Translesion and Repair DNA Polymerases: Diverse Structure and Mechanism.

The number of DNA polymerases identified in each organism has mushroomed in the past two decades. Most newly found DNA polymerases specialize in translesion synthesis and DNA repair instead of replication. Although intrinsic error rates are higher for translesion and repair polymerases than for replicative polymerases, the specialized polymerases increase genome stability and reduce tumorigenesis. Reflecting the numerous types of DNA lesions and variations of broken DNA ends, translesion and repair polymerases differ in structure, mechanism, and function. Here, we review the unique and general features of polymerases specialized in lesion bypass, as well as in gap-filling and end-joining synthesis.

Freedom to err: The expanding cellular functions of translesion DNA polymerases.

Translesion synthesis (TLS) DNA polymerases were originally described as error-prone enzymes involved in the bypass of DNA lesions. However, extensive research over the past few decades has revealed that these enzymes play pivotal roles not only in lesion bypass, but also in a myriad of other cellular processes. Such processes include DNA replication, DNA repair, epigenetics, immune signaling, and even viral infection. This review discusses the wide range of functions exhibited by TLS polymerases, including their underlying biochemical mechanisms and associated mutagenicity. Given their multitasking ability to alleviate replication stress, TLS polymerases represent a cellular dependency and a critical vulnerability of cancer cells. Hence, this review also highlights current and emerging strategies for targeting TLS polymerases in cancer therapy.

A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions.

Advances in medicine and biotechnology rely on a deep understanding of biological processes. Despite the increasingly available types and amounts of omics data, significant knowledge gaps remain, with current approaches to identify and curate missing annotations being limited to a set of already known reactions. Here, we introduce Network Integrated Computational Explorer for Gap Annotation of Metabolism (NICEgame), a workflow to identify and curate nonannotated metabolic functions in genomes using the ATLAS of Biochemistry and genome-scale metabolic models (GEMs). To resolve gaps in GEMs, NICEgame provides alternative sets of known and hypothetical reactions, assesses their thermodynamic feasibility, and suggests candidate genes to catalyze these reactions. We identified metabolic gaps and applied NICEgame in the latest GEM of Escherichia coli, iML1515, and enhanced the E. coli genome annotation by resolving 47% of these gaps. NICEgame, applicable to any GEM and functioning from open-source software, should thus enhance all GEM-based predictions and subsequent biotechnological and biomedical applications.

The Osteotomy Gap Filling Rate of Multiple Time Points After Medial Opening Wedge High Tibial Osteotomy Without Bone Graft.

BACKGROUND: The purpose of the present study was to analyze osteotomy gap filling of multiple time points until 2 years post-medial opening wedge high tibial osteotomy (MOWHTO). The absolute value of gap filling and postoperative changes relative to the preoperative void osteotomy gap were evaluated using computed tomography (CT) at each time point. METHODS: Data of thirty patients who underwent MOWHTO between September 2019 and July 2021 were retrospectively analyzed. Surgical procedures without bone grafts were performed; a standardized rehabilitation protocol was implemented. The osteotomy gap filling rate was measured using CT scans at the immediate postoperative period and at 6, 12, and 24 months after surgery. Statistical analysis was performed to assess changes over time. RESULTS: The osteotomy gap filling rate showed a significant continuous increase after MOWHTO, reaching 45.2% at 6 months and 66.7 and 84.4% at 1 and 2 years postoperatively, respectively. The most substantial increase occurred within the initial 6 months, thus indicating a critical period for bone healing. CONCLUSION: The osteotomy gap filling rate showed a significant and gradual increase from immediately after surgery to 2 years after MOWTHO without bone grafting, the greatest of which was achieved in the initial 6-month period. Therefore, this study may be helpful for planning postoperative rehabilitation, including the extent of weight-bearing load and the period of crutch use.

Utilizing Deep Neural Networks to Fill Gaps in Small Genomes.

With the widespread adoption of next-generation sequencing technologies, the speed and convenience of genome sequencing have significantly improved, and many biological genomes have been sequenced. However, during the assembly of small genomes, we still face a series of challenges, including repetitive fragments, inverted repeats, low sequencing coverage, and the limitations of sequencing technologies. These challenges lead to unknown gaps in small genomes, hindering complete genome assembly. Although there are many existing assembly software options, they do not fully utilize the potential of artificial intelligence technologies, resulting in limited improvement in gap filling. Here, we propose a novel method, DLGapCloser, based on deep learning, aimed at assisting traditional tools in further filling gaps in small genomes. Firstly, we created four datasets based on the original genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla. To further extract effective information from the gene sequences, we also added homologous genomes to enrich the datasets. Secondly, we proposed the DGCNet model, which effectively extracts features and learns context from sequences flanking gaps. Addressing issues with early pruning and high memory usage in the Beam Search algorithm, we developed a new prediction algorithm, Wave-Beam Search. This algorithm alternates between expansion and contraction phases, enhancing efficiency and accuracy. Experimental results showed that the Wave-Beam Search algorithm improved the gap-filling performance of assembly tools by 7.35%, 28.57%, 42.85%, and 8.33% on the original results. Finally, we established new gap-filling standards and created and implemented a novel evaluation method. Validation on the genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla showed that DLGapCloser increased the number of filled gaps by 8.05%, 15.3%, 1.4%, and 7% compared to traditional assembly tools.

Differences in scanpath pattern and verbal working memory predicts efficient reading in the Cloze gap-filling test.

Different tests measure text comprehension, including the cloze gap-filling test, often used for language learning. Different studies hypothesized cognitive strategies in this type of test and their relationship with working memory and performance. However, no study investigated the cloze test, working memory, and possible cognitive strategies, while performing the test. Therefore, this study aimed to identify cognitive visual strategies in the cloze test by applying an unsupervised algorithm and to analyze the relationship between these strategies with working memory and performance in the cloze test. Our sample consisted of 51 university students, the largest sample in studies of cognitive strategies with cloze tests. Participants answered an 11-item cloze test in a computer with eye-tracking, a verbal working memory test, and a visuospatial working memory test. Our analysis of participants' scanpath identified two main strategies: one with fewer toggles between text and word bank and fewer fixations than the other one, indicating the existence of a global strategy. Furthermore, a model predicting the efficiency of participants in the cloze test found that item complexity, using a global strategy, and higher scores of working memory were the most significant predictors. These results confirm the hypothesis of a global strategy being related to successfully achieving higher-order reading processes.

Unraveling the "Gap-Filling" Mechanism of Multiple Charge Carriers in Aqueous Zn-MoS2 Batteries.

The utilization rate of active sites in cathode materials for Zn-based batteries is a key factor determining the reversible capacities. However, a long-neglected issue of the strong electrostatic repulsions among divalent Zn2+ in hosts inevitably causes the squander of some active sites (i.e., gap sites). Herein, we address this conundrum by unraveling the "gap-filling" mechanism of multiple charge carriers in aqueous Zn-MoS2 batteries. The tailored MoS2 /(reduced graphene quantum dots) hybrid features an ultra-large interlayer spacing (2.34 nm), superior electrical conductivity/hydrophilicity, and robust layered structure, demonstrating highly reversible NH4 + /Zn2+ /H+ co-insertion/extraction chemistry in the 1 M ZnSO4 +0.5 M (NH4 )2 SO4 aqueous electrolyte. The NH4 + and H+ ions can act as gap fillers to fully utilize the active sites and screen electrostatic interactions to accelerate the Zn2+ diffusion. Thus, unprecedentedly high rate capability (439.5 and 104.3 mAh g-1 at 0.1 and 30 A g-1 , respectively) and ultra-long cycling life (8000 cycles) are achieved.

MTG-Link: leveraging barcode information from linked-reads to assemble specific loci.

BACKGROUND: Local assembly with short and long reads has proven to be very useful in many applications: reconstruction of the sequence of a locus of interest, gap-filling in draft assemblies, as well as alternative allele reconstruction of large Structural Variants. Whereas linked-read technologies have a great potential to assemble specific loci as they provide long-range information while maintaining the power and accuracy of short-read sequencing, there is a lack of local assembly tools for linked-read data. RESULTS: We present MTG-Link, a novel local assembly tool dedicated to linked-reads. The originality of the method lies in its read subsampling step which takes advantage of the barcode information contained in linked-reads mapped in flanking regions. We validated our approach on several datasets from different linked-read technologies. We show that MTG-Link is able to assemble successfully large sequences, up to dozens of Kb. We also demonstrate that the read subsampling step of MTG-Link considerably improves the local assembly of specific loci compared to other existing short-read local assembly tools. Furthermore, MTG-Link was able to fully characterize large insertion variants and deletion breakpoints in a human genome and to reconstruct dark regions in clinically-relevant human genes. It also improved the contiguity of a 1.3 Mb locus of biological interest in several individual genomes of the mimetic butterfly Heliconius numata. CONCLUSIONS: MTG-Link is an efficient local assembly tool designed for different linked-read sequencing technologies. MTG-Link source code is available at https://github.com/anne-gcd/MTG-Link and as a Bioconda package.

A Solvent-Free, Thermally Curable Low-Temperature Organic Planarization Layer for Thin Film Encapsulation.

Thin film encapsulation (TFE) is an essential component to ensure reliable operation of environmentally susceptible organic light-emitting diode-based display. In order to integrate defect-free TFE on display with complex surface structures, additional planarization layer is imperative to planarize the surface topography. The thickness of conventional planarization layer is as high as tens of µm, but the thickness must be reduced substantially to minimize the light leakage in smaller devices such as micro light-emitting diodes. In this study, a thin-less than 2 µm-planarization is achieved via solvent-free process, initiated chemical vapor deposition (iCVD). By adapting copolymer from two soft, but curable monomers, glycidyl acrylate (GA) and 2-(dimethylamino)ethyl methacrylate, excellent planarization performance is achieved on various nano-grating patterns. With only 1.5 µm-thick iCVD planarization layer, a 600 nm-deep trench polyurethane acrylate pattern is flattened completely. The TFE fabricated on planarized pattern exhibits excellent barrier property as fabricated on flat glass substrate, which strongly suggests that iCVD planarization layer can serve as a promising planarization layer to fabricate TFE on various types of complicated device surfaces.

The 17-y spatiotemporal trend of PM2.5 and its mortality burden in China.

Investigations on the chronic health effects of fine particulate matter (PM2.5) exposure in China are limited due to the lack of long-term exposure data. Using satellite-driven models to generate spatiotemporally resolved PM2.5 levels, we aimed to estimate high-resolution, long-term PM2.5 and associated mortality burden in China. The multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) at 1-km resolution was employed as a primary predictor to estimate PM2.5 concentrations. Imputation techniques were adopted to fill in the missing AOD retrievals and provide accurate long-term AOD aggregations. Monthly PM2.5 concentrations in China from 2000 to 2016 were estimated using machine-learning approaches and used to analyze spatiotemporal trends of adult mortality attributable to PM2.5 exposure. Mean coverage of AOD increased from 56 to 100% over the 17-y period, with the accuracy of long-term averages enhanced after gap filling. Machine-learning models performed well with a random cross-validation R2 of 0.93 at the monthly level. For the time period outside the model training window, prediction R2 values were estimated to be 0.67 and 0.80 at the monthly and annual levels. Across the adult population in China, long-term PM2.5 exposures accounted for a total number of 30.8 (95% confidence interval [CI]: 28.6, 33.2) million premature deaths over the 17-y period, with an annual burden ranging from 1.5 (95% CI: 1.3, 1.6) to 2.2 (95% CI: 2.1, 2.4) million. Our satellite-based techniques provide reliable long-term PM2.5 estimates at a high spatial resolution, enhancing the assessment of adverse health effects and disease burden in China.

A Novel Quantitative Evaluation of Bone Formation After Opening Wedge High Tibial Osteotomy Using Tomosynthesis.

This study aimed to establish and validate a novel evaluation method using digital tomosynthesis to quantify bone formation in the gap after opening wedge high tibial osteotomy (OW-HTO). We retrospectively analyzed bone formation in the gap in 22 patients who underwent OW-HTO using digital tomosynthesis at 1, 2, 3, 6, 9, and 12 months postoperatively. Bone formation was semi-quantitatively assessed using the modified van Hemert's score and density measurements on digital tomosynthesis images. The gap filling value (GFV) was calculated as the ratio of the intensities of the opening gap and the tibial shaft. In addition, the relationship between the modified van Hemert's score and GFV was evaluated. The reproducibility of GFV had an interclass correlation coefficient (ICC [1,2]) of 0.958 for intraobserver reliability and an ICC (2,1) of 0.975 for interobserver reliability. The GFV increased in a time-dependent manner and was moderately correlated with the modified van Hemert's score (r = 0.630, p < 0.001). The GFV plateaued at 6 months postoperatively. In addition, the GFV was higher in patients with a modified van Hemert's score of 2 than in patients with a modified van Hemert's score of 3 (p = 0.008). The GFVs obtained using digital tomosynthesis can be used to assess postoperative bone formation in the opening gap after OW-HTO with high accuracy and reproducibility.

Analysing the value of environmental citizen-generated data: Complementarity and cost per observation.

The proliferation of Citizen Science initiatives has increased the expectations of practitioners who need data for design, analysis, management and research in environmental applications. Many Citizen Science experiences have reported tangible societal benefits related to improved governance of natural resources due to the involvement of citizens and communities. However, from the perspective of data generation, most of the literature on Citizen Science tends to regard it as a potentially cost-effective source of data, with major concerns about the quality of data. The Ground Truth 2.0 project brought the opportunity to examine the scope of this potential by analysing the value of citizen-generated data. We propose a methodology to account for the value of citizen observations as a function of their complementarity to existing environmental observations and the evolution of their costs in time. The application of the proposed methodology in the chosen case studies that were all established using a co-design approach shows that the cost of obtaining Citizen Science data is not as low as frequently stated in literature. This is because the costs associated with co-design events for creating a Citizen Science community, as well as the functional and technical design of the tools, are much higher than the costs of rolling out the actual observation campaigns. In none of the considered cases did an increment in the number of preparatory events translate into an immediate increase in the collected observations. Nevertheless, Citizen Science appears to have the greatest value in places where in-situ environmental monitoring is not implemented.

Filling gaps of genome scaffolds via probabilistic searching optical maps against assembly graph.

BACKGROUND: Optical maps record locations of specific enzyme recognition sites within long genome fragments. This long-distance information enables aligning genome assembly contigs onto optical maps and ordering contigs into scaffolds. The generated scaffolds, however, often contain a large amount of gaps. To fill these gaps, a feasible way is to search genome assembly graph for the best-matching contig paths that connect boundary contigs of gaps. The combination of searching and evaluation procedures might be "searching followed by evaluation", which is infeasible for long gaps, or "searching by evaluation", which heavily relies on heuristics and thus usually yields unreliable contig paths. RESULTS: We here report an accurate and efficient approach to filling gaps of genome scaffolds with aids of optical maps. Using simulated data from 12 species and real data from 3 species, we demonstrate the successful application of our approach in gap filling with improved accuracy and completeness of genome scaffolds. CONCLUSION: Our approach applies a sequential Bayesian updating technique to measure the similarity between optical maps and candidate contig paths. Using this similarity to guide path searching, our approach achieves higher accuracy than the existing "searching by evaluation" strategy that relies on heuristics. Furthermore, unlike the "searching followed by evaluation" strategy enumerating all possible paths, our approach prunes the unlikely sub-paths and extends the highly-probable ones only, thus significantly increasing searching efficiency.

HYFI: Hybrid filling of the dead-time gap for faster zero echo time imaging.

The aim of this work was to improve the SNR efficiency of zero echo time (ZTE) MRI pulse sequences for faster imaging of short-T2 components at large dead-time gaps. ZTE MRI with hybrid filling (HYFI) is a strategy for retrieving inner k-space data missed during the dead-time gaps arising from radio-frequency excitation and switching in ZTE imaging. It performs hybrid filling of the inner k-space with a small single-point-imaging core surrounded by a stack of shells acquired on radial readouts in an onion-like fashion. The exposition of this concept is followed by translation into guidelines for parameter choice and implementation details. The imaging properties and performance of HYFI are studied in simulations as well as phantom, in vitro and in vivo imaging, with an emphasis on comparison with the pointwise encoding time reduction with radial acquisition (PETRA) technique. Simulations predict higher SNR efficiency for HYFI compared with PETRA at preserved image quality, with the advantage increasing with the size of the k-space gap. These results are confirmed by imaging experiments with gap sizes of 25 to 50 Nyquist dwells, in which scan times for similar image quality could be reduced by 25% to 60%. The HYFI technique provides both high SNR efficiency and image quality, thus outperforming previously known ZTE-based pulse sequences, in particular for large k-space gaps. Promising applications include direct imaging of ultrashort-T2 components, such as the myelin bilayer or collagen, T2 mapping of ultrafast relaxing signals, and ZTE imaging with reduced chemical shift artifacts.

Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005-2016.

Predicting long-term spatiotemporal characteristics of fine particulate matter (PM2.5) is important in China to understand historical levels of PM2.5, to support health effects research of both long-term and short-term exposures to PM2.5, and to evaluate the efficacy of air pollution control policies. Satellite-retrieved aerosol optical depth (AOD) provides a unique opportunity to characterize the long-term trends of ground-level PM2.5 at high spatial resolution. However, the missing rate of AOD in Northeastern China (NEC) is very high, especially in winter, and challenges the accuracy of long-term predictions of PM2.5 if left unresolved. Using random forest algorithms, this study developed a gap-filling approach combing satellite AOD, meteorological data, land use parameters, population and visibility in the NEC during 2005-2016. The model, including all predictors, combined with a model without AOD was able to fill the gap of PM2.5 predictions caused by missing AOD at 1-km resolution. The R2 (RMSE) of the full-coverage predictions was 0.81 (18.5 µg/m3) at the daily level. Gap-filled PM2.5 predictions on days with missing AOD reduced the relative prediction error from 28% to 2.5% in winter. The leave-one-year-out-cross-validation R2 (RMSE) of the full-coverage predictions was 0.65 (16.3 µg/m3) at the monthly level, indicating relatively high accuracy of predicted historical PM2.5 concentrations. Our results suggested that AOD helped increase the reliability of historical PM2.5 prediction when ground PM2.5 measurements were unavailable, even though predictions from the AOD model only accounted for approximate 37% of the whole dataset. Predicted PM2.5 level in NEC have increased since 2005, reached its peak during 2013-2015, then saw a major decline in 2016. Our high-resolution predictions also showed a south to north gradient and many pollution hot spots in the city clusters surrounding provincial capitals, as well as within large cities. Overall, by combining predictions from the AOD model with higher accuracy and predictions from the non-AOD model to achieve full coverage, our modeling approach could produce long-term, full-coverage historical PM2.5 levels in high-latitude areas in China, despite the widespread and persistent AOD missingness.

Systematically gap-filling the genome-scale metabolic model of CHO cells.

OBJECTIVE: Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells. RESULTS: We expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased. CONCLUSIONS: The present CHO model is an important step towards more complete metabolic models of CHO cells.

Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks.

Optical motion capture is a mature contemporary technique for the acquisition of motion data; alas, it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural network architectures applied to the gap-filling problem in motion capture sequences within the FBM framework providing a representation of body kinematic structure. The results are compared with interpolation and matrix completion methods. We found out that, for longer sequences, simple linear feedforward neural networks can outperform the other, sophisticated architectures, but these outcomes might be affected by the small amount of data availabe for training. We were also able to identify that the acceleration and monotonicity of input sequence are the parameters that have a notable impact on the obtained results.

Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host-microbial interactions.

Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.

Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis.

Methane flux (FCH4 ) measurements using the eddy covariance technique have increased over the past decade. FCH4 measurements commonly include data gaps, as is the case with CO2 and energy fluxes. However, gap-filling FCH4 data are more challenging than other fluxes due to its unique characteristics including multidriver dependency, variabilities across multiple timescales, nonstationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap-filling method for other fluxes, to FCH4 datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH4 . However, there is still no consensus regarding FCH4 gap-filling methods due to limited comparative research. We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH4 datasets. Here, we compare the performance of MDS and three ML algorithms (ANN, random forest [RF], and support vector machine [SVM]) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multidriver dependency of FCH4 and reduce the internal complexity of the algorithmic structures. We applied this approach to five benchmark FCH4 datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA-derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap-filling uncertainty is much larger than measurement uncertainty in accumulated CH4 budget. Therefore, the approach used for FCH4 gap filling can have important implications for characterizing annual ecosystem-scale methane budgets, the accuracy of which is important for evaluating natural and managed systems and their interactions with global change processes.

Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud.

Remote sensing optical sensors onboard operational satellites cannot have high spectral, spatial and temporal resolutions simultaneously. In addition, clouds and aerosols can adversely affect the signal contaminating the land surface observations. We present a HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to combine multispectral images of different sensors to reduce noise and produce monthly gap free high resolution (30 m) observations over land. Our approach uses images from the Landsat (30 m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (500 m spatial resolution and daily revisit cycle). We implement a bias-aware Kalman filter method in the Google Earth Engine (GEE) platform to obtain fused images at the Landsat spatial-resolution. The added bias correction in the Kalman filter estimates accounts for the fact that both model and observation errors are temporally auto-correlated and may have a non-zero mean. This approach also enables reliable estimation of the uncertainty associated with the final reflectance estimates, allowing for error propagation analyses in higher level remote sensing products. Quantitative and qualitative evaluations of the generated products through comparison with other state-of-the-art methods confirm the validity of the approach, and open the door to operational applications at enhanced spatio-temporal resolutions at broad continental scales.