Mapping Workflow Research Articles

Earth system science applications of next-generation SEM-EDS automated mineral mapping

Sedimentary rocks contain a unique record of the evolution of the Earth system. Deciphering this record requires a robust understanding of the identity, origin, composition, and post-depositional history of individual constituents. Petrographic analysis informed by Scanning Electron Microscope - Energy Dispersive Spectroscopy (SEM-EDS) mineral mapping can reveal the mineral identity, morphology and petrological context of each imaged grain, making it a valuable tool in the Earth Scientist’s analytical arsenal. Recent technological developments, including quantitative deconvolution of mixed-phase spectra (producing “mixels”), now allow rapid quantitative SEM-EDS-based analysis of a broad range of sedimentary rocks, including the previously troublesome fine-grained lithologies that comprise most of the sedimentary record. Here, we test the reliability and preferred mineral mapping work flow of a modern Field-Emission scanning electron microscope equipped with the Thermofisher Scientific Maps Mineralogy mineral mapping system, focusing on mud/siltstones and calcareous shales. We demonstrate that SEM-EDS mineral mapping that implements 1) a strict error minimization spectral matching approach and 2) spectral deconvolution to produce ‘mixels’ for mixed-phase X-ray volumes can robustly identify individual grains and produce quantitative mineralogical data sets comparable to conventional X-ray diffraction (XRD) analysis (R2 > 0.95). The correlation between SEM-EDS and XRD-derived mineralogy is influenced by mineral abundance, processing modes and mapped area characteristics. Minerals with higher abundance (>10 wt%) show better correlation, likely the result of increased uncertainty for XRD quantification of low-abundance phases. Automated spectral deconvolution to produce ‘mixels’ greatly reduces the proportion of unclassified pixels, especially in the fine-grained fraction, ultimately improving mineral identification and quantification. Mapping of larger areas benefits bulk mineralogy analysis, while customized area size and shape allows high-resolution in situ mineralogical analysis. Finally, we review SEM-EDS-based mineral mapping applications in the Earth Sciences, via case studies illustrating 1) approaches for the quantitative differentiation of various mineral components including detrital (allogenic), syndepositional (authigenic) and burial diagenetic phases, 2) the origin and significance of lamination, 3) the effectiveness and appropriateness of sequential leaching in geochemical studies, and 4) the utility of mineral maps to identify target grains within specific petrological contexts for in situ geochemical or geochronological analysis.

Improved IDW Interpolation Application Using 3D Search Neighborhoods: Borehole Data-Based Seismic Liquefaction Hazard Assessment and Mapping

Traditional inverse distance weighting (IDW) interpolation is a process employed to estimate unknown values based on neighborhoods in 2D space. Proposed in this study is an improved IDW interpolation method that uses 3D search neighborhoods for effective interpolation on vertically connected observation data, such as water level, depth, and altitude. Borehole data are the data collected by subsurface boring activities and exhibit heterogeneous spatial distribution as they are densely populated near civil engineering or construction sites. In addition, they are 3D spatial data that show different subsurface characteristics by depth. The subsurface characteristics observed as such are used as core data in spatial modeling in fields, such as geology modeling, estimation of groundwater table distribution, global warming assessment, and seismic liquefaction assessment, among others. Therefore, this study proposed a seismic liquefaction assessment and mapping workflow using an improved IDW application by combining geographic information system (GIS) (ArcGIS (Esri, Redlands, CA, USA)), NURBS-based 3D CAD system (Rhino/Grasshopper (Robert McNeel & Associates, Seattle, WA, USA)), and numerical analysis system (MATLAB (MathWorks, Natick, MA, USA)). The 3D neighborhood search was conducted by the B-rep-based 3D topology analysis, and the mapping was done under the 2.5D environment by combining the voxel layer, DEM, and aerial images. The experiment was performed by collecting data in Songpa-gu, Seoul, which has the highest population density among the OECD countries. The results of the experiment showed between 7 and 105 areas with liquefaction potentials according to the search distance and the method of the approach. Finally, this study improved users’ accessibility to interpolation results by producing a 3D web app that used REST API based on OGC I3S Standards. Such an approach can be applied effectively in spatial modeling that uses 3D observation data, and in the future, it can contribute to the expansion of 3D GIS application.

Optimized Multi-Attribute Method Workflow Addressing Missed Cleavages and Chromatographic Tailing/Carry-Over of Hydrophobic Peptides.

Peptide mapping by liquid chromatography mass spectrometry (LC-MS) and the related multi-attribute method (MAM) are well-established analytical tools for verification of the primary structure and mapping/quantitation of co- and post-translational modifications (PTMs) or product quality attributes in biopharmaceutical development. Proteolytic digestion is a key step in peptide mapping workflows, which traditionally is labor-intensive, involving multiple manual steps. Recently, simple high-temperature workflows with automatic digestion were introduced, which facilitate robustness and reproducibility across laboratories. Here, a modified workflow with an automatic digestion step is presented, which includes a two-step digestion at high and low temperatures, as opposed to the original one-step digestion at a high temperature. The new automatic digestion workflow significantly reduces the number of missed cleavages, obtaining a more complete digestion profile. In addition, we describe how chromatographic peak tailing and carry-over is dramatically reduced for hydrophobic peptides by switching from the traditional C18 reversed-phase (RP) column chemistry used for peptide mapping to a less retentive C4 column chemistry. No negative impact is observed on MS/MS-derived sequence coverage when switching to a C4 column chemistry. Overall, the new peptide mapping workflow significantly reduces the number of missed cleavages, yielding more robust and simple data interpretation, while providing dramatically reduced tailing and carry-over of hydrophobic peptides.

138. Optical Genome Mapping workflow for identification and analysis of variants in Hematological Malignancies

Clinical guidelines for hematological malignancies include structural variation (SV) analysis as part of the genetic analysis. Currently, this requires a combination of three cytogenetic technologies: karyotyping, fluorescence in situ hybridization (FISH), and chromosomal microarray (CMA) to detect copy number variants, translocations, inversions, and other rearrangements. Next generation sequencing is also applied for small variant analysis but not successfully used for comprehensive SV analysis. Optical genome mapping (OGM) consolidates these cytogenetic techniques into a single assay capable of detecting all important genomic structural abnormalities. Furthermore, OGM detects cryptic SVs in a significant portion of cases and results in change interpretation of the biology underlying the malignancy in many cases. OGM is able to comprehensively detect variants down to 5% variant allele fraction from blood, bone marrow aspirates, and tumor biopsies, making it an ideal choice for oncology clinical research. Preanalytical and analytical steps require approximately 3-5 days from input sample to analyzed results. Dynamic filtering in OGM software can remove most polymorphic variants and prioritize relevant variants. In addition, it allows assignment of classification to variants and multiple user level review process (e.g. preliminary analysis by lab scientist and final review by lab supervisor). For example, an ALL case with t(9;22), deletion of CDKN2A, and whole chromosome gains of 4,6, and 10 can be easily visualized and then further examined and classified as needed. A variety of cases with hallmark abnormalities from various leukemias is presented here. This comprehensive technology allows for a quicker, more reliable output than traditional cytogenetic approaches.

66. Optical genome mapping workflow for Somatic Abnormality detection in Multiple Solid Tumor types

66. Optical genome mapping workflow for Somatic Abnormality detection in Multiple Solid Tumor types

A Novel Workflow for Crop Type Mapping with a Time Series of Synthetic Aperture Radar and Optical Images in the Google Earth Engine

High-resolution crop type mapping is of importance for site-specific agricultural management and food security in smallholder farming regions, but is challenging due to limited data availability and the need for image-based algorithms. In this paper, we developed an efficient object- and pixel-based mapping algorithm to generate a 10 m resolution crop type map over large spatial domains by integrating time series optical images (Sentinel-2) and synthetic aperture radar (SAR) images (Sentinel-1) using the Google Earth Engine (GEE) platform. The results showed that the proposed method was reliable for crop type mapping in the study area with an overall accuracy (OA) of 93.22% and a kappa coefficient (KC) of 0.89. Through experiments, we also found that the monthly median values of the vertical transmit/vertical receive (VV) and vertical transmit/horizontal receive (VH) bands were insensitive to crop type mapping itself, but adding this information to supplement the optical images improved the classification accuracy, with an OA increase of 0.09–2.98%. Adding the slope of vegetation index change (VIslope) at the critical period to crop type classification was obviously better than that of relative change ratio of vegetation index (VIratio), both of which could make an OA improvement of 2.58%. These findings not only highlighted the potential of the VIslope and VIratio indices during the critical period for crop type mapping in small plots, but suggested that SAR images could be included to supplement optical images for crop type classification.

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.

Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.

A Plug and Play Approach to Vocabulary Mapping and Document Annotation

ABSTRACTThis study investigates the utility of open‐source data analytics, reporting, and integration tools for mapping knowledge organization systems (KOS) dedicated to COVID‐19. Using various natural language processing and machine learning methods, a workflow was created with the KNIME Analytics Platform for term mapping and document annotation tasks using string‐based, sense‐based, and rule‐based algorithmic methods. Results suggest strong support for mapping vocabularies within similar domains and appropriate tagging of unstructured data with concepts from an integrated dictionary created from mapped terms. This study demonstrates the use of a shareable, easily adaptable workflow for KOS mapping and annotating documents focused on COVID‐19 research. These mapped terms were then used for annotation of clinical trials. The results suggest that researchers could select and align vocabularies of interest and then use the results to annotate documents of interest.

Seismic reservoir characterization of the Gassum Formation in the Stenlille aquifer gas storage, Denmark — Part 2: Unsupervised classification

Ideally, a good static reservoir model provides an accurate estimate of the extent, porosity, permeability, and lithology of the container as well as the properties of the seal and any faults or fractures that may allow the reservoir to leak. The major pitfall of deterministic, statistical, or supervised learning workflows is that they estimate only the properties sampled by the wells or provided by empirical relations and may miss mapping heterogeneities in the reservoir and seal that can give rise to flow baffles and reservoir leakage. This shortcoming is exacerbated when the number of wells is small, the types of logs recorded are limited, and the migrated seismic gathers are absent or of limited quality. In contrast, unsupervised learning looks for patterns in the seismic amplitude and attribute volumes themselves. In this paper, we apply unsupervised learning algorithms to evaluate the natural gas storage Stenlille aquifer in Denmark and compare the results with a supervised multiattribute regression reservoir characterization described in a companion paper. Specifically, we apply principal component analysis, self-organizing mapping, and generative topographic mapping workflows to extract patterns across eight attribute volumes: relative acoustic impedance, energy, sweetness, gray level co-occurrence matrices (GLCM) entropy, curvedness, and three spectral magnitude volumes. We find that the large-scale patterns are similar, but that the unsupervised learning algorithms provide greater detail. Because our deterministic model was built on poststack data using the limited well-log data available, we believe that the heterogeneity mapped by the unsupervised learning workflows provides a relatively unbiased means of estimating risk in our reservoir model. Quantifying the importance of these anomalies will need to be reconciled with a dynamic reservoir model.

Reconstruction of Land and Marine Features by Seismic and Surface Geomorphology Techniques

Seismic reflection utilizes sound waves transmitted into the subsurface, reflected at rock boundaries, and recorded at the surface. Interpretation of their travel times and amplitudes are the key for reconstructing various geomorphological features across geological time (e.g., reefs, dunes, and channels). Furthermore, the integration of surface geomorphology technique mapping, such as digital elevation models, with seismic geomorphology can increase land and marine feature modelling and reduce data uncertainty, as well. This paper presents an overview of seismic and surface geomorphology techniques and proposes an integrated workflow for better geological mapping, 3D surface imaging, and reconstruction. We intend to identify which techniques are more often used and which approaches are more appropriate for better output results. We noticed that an integration of surface and subsurface geomorphology techniques could be beneficial for society in landscape mapping, reservoir characterization, and city/regional planning.

Adapting Museum Workflows to Accommodate Omic-based, Multi-species Occurrences from Environmental Samples

Natural history museums play a critical role in curating global anthropological, biological and geological diversity. Museums host more than voucher samples for comparative identification purposes. They host a suite of derived products and associated information, which cascade from these records of diversity in time and space. Increasingly, museums capture whole communities through curation of environmental samples and their derived data. Environmental samples are defined as material samples that yield multi-species occurrence records that cannot be sorted into traditional independent lots and whose membership can only be determined by downstream analytical processes, usually based on the generation of DNA sequence profiles. These materials include samples such as filtered water; feces, gut dissections or gastric lavage; scraped and blended settlement plates; as well as sediment, ice or soil cores. Given the level of ongoing and predicted global change, through the curation of these environmental samples, museums provide time capsules of diversity and longitudinal breadth to examine temporal patterns in phenology, range shifts, species interactions and diversity loss or gain. The future of natural history museums is likely to expand from providing inventory-like services to more observatory functions. They should provide strategic windows to the past by partnering with place-based observatories or targeted regions of predicted change. Rather than individual specimens, environmental samples represent entire systems as events, defined by space and time. Thus, these environmental samples are explicitly event-centric. The cascade of derived information, protocols and handling steps, as well as analytical processes and their associated metadata, needs to be tracked via the minting of persistent identifiers at each material sample and derived data step. Efforts are underway through collaboratively funded programs like the iSamples Cyberinfrastructure group, funded by the US National Science Foundation, to semantically map these various material samples while capturing their parent-child relationships. Moreover, museums, such as the Smithsonian’s National Museum of Natural History, are scoping and examining existing systems of record, metadata models and mapping workflows, with particular emphasis on environmental samples. These event-based samples create new challenges for historically taxonomically-siloed departments pertaining to custodianship, permitting, versioning of biodiversity occurrence records based on improved reference libraries, and future use and reuse of these material samples. Management systems are being augmented or adopted to encompass these new workflows to ensure that today’s samples can support tomorrow’s science in service to a sustainable future.

Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data

CONTEXTMapping crop types from satellite images is a promising application in agricultural systems. However, it is a challenge to automate in-season crop type mapping over a large area because of the insufficiency of ground truth and issues of scalability, reusability, and accessibility of the classification model. This study introduces a framework for automatic crop type mapping using spatiotemporal crop information and Sentinel-2 data based on Google Earth Engine (GEE). The main advantage of the framework is using the trusted pixels extracted from the historical Cropland Data Layer (CDL) to replace ground truth and label training samples in satellite images. OBJECTIVEThis paper will achieve three objectives: (1) assessing spatiotemporal crop information derived from the historical crop cover maps; (2) mapping crop cover, mainly crop fields without regular historical crop rotation patterns, from remote sensing data using supervised learning classification and validating mapping results; and (3) automating in-season crop mapping and exploring the scalability of the framework. METHODSThe proposed crop mapping workflow consists of four stages. The data preparation stage preprocesses CDL and Sentinel-2 data into the required structure. The spatiotemporal crop information sampling stage extracts trusted pixels from the historical CDL time series and labels Sentinel-2 data. Then a crop type classification model can be trained using the supervised learning classifier in the model training stage. In the mapping/validation stage, an in-season crop cover map over the full Sentinel-2 tile will be produced using the trained model and the classification performance will be validated using CDL or other ground truth data. RESULTS AND CONCLUSIONSWe systematically perform a group of experiments for in-season mapping of five major crop types (corn, cotton, rice, soybeans, and soybeans-wheat double cropping) over the Mississippi Delta region. The result indicates that the crop cover map of the study area is expected to reach 80%–90% agreement with CDL within the growing season. To further facilitate the use of the framework, we also develop a GEE-enabled online prototype, In-season Crop Mapping Kit, and explore its scalability over agricultural fields in various ecoregions including California, Idaho, Kansas, and Illinois. SIGNIFICANCEThe mapping-without-ground-truth approach described in this paper can significantly reduce ground truthing process and save substantial resource needs and labor costs, which is applicable to the production of in-season CDL-like data for the entire United States. The findings and outputs will benefit the agriculture community and other agricultural sectors ranging from government, academia, and companies.

Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

The accurate detection of burned forest area is essential for post-fire management and assessment, and for quantifying carbon budgets. Therefore, it is imperative to map burned areas accurately. Currently, there are few burned-area products around the world. Researchers have mapped burned areas directly at the pixel level that is usually a mixture of burned area and other land cover types. In order to improve the burned area mapping at subpixel level, we proposed a Burned Area Subpixel Mapping (BASM) workflow to map burned areas at the subpixel level. We then applied the workflow to Sentinel 2 data sets to obtain burned area mapping at subpixel level. In this study, the information of true fire scar was provided by the Department of Emergency Management of Hunan Province, China. To validate the accuracy of the BASM workflow for detecting burned areas at the subpixel level, we applied the workflow to the Sentinel 2 image data and then compared the detected burned area at subpixel level with in situ measurements at fifteen fire-scar reference sites located in Hunan Province, China. Results show the proposed method generated successfully burned area at the subpixel level. The methods, especially the BASM-Feature Extraction Rule Based (BASM-FERB) method, could minimize misclassification and effects due to noise more effectively compared with the BASM-Random Forest (BASM-RF), BASM-Backpropagation Neural Net (BASM-BPNN), BASM-Support Vector Machine (BASM-SVM), and BASM-notra methods. We conducted a comparison study among BASM-FERB, BASM-RF, BASM-BPNN, BASM-SVM, and BASM-notra using five accuracy evaluation indices, i.e., overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), intersection over union (IoU), and Kappa coefficient (Kappa). The detection accuracy of burned area at the subpixel level by BASM-FERB’s OA, UA, IoU, and Kappa is 98.11%, 81.72%, 74.32%, and 83.98%, respectively, better than BASM-RF’s, BASM-BPNN’s, BASM-SVM’s, and BASM-notra’s, even though BASM-RF’s and BASM-notra’s average PA is higher than BASM-FERB’s, with 89.97%, 91.36%, and 89.52%, respectively. We conclude that the newly proposed BASM workflow can map burned areas at the subpixel level, providing greater accuracy in regards to the burned area for post-forest fire management and assessment.

Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

Migratory shorebirds are notable consumers of benthic invertebrates on intertidal sediments. The distribution and abundance of shorebirds will strongly depend on their prey and on landscape and sediment features such as mud and surface water content, topography, and the presence of ecosystem engineers. An understanding of shorebird distribution and ecology thus requires knowledge of the various habitat types which may be distinguished in intertidal areas. Here, we combine Sentinel-1 and Sentinel-2 imagery and a digital elevation model (DEM), using machine learning techniques to map intertidal habitat types of importance to migratory shorebirds and their benthic prey. We do this on the third most important non-breeding area for migratory shorebirds in the East Atlantic Flyway, in the Bijagós Archipelago in West Africa. Using pixel-level random forests, we successfully mapped rocks, shell beds, and macroalgae and distinguished between areas of bare sediment and areas occupied by fiddler crabs, an ecosystem engineer that promotes significant bioturbation on intertidal flats. We also classified two sediment types (sandy and mixed) within the bare sediment and fiddler crab areas, according to their mud content. The overall classification accuracy was 82%, and the Kappa Coefficient was 73%. The most important predictors were elevation, the Sentinel-2-derived water and moisture indexes, and Sentinel-1 VH band. The association of Sentinel-2 with Sentinel-1 and a DEM produced the best results compared to the models without these variables. This map provides an overall picture of the composition of the intertidal habitats in a site of international importance for migratory shorebirds. Most of the intertidal flats of the Bijagós Archipelago are covered by bare sandy sediments (59%), and ca. 22% is occupied by fiddler crabs. This likely has significant implications for the spatial arrangement of the shorebird and benthic invertebrate communities due to the ecosystem engineering by the fiddler crabs, which promotes two vastly different intertidal species assemblages. This large-scale mapping provides an important product for the future monitoring of this high biodiversity area, particularly for ecological research related to the distribution and feeding ecology of the shorebirds and their prey. Such information is key from a conservation and management perspective. By delivering a successful and comprehensive mapping workflow, we contribute to the filling of the current knowledge gap on the application of remote sensing and machine learning techniques within intertidal areas, which are among the most challenging environments to map using remote sensing techniques.

Seamless Navigation, 3D Reconstruction, Thermographic and Semantic Mapping for Building Inspection.

We present a workflow for seamless real-time navigation and 3D thermal mapping in combined indoor and outdoor environments in a global reference frame. The automated workflow and partly real-time capabilities are of special interest for inspection tasks and also for other time-critical applications. We use a hand-held integrated positioning system (IPS), which is a real-time capable visual-aided inertial navigation technology, and augment it with an additional passive thermal infrared camera and global referencing capabilities. The global reference is realized through surveyed optical markers (AprilTags). Due to the sensor data’s fusion of the stereo camera and the thermal images, the resulting georeferenced 3D point cloud is enriched with thermal intensity values. A challenging calibration approach is used to geometrically calibrate and pixel-co-register the trifocal camera system. By fusing the terrestrial dataset with additional geographic information from an unmanned aerial vehicle, we gain a complete building hull point cloud and automatically reconstruct a semantic 3D model. A single-family house with surroundings in the village of Morschenich near the city of Jülich (German federal state North Rhine-Westphalia) was used as a test site to demonstrate our workflow. The presented work is a step towards automated building information modeling.

Abstract 2931: Optical genome mapping workflow for identification and annotation of variants in hematological diseases

Abstract Guidelines from WHO, NCCN and others, for the genetic analysis of hematological malignancies included structural variation analysis. Traditionally, this has relied on a combination of three cytogenetic technologies for structural variation analysis: karyotyping, FISH, and microarray, are used to detect copy number variants, translocations, inversions. Next generation sequencing is also applied for mutation analysis but has not been successful for structural variation analysis. These traditional methods have many very manual aspects and require extensive expertise. Optical genome mapping (OGM) consolidates assays into a single laboratory assay in which the output provides the visualization of structural and copy number variants at one time. OGM is able to comprehensively detect structural variations genome wide down to 5% variant allele fraction for CNVs, inversions, and translocations from blood and bone marrow aspirates making it an attractive choice for hematologic malignancy genomic analysis. Preanalytical and analytical steps require approximately 4-5 days from sample to processed data with structural variation calls. Dynamic filtering in the user interface can be configured to remove most polymorphic variants and prioritize relevant variants. In addition, the OGM graphical user interface software, Bionano Access 1.7, allows for the user to assign classification/relevance to the variants for each case. For example, an ALL sample with t(9;22), deletion of CDKN2A, and whole chromosome gains of 4,6, and 10 can be easily visualized with the Circos plot and, then, can be further examined and annotated as needed. A second analyst can repeat the process blind to the first analysis and a supervisor can adjudicate the classifications. A variety of cases with hallmark abnormalities from various leukemias will be presented with the filtering and prioritization workflow used to derive them. This comprehensive technology allows for a quicker, more reliable output than traditional cytogenetic approaches. Citation Format: Jennifer Hauenstein, Andy Pang, Alka Chaubey, Alex Hastie. Optical genome mapping workflow for identification and annotation of variants in hematological diseases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2931.

Mapping Sensitive Vegetation Communities in Mining Eco-space using UAV-LiDAR

Near earth sensing from uncrewed aerial vehicles or UAVs has emerged as a potential approach for fine-scale environmental monitoring. These systems provide a cost-effective and repeatable means to acquire remotely sensed images in unprecedented spatial detail and a high signal-to-noise ratio. It is increasingly possible to obtain both physiochemical and structural insights into the environment using state-of-art light detection and ranging (LiDAR) sensors integrated onto UAVs. Monitoring sensitive environments, such as swamp vegetation in longwall mining areas, is essential yet challenging due to their inherent complexities. Current practices for monitoring these remote and challenging environments are primarily ground-based. This is partly due to an absent framework and challenges of using UAV-based sensor systems in monitoring such sensitive environments. This research addresses the related challenges in developing a LiDAR system, including a workflow for mapping and potentially monitoring highly heterogeneous and complex environments. This involves amalgamating several design components, including hardware integration, calibration of sensors, mission planning, and developing a processing chain to generate usable datasets. It also includes the creation of new methodologies and processing routines to establish a pipeline for efficient data retrieval and generation of usable products. The designed systems and methods were applied to a peat swamp environment to obtain an accurate geo-spatialised LiDAR point cloud. Performance of the LiDAR data was tested against ground-based measurements on various aspects, including visual assessment for generation LiDAR metrices maps, canopy height model, and fine-scale mapping.

9. Optical genome mapping workflow for identification and annotation of variants in hematological malignancy

Hematological malignancy genetic analysis guidelines from WHO, NCCN, and others have centered around structural and copy number variants. This focus has traditionally relied on a combination of three cytogenetic technologies: karyotyping, FISH, and microarray. These traditional methods rely heavily on manual interpretation and require extensive expertise. Optical genome mapping (OGM) consolidates approaches into a single laboratory assay in which the output provides simultaneous analysis of structural and copy number variants. OGM can comprehensively detect these variations genome-wide down to 5% variant allele fraction from fresh or frozen bone marrow aspirates and blood. Laboratory and analytical steps require four to five days from sample to datasets with annotated variant calls, making it an attractive choice for hematologic malignancy genomic analysis. Dynamic filtering in the OGM graphical user interface (Bionano Access™) can be configured to remove polymorphic variants and prioritize the most promising candidates. Subsequently, the software allows analysts to evaluate genome-wide variants and classify each based on relevance. For example: an ALL dataset with t(9;22), deletion of CDKN2A, and whole chromosome gains of 4, 6, and 10 can be loaded into circos plot visualization with pre-defined filters focused on relevant variants. Analysts can then examine and notate each variant independently of one another, then submit completed classifications to a lab director. The director can then adjudicate classifications, leave notes, and download the final output. A variety of cases with hallmark abnormalities from various malignancies will be presented with the filtering and prioritization workflow used to derive them. This comprehensive approach allows for a quicker, more reliable output than traditional cytogenetic approaches.

Integration of structural and functional data in VT ablation -- SENSE2 protocol mapping

Abstract Funding Acknowledgements Type of funding sources: None. Background We have previously developed the sense protocol functional substrate mapping technique for VT ablation(1). However, functional substrate characterizaiton can involve protracted mapping time. Purpose We incorporated the integration of MRI data using ADAS-3D software into the mapping workflow, to integrate structural mapping information into the functional mapping substrate characterization, in order to improve procedural efficiency. Methods CMRs were performed in 20 patients with ischemic related VT and VT therapy in the previous 6 months. These were processed with the ADAS-3D software to characterize the extent of ventricular scars and also ADAS corridors which may correlate with VT channels. Focused substrate maps were then performed in patients, guided by the extent of ADAS scar and corridors, looking at the scar substrate in intrinsic rhythm and then functional channels using single extra pacing from the RV at 20ms above ERP (SENSE2 Protocol). Specifically healthy areas 2cm beyond the scar borderzone based on ADAS were not mapped, in order to reduce substrate mapping time and complete geometries were not created. Following delineation of functional channels pacemapping and entrainment mapping were used to confirm targets for ablation. The ADAS 3D MRI was integrated into the into the VT substrate map on Ensite-Precision with alignment to the aorta, RV and PA (Figure 1). We compared our data with previous functional mapping data without the integration of MRI. Results 20 patients (age 70 years; 19 male subjects) underwent ablation. Mean EF 28%. Median procedure time was 161 minutes compared with 246 minutes (in our previous study)(p=<0.001) Mean substrate mapping time was 32 mins vs 63 mins (p=<0.001). Mean ablation time was 22 mins vs 32 mins (p=0.11). 85% (17 of 20) patients were free from symptomatic VT/ anti-tachycardia pacing or implantable cardioverter defibrillator shocks at a median follow-up of 171 days. The mean VT burden was reduced from 22 events per patient in the 6 months’ pre-ablation to 1 event per patient in the median follow up period of 171 days post ablation (p=0.02). Mean shocks per-patient burden decreased from 3.5 to 0.08 in the same time period(p=0.03). Conclusion The SENSE2 protocol involves the integration of structural and functional data into the VT workflow for substrate characterization. It enables focused substrate maps to be performed without the need for complete geometry to be created in large ventricles. Outcomes compare favourably with our previous data but with significantly shorter procedure times. This streamlined workflow has the potential to improve care in VT ablation by shortening procedure times with similar outcomes which may reduce risks for the patient. Figure 1: Comparison of Voltage Map with MRI scar & corridors using ADAS

Transmural voltage gradient dispersion and heterogeneity in brugada syndrome phenotype - a novel workflow for advanced mapping using endocardial unipolar electrograms J-point elevation

Abstract Funding Acknowledgements Type of funding sources: None. Background Differential action potential duration shortening across the right ventricular (RV) myocardial wall is primarily responsible for the Brugada Syndrome (BrS) phenotype [1]. To date, data on electrical substrate characterization in humans with BrS phenotype is limited and risk evaluation is still controversial. Purpose We hypothesized that Uni-JEl mapping could be used as a marker of transmural voltage gradient dispersion resulting. Our aim was to evaluate Uni-JEl mapping in defining arrhythmogenic substrates in patients with BrS phenotype. Methods 12 patients were included in our analysis. 2 normal patients provided control data and 10 asymptomatic subjects with spontaneous type-1 BrS underwent 3D RV mapping (CARTO3 System, Biosense Webster). Among BrS patients we had 3 patients with arrhythmic events (aborted sudden death or appropriate ICD therapies) during follow-up (median 56, interquartile range: 46-74 months) and 7 patients without arrhythmic events. In the former group we had 1 patient with inducibility of VT/VF during EPS (EPS+) and 2 patients non-inducibles during EPS (EPS-), in the latter group we had 3 patients with EPS+ and 4 patients with EPS-. Electrophysiological data and signals were exported and OpenEP [2] was used to convert Carto proprietary data formats into Matlab format (Fig.1). Uni-JEl was calculated for each point map as the unipolar value at J point on surface electrocardiogram. Uni-JEl values were then interpolated in Paraview to create Uni-JEl maps, interpolating data points on the mesh cell (Fig.1). Finally, a region of interest (ROI) was selected and the calculation of mean Uni-JEI (MUni-JEI, as a measure of voltage gradient dispersion), interquartile range and range (intrqUni-JEI and ∆Uni-JEI, as markers of heterogeneity of dispersion) was performed. Results are shown as mean ± standard deviation for the group of BrS patients and the actual values for the two controls. Results BrS patients showed Muni-JEl, intrqUni-JEl and ∆Uni-JEI higher than controls (2.03 mV ± 0.31 mV vs 0,82 mV and 1,1 mV, 1.90 mV ± 0.82 mV vs 1,04 mV and 1,18 mV 6.26 mV ± 1.98 mV vs 3,54 mV and 4,01 mV, respectively). BrS patients with arrhythmic events during the follow-up showed higher intrqUni-JEl and the ∆Uni-JEI respect to BrS with EPS+ and without arrhythmic events during follow-up (2.31 mV ± 0.44 mV vs 0.78 mV ± 0.11 mV and 6.69 mV ± 2.27 mV vs 3.98 mV ± 0.31 mV). Figure 2 shows some examples of calculated Uni-JEl maps for each group under study. Conclusions In this work we introduced a novel workflow for the electrical substrate characterization of subjects with BrS phenotype. The results from our preliminary analysis indicate that a higher transmural voltage gradient dispersion and heterogeneity can be found in type-1 BrS with respect to normal subjects. Voltage gradient dispersion heterogeneity could be used to better recognize high risk BrS patients regardless of VT/VF inducibility during EPS.