and cell–cell junctions. Experimental data spanning several scales was used in these studies, ranging from high-resolution data, such as X-ray crystallography and AlphaFold structure, to low-resolution data, such as cryo-electron tomography maps and data from co-immunoprecipitation experiments. Two recurrent modeling challenges emerged across a range of studies. First, these assemblies contained significant fractions of disordered regions, necessitating the development of new methods for modeling disordered regions in the context of ordered regions. Second, methods needed to be developed to utilize the information from cryo-electron tomography, a timely challenge as structural biology is increasingly moving towards in situ characterization. Here, we recapitulate recent developments in the modeling of disordered proteins and the analysis of cryo-electron tomography data and highlight other opportunities for method development in the context of integrative modeling.

АВТОМАТИЧЕСКАЯ ИДЕНТИФИКАЦИЯ НОВОЗЕМЕЛЬСКОЙ БОРЫ

The paper examines a type of downslope windstorm – the Novaya Zemlya bora. This is a insufficiently studied mesoscale phenomenon characterized by the presence of high wind speeds on the western slope of the archipelago, threatening the safety of port buildings and maritime navigation. The paper proposes an approach for automatic identification of bora, which will allow to make a climatic picture of this phenomenon and to identify its characteristic features. The developed approach was applied to long-term (2015–2023) high-resolution numerical simulation data obtained using the WRF atmospheric model, and to lower-resolution data – atmospheric reanalysis ERA5. Over a 9-year period, about 220 bora events were analyzed, and this was done not only for the entire archipelago (which is typical for most studies), but also for individual regions of Novaya Zemlya. It was shown that the qualitative climatic characteristics of bora do not depend on spatial resolution, which potentially allows us to apply the developed method to identify the bora on longer time periods, for example, the entire period covered by the ERA5 reanalysis data. However, on a quantitative level, high resolution showed a greater intensity and duration of the phenomenon, as expected.

Kinematical signatures: Distinguishing between warps and radial flows

Context. Increasing evidence shows that warped disks are common, challenging the methods used to model their velocity fields. Molecular line emission of these disks is characterized by a twisted pattern, similar to the signal from radial flows, complicating the study of warped disk kinematics. Previous attempts to model these features have encountered difficulties in distinguishing between the underlying kinematics of different disks. Aims. This study aims to advance gas kinematics modeling capabilities by extending the Extracting Disk Dynamics (eddy) package to include warped geometries and radial flows. We assess the performance of eddy in recovering input parameters for scenarios involving warps, radial flows, and combinations of the two. Additionally, we provide a basis to break the visual degeneracy between warped disks and radial flow, establishing a criterion to distinguish them. Methods. We extended the eddy package to handle warped geometries by including a parametric prescription of a warped disk and a ray-casting algorithm to account for the surface self-obscuration arising from the 3D to 2D projection. The effectiveness of the tool was tested using the radiative transfer code RADMC3D, generating synthetic models for disks with radial flows, warped disks, and warped disks with radial flows. Results. We demonstrate the efficacy of our tool in accurately recovering the geometrical parameters of systems, particularly in data with sufficient angular resolution. Importantly, we observe minimal impact from thermal noise levels typical in Atacama Large Millimeter/submillimeter Array (ALMA) observations. Furthermore, our findings reveal that fitting an incorrect model type produces characteristic residual signatures, which serve as kinematic criteria for disk classification. Conclusions. Characterizing gas kinematics requires careful consideration of twisted motions. While our model provides insights into disk geometries, caution is needed when interpreting parameters in regions with complex kinematics or low-resolution data. Future ALMA baseline observations should help clarify warped disk kinematics.

Asteroid material classification based on multi-parameter constraints using artificial intelligence

Context. Material types of asteroids provide key clues to their evolutionary history and contained resources. The Gaia mission has released extensive low-resolution spectral observation data of small Solar System bodies. However, methods for classifying asteroids based on low-resolution space-based spectra are still inadequate, and do not fully leverage the complementary features of spectra and multiple intrinsic attributes of asteroids to achieve precise material classification. Aims. Our goal is to propose a method with a higher generalization accuracy for asteroid material classification by integrating multi-source information, identifying optimal feature combinations for model inputs, and deepening the understanding of relationships among asteroid parameters. Methods. The effective asteroid photometric, physical, and orbital parameters were screened using the information gain ratio and Spearman’s rank correlation coefficient. Then, artificial intelligence techniques were employed to combine asteroid spectra with the selected various parameters for six-class material classification. By comparing five machine learning models, we identified network structures with higher validation accuracy and stable generalization performance. Meanwhile, feature ablation experiments were conducted to determine the input parameter combinations suitable for different scenarios. Finally, based on the statistical results and model outputs, the constraint relationships among asteroid parameters were visualized and analyzed. Results. The proposed AsterRF model achieved a validation accuracy of 92.2%, an improvement of approximately 7.8 percentage points compared to existing methods that use only spectra. V-type asteroids exhibited the highest classification accuracy, followed by A-type and D-type. X-type asteroids had the lowest precision and recall, and were easily confused with C-type. The model generally showed higher classification confidence for S-type asteroids. The top five attributes that the model focused on are the phase slope parameter (G), orbital type, albedo, H magnitude, and effective diameter. Additionally, the correlations between asteroid materials and other parameters were generally below 0.4. Conclusions. Incorporating optimal asteroid parameter combinations can significantly enhance classification accuracy based on spectra. A dual-channel network that processes spectra and parameter inputs separately, and employs a self-attention mechanism for feature fusion is effective in combining multi-source asteroid information. Both the statistical correlations and model performance-based importance rankings of parameters contribute to understanding the constraint relationships among asteroid attributes.

Generalized super-resolution 4D Flow MRI - using ensemble learning to extend across the cardiovascular system.

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was therefore to explore the generalizability of SR 4D Flow MRI using a combination of existing super-resolution base models, novel heterogeneous training sets, and dedicated ensemble learning techniques; the latter-most being effectively used for improved domain adaption in other domains or modalities, however, with no previous exploration in the setting of 4D Flow MRI. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinicallevel input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with the novel use of ensemble learning in the setting of advanced fullfield flow imaging extending utility across various clinical areas of interest.

Fast-Vid2Vid++: Spatial-Temporal Distillation for Real-Time Video-to-Video Synthesis.

Video-to-Video synthesis (Vid2Vid) gains remarkable performance in generating a photo-realistic video from a sequence of semantic maps, such as segmentation, sketch and pose. However, this pipeline is heavily limited to high computational cost and long inference latency, mainly attributed to two essential factors: 1) network architecture parameters, 2) sequential data stream. Recently, the parameters of image-based generative models have been significantly reduced via more efficient network architectures. Existing methods mainly focus on slimming network architectures but ignore the size of the sequential data stream. Moreover, due to the lack of temporal coherence, image-based compression is not sufficient for the compression of the video task. In this paper, we present a spatial-temporal hybrid distillation compression framework, Fast-Vid2Vid++, which focuses on knowledge distillation of the teacher network and the data stream of generative models on both space and time. Fast-Vid2Vid++ makes the first attempt at time dimension to transfer hierarchical features and time coherence knowledge to reduce computational resources and accelerate inference. Specifically, we compress the data stream spatially and reduce the temporal redundancy. We distill the knowledge of the hierarchical features and the final response from the teacher network to the student network in high-resolution and full-time domains. We transfer the long-term dependencies of the features and video frames to the student model. After the proposed spatial-temporal hybrid knowledge distillation (Spatial-Temporal-HKD), our model can synthesize high-resolution key-frames using the low-resolution data stream. Finally, Fast-Vid2Vid++ interpolates intermediate frames by motion compensation with slight latency and generates full-length sequences with motion-aware inference (MAI). On standard benchmarks, Fast-Vid2Vid++ achieves a real-time performance of 30-59 FPS and saves 28-35× computational cost on a single V100 GPU.

Methods for refining the depth map obtained from depth sensors

Depth maps are essential in applications such as robotics, augmented reality, autonomous vehicles, and medical imaging, providing critical spatial information. However, depth maps from sensors like time-of-flight (ToF) and structured light systems often suffer from low resolution, noise, and missing data. Addressing these challenges, this study presents an innovative method to refine depth maps by integrating high-resolution color images. The proposed approach employs both hard- and soft-decision pixel assignment strategies to adaptively enhance depth map quality. The hard-decision model simplifies edge classification, while the soft-decision model, integrated within a Markov Random Field framework, improves edge consistency and reduces noise. By analyzing discrepancies between edges in depth maps and color images, the method effectively mitigates artifacts such as texturecopying and blurred edges, ensuring better alignment between the datasets. Key innovations include the use of the Canny edge detection operator to identify and categorize edge inconsistencies and anisotropic affinity calculations for precise structural representation. The soft-decision model introduces advanced noise reduction techniques, improving depth map resolution and preserving edge details better than traditional methods. Experimental validation on Middlebury benchmark datasets demonstrates that the proposed method outperforms existing techniques in reducing Mean Absolute Difference values, especially in high-upscaling scenarios. Visual comparisons highlight its ability to suppress artifacts and enhance edge sharpness, confirming its effectiveness across various conditions. This approach holds significant potential for applications requiring high-quality depth maps, including robotics, augmented reality, autonomous systems, and medical imaging. By addressing critical limitations of current methods, the study offers a robust, versatile solution for depth map refinement, with opportunities for real-time optimization in dynamic environments.

Method for predicting conductive heat transfer topologies based on Fourier neural operator

This paper presents an iterative topology optimizer for conductive heat transfer structures based on the Fourier neural operator (FNO). A data-driven model based on FNO is trained to predict the temperature under different material distributions, different boundary conditions, and different thermal loads. A new method is used to generate data, which makes the modeling process of temperature predictor completely independent of the traditional optimization method. Then by coupling the trained temperature predictor with the solid isotropic material with penalization (SIMP) method, a new iterative topology optimizer is formed. Numerical experiments demonstrate that the proposed method can generate heat transfer structures with good performance, and can apply the model trained on low-resolution data to the structural topology optimization with high resolution, which greatly improves the optimization efficiency. In addition to the heat conduction structure optimization problem, the method developed in this paper is expected to be applied to other optimization problems or coupled with other conventional optimization methods

A General Super-Resolution Approach Integrating Physical Information for Temperature Field Measurement.

In industrial measurement, temperature field measurement typically relies on thermocouples and spectroscopic techniques. These traditional methods often suffer from insufficient precision, resulting in prevalent low-resolution measurements in real thermal scenarios. To address this challenge, we propose a novel general super-resolution approach for temperature field measurement in various thermal scenarios, leveraging the low-resolution (LR) data obtained from sensor array technology. The method incorporates skip connections and multi-path learning, along with physical information loss, to enhance accuracy. To validate the effectiveness of the approach, simulations across three two-dimensional thermal scenarios are conducted: the heating process in silicon chips, the thermodynamic process of hot and cold water mixing, and the convective heat transfer phenomena involved in metal sheet dissipation under airflow. The results show that the learning model can accurately predict the HR temperature. The proposed approach offers a pathway for generating HR solutions, bypassing traditional time-consuming simulation processes while ensuring data accuracy. By utilizing a fixed model and a lightweight physical loss function, we simplify the deployment process, facilitating applications in computational fluid dynamics (CFD) solutions, engineering measurements, and related fields.

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.

Assessment of Benthic Meiofauna in Multi-Stressed Environment of a Tropical Estuary: a Case Study Using Low Taxonomic Resolution Data

Assessment of Benthic Meiofauna in Multi-Stressed Environment of a Tropical Estuary: a Case Study Using Low Taxonomic Resolution Data

Laser ablation inductively coupled plasma mass spectrometry measurements for high-resolution chemical ice core analyses with a first application to an ice core from Skytrain Ice Rise (Antarctica)

Abstract. Conventional methods of inorganic impurity analysis do not provide high enough depth resolution for many scientific questions in ice core science. In this study, we present a setup of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) for high-resolution glacier ice impurity analysis to the sub-millimetre scale. This setup enables ice core chemical impurity analysis to a depth resolution of ∼182 µm while consuming only very small amounts of ice. The system performs simultaneous analysis of sodium, magnesium and aluminium incorporated in the ice matrix. In this case study within the framework of the WACSWAIN (WArm Climate Stability of the West Antarctic Ice Sheet in the last INterglacial) project, our method is applied to a selection of samples from the Skytrain ice core (West Antarctica) over a total length of 6.7 m consisting of about 130 single samples. The main goal of this study is to use the new LA-ICP-MS method to extract meaningful climate signals on a depth resolution level beyond the limits of continuous-flow analysis (CFA). A comparison between low-resolution CFA data and the high-resolution LA-ICP-MS data reveals generally good agreement on the decimetre scale. Stacking of parallel laser measurements together with frequency analysis is used to analyse the high-resolution LA-ICP-MS data at millimetre resolution. Spectral analysis reveals that despite effects of impurity accumulation along ice crystal grain boundaries, periodic concentration changes in the Skytrain ice core on the millimetre scale can be identified in ice from 26.8 ka (kiloyears before present, i.e. 1950 CE). These findings open new possibilities for climate data interpretation with respect to fast changes in the last glacial period and beyond, for example within the Beyond EPICA oldest-ice project.