Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Peering into lunar permanently shadowed regions with deep learning.

ABSTRACT: The lunar permanently shadowed regions (PSRs) are expected to host large quantities of water-ice, which are key for sustainable exploration of the Moon and beyond. In the near future, NASA and other entities plan to send rovers and humans to characterize water-ice within PSRs. However, there exists only limited information about the small-scale geomorphology and distribution of ice within PSRs because the orbital imagery captured to date lacks sufficient resolution and/or signal. In this paper, we develop and validate a new method of post-processing LRO NAC images of PSRs. We show that our method is able to reveal previously unseen geomorphological features such as boulders and craters down to 3 meters in size, whilst not finding evidence for surface frost or near-surface ice. Our post-processed images significantly facilitate the exploration of PSRs by reducing the uncertainty of target selection and traverse/mission planning.

SUBMITTER: Bickel VT

PROVIDER: S-EPMC8460740 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Peering into lunar permanently shadowed regions with deep learning.

Bickel V T VT Moseley B B Lopez-Francos I I Shirley M M

Nature communications 20210923 1

The lunar permanently shadowed regions (PSRs) are expected to host large quantities of water-ice, which are key for sustainable exploration of the Moon and beyond. In the near future, NASA and other entities plan to send rovers and humans to characterize water-ice within PSRs. However, there exists only limited information about the small-scale geomorphology and distribution of ice within PSRs because the orbital imagery captured to date lacks sufficient resolution and/or signal. In this paper, ...[more]

PMID: 34556656

Similar Datasets

Comprehensive mapping of lunar surface chemistry by adding Chang'e-5 samples with deep learning.

Project description:Lunar surface chemistry is essential for revealing petrological characteristics to understand the evolution of the Moon. Existing chemistry mapping from Apollo and Luna returned samples could only calibrate chemical features before 3.0 Gyr, missing the critical late period of the Moon. Here we present major oxides chemistry maps by adding distinctive 2.0 Gyr Chang'e-5 lunar soil samples in combination with a deep learning-based inversion model. The inferred chemical contents are more precise than the Lunar Prospector Gamma-Ray Spectrometer (GRS) maps and are closest to returned samples abundances compared to existing literature. The verification of in situ measurement data acquired by Chang'e 3 and Chang'e 4 lunar rover demonstrated that Chang'e-5 samples are indispensable ground truth in mapping lunar surface chemistry. From these maps, young mare basalt units are determined which can be potential sites in future sample return mission to constrain the late lunar magmatic and thermal history.

| S-EPMC10661975 | biostudies-literature

Lunar impact crater identification and age estimation with Chang'E data by deep and transfer learning.

Project description:Impact craters, which can be considered the lunar equivalent of fossils, are the most dominant lunar surface features and record the history of the Solar System. We address the problem of automatic crater detection and age estimation. From initially small numbers of recognized craters and dated craters, i.e., 7895 and 1411, respectively, we progressively identify new craters and estimate their ages with Chang'E data and stratigraphic information by transfer learning using deep neural networks. This results in the identification of 109,956 new craters, which is more than a dozen times greater than the initial number of recognized craters. The formation systems of 18,996 newly detected craters larger than 8 km are estimated. Here, a new lunar crater database for the mid- and low-latitude regions of the Moon is derived and distributed to the planetary community together with the related data analysis.

| S-EPMC7755906 | biostudies-literature

Identifying Periampullary Regions in MRI Images Using Deep Learning.

Project description:BackgroundDevelopment and validation of a deep learning method to automatically segment the peri-ampullary (PA) region in magnetic resonance imaging (MRI) images.MethodsA group of patients with or without periampullary carcinoma (PAC) was included. The PA regions were manually annotated in MRI images by experts. Patients were randomly divided into one training set, one validation set, and one test set. Deep learning methods were developed to automatically segment the PA region in MRI images. The segmentation performance of the methods was compared in the validation set. The model with the highest intersection over union (IoU) was evaluated in the test set.ResultsThe deep learning algorithm achieved optimal accuracies in the segmentation of the PA regions in both T1 and T2 MRI images. The value of the IoU was 0.68, 0.68, and 0.64 for T1, T2, and combination of T1 and T2 images, respectively.ConclusionsDeep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the PA region in MRI images. This automated non-invasive method helps clinicians to identify and locate the PA region using preoperative MRI scanning.

| S-EPMC8193851 | biostudies-literature

Design and structure of overlapping regions in PCA via deep learning.

Project description:Polymerase cycling assembly (PCA) stands out as the predominant method in the synthesis of kilobase-length DNA fragments. The design of overlapping regions is the core factor affecting the success rate of synthesis. However, there still exists DNA sequences that are challenging to design and construct in the genome synthesis. Here we proposed a deep learning model based on extensive synthesis data to discern latent sequence representations in overlapping regions with an AUPR of 0.805. Utilizing the model, we developed the SmartCut algorithm aimed at designing oligonucleotides and enhancing the success rate of PCA experiments. This algorithm was successfully applied to sequences with diverse synthesis constraints, 80.4 % of which were synthesized in a single round. We further discovered structure differences represented by major groove width, stagger, slide, and centroid distance between overlapping and non-overlapping regions, which elucidated the model's reasonableness through the lens of physical chemistry. This comprehensive approach facilitates streamlined and efficient investigations into the genome synthesis.

| S-EPMC11799973 | biostudies-literature

Evidence for surface water ice in the lunar polar regions using reflectance measurements from the Lunar Orbiter Laser Altimeter and temperature measurements from the Diviner Lunar Radiometer Experiment.

Project description:We find that the reflectance of the lunar surface within 5 ° of latitude of the South Pole increases rapidly with decreasing temperature, near ~110K, behavior consistent with the presence of surface water iceThe North polar region does not show this behavior, nor do South polar surfaces at latitudes more than 5° from the pole. This South pole reflectance anomaly persists when analysis is limited to surfaces with slopes less than 10° to eliminate false detection due to the brightening effect of mass wasting, and also when the very bright south polar crater Shackleton is excluded from the analysis. We also find that south polar regions of permanent shadow that have been reported to be generally brighter at 1064 nm do not show anomalous reflectance when their annual maximum surface temperatures are too high to preserve water ice. This distinction is not observed at the North Pole. The reflectance excursion on surfaces with maximum temperatures below 110K is superimposed on a general trend of increasing reflectance with decreasing maximum temperature that is present throughout the polar regions in the north and south; we attribute this trend to a temperature or illumination-dependent space weathering effect (e.g. Hemingway et al. 2015). We also find a sudden increase in reflectance with decreasing temperature superimposed on the general trend at 200K and possibly at 300K. This may indicate the presence of other volatiles such as sulfur or organics. We identified and mapped surfaces with reflectances so high as to be unlikely to be part of an ice-free population. In this south we find a similar distribution found by Hayne et al. 2015 based on UV properties. In the north a cluster of pixels near that pole may represent a limited frost exposure.

| S-EPMC7197374 | biostudies-literature

Direct evidence of surface exposed water ice in the lunar polar regions.

Project description:Water ice may be allowed to accumulate in permanently shaded regions on airless bodies in the inner solar system such as Mercury, the Moon, and Ceres [Watson K, et al. (1961) J Geophys Res 66:3033-3045]. Unlike Mercury and Ceres, direct evidence for water ice exposed at the lunar surface has remained elusive. We utilize indirect lighting in regions of permanent shadow to report the detection of diagnostic near-infrared absorption features of water ice in reflectance spectra acquired by the Moon Mineralogy Mapper [M (3)] instrument. Several thousand M (3) pixels (∼280 × 280 m) with signatures of water ice at the optical surface (depth of less than a few millimeters) are identified within 20° latitude of both poles, including locations where independent measurements have suggested that water ice may be present. Most ice locations detected in M (3) data also exhibit lunar orbiter laser altimeter reflectance values and Lyman Alpha Mapping Project instrument UV ratio values consistent with the presence of water ice and also exhibit annual maximum temperatures below 110 K. However, only ∼3.5% of cold traps exhibit ice exposures. Spectral modeling shows that some ice-bearing pixels may contain ∼30 wt % ice that is intimately mixed with dry regolith. The patchy distribution and low abundance of lunar surface-exposed water ice might be associated with the true polar wander and impact gardening. The observation of spectral features of H2O confirms that water ice is trapped and accumulates in permanently shadowed regions of the Moon, and in some locations, it is exposed at the modern optical surface.

| S-EPMC6130389 | biostudies-literature

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

Project description:BackgroundIn the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide.ResultsBased on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome).ConclusionThe predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

| S-EPMC5984344 | biostudies-literature

Deep learning approach for predicting functional Z-DNA regions using omics data.

Project description:Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using sequence information. Z-DNA CHiP-seq experiment results showed little overlap with Z-Hunt predictions implying that sequence information only is not sufficient to explain emergence of Z-DNA at different genomic locations. Adding epigenetic and other functional genomic mark-ups to DNA sequence level can help revealing the functional Z-DNA sites. Here we take advantage of the deep learning approach that can analyze and extract information from large volumes of molecular biology data. We developed a machine learning approach DeepZ that aggregates information from genome-wide maps of epigenetic markers, transcription factor and RNA polymerase binding sites, and chromosome accessibility maps. With the developed model we not only verify the experimental Z-DNA predictions, but also generate the whole-genome annotation, introducing new possible Z-DNA regions, which have not yet been found in experiments and can be of interest to the researchers from various fields.

| S-EPMC7644757 | biostudies-literature

Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest.

Project description:PurposeTo compare change over time in eye-specific optical coherence tomography (OCT) retinal nerve fiber layer (RNFL)-based region-of-interest (ROI) maps developed using unsupervised deep-learning auto-encoders (DL-AE) to circumpapillary RNFL (cpRNFL) thickness for the detection of glaucomatous progression.MethodsForty-four progressing glaucoma eyes (by stereophotograph assessment), 189 nonprogressing glaucoma eyes (by stereophotograph assessment), and 109 healthy eyes were followed for ≥3 years with ≥4 visits using OCT. The San Diego Automated Layer Segmentation Algorithm was used to automatically segment the RNFL layer from raw three-dimensional OCT images. For each longitudinal series, DL-AEs were used to generate individualized eye-based ROI maps by identifying RNFL regions of likely progression and no change. Sensitivities and specificities for detecting change over time and rates of change over time were compared for the DL-AE ROI and global cpRNFL thickness measurements derived from a 2.22-mm to 3.45-mm annulus centered on the optic disc.ResultsThe sensitivity for detecting change in progressing eyes was greater for DL-AE ROIs than for global cpRNFL annulus thicknesses (0.90 and 0.63, respectively). The specificity for detecting not likely progression in nonprogressing eyes was similar (0.92 and 0.93, respectively). The mean rates of change in DL-AE ROI were significantly faster than for cpRNFL annulus thickness in progressing eyes (-1.28 µm/y vs. -0.83 µm/y) and nonprogressing eyes (-1.03 µm/y vs. -0.78 µm/y).ConclusionsEye-specific ROIs identified using DL-AE analysis of OCT images show promise for improving assessment of glaucomatous progression.Translational relevanceThe detection and monitoring of structural glaucomatous progression can be improved by considering eye-specific regions of likely progression identified using deep learning.

| S-EPMC8300051 | biostudies-literature

A deep learning method for predicting interactions for intrinsically disordered regions of proteins.

Project description:Intrinsically disordered proteins or regions (IDPs/IDRs) adopt diverse binding modes with different partners, ranging from ordered to multivalent to fuzzy conformations in the bound state. Characterizing IDR interfaces is challenging experimentally and computationally. Alphafold-multimer and Alphafold3, the state-of-the-art structure prediction methods, are less accurate at predicting IDR binding sites at their benchmarked confidence cutoffs. Their performance improves upon lowering the confidence cutoffs. Here, we developed Disobind, a deep-learning method that predicts inter-protein contact maps and interface residues for an IDR and a partner protein, given their sequences. It outperforms AlphaFold-multimer and AlphaFold3 at multiple confidence cutoffs. Combining the Disobind and AlphaFold-multimer predictions further improves the performance. In contrast to most current methods, Disobind considers the context of the binding partner and does not depend on structures and multiple sequence alignments. Its predictions can be used to localize IDRs in integrative structures of large assemblies and characterize and modulate IDR-mediated interactions.

| S-EPMC11702703 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data