The Need for Accurate Pre-processing and Data Integration for the Application of Hyperspectral Imaging in Mineral Exploration

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften.Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications

In vitro analyses of mitochondrial ATP/phosphate carriers from Arabidopsis thaliana revealed unexpected Ca2+-effects

Background: Adenine nucleotide/phosphate carriers (APCs) from mammals and yeast are commonly known to adapt the mitochondrial adenine nucleotide pool in accordance to cellular demands. They catalyze adenine nucleotide - particularly ATP-Mg - and phosphate exchange and their activity is regulated by calcium. Our current knowledge about corresponding proteins from plants is comparably limited. Recently, the three putative APCs from Arabidopsis thaliana were shown to restore the specific growth phenotype of APC yeast loss-of-function mutants and to interact with calcium via their N-terminal EF-hand motifs in vitro. In this study, we performed biochemical characterization of all three APC isoforms from A. thaliana to gain further insights into their functional properties. Results: Recombinant plant APCs were functionally reconstituted into liposomes and their biochemical characteristics were determined by transport measurements using radiolabeled substrates. All three plant APCs were capable of ATP, ADP and phosphate exchange, however, high preference for ATP-Mg, as shown for orthologous carriers, was not detectable. By contrast, the obtained data suggest that in the liposomal system the plant APCs rather favor ATP-Ca as substrate. Moreover, investigation of a representative mutant APC protein revealed that the observed calcium effects on ATP transport did not primarily/essentially involve Ca2+-binding to the EF-hand motifs in the N-terminal domain of the carrier. Conclusion: Biochemical characteristics suggest that plant APCs can mediate net transport of adenine nucleotides and hence, like their pendants from animals and yeast, might be involved in the alteration of the mitochondrial adenine nucleotide pool. Although, ATP-Ca was identified as an apparent import substrate of plant APCs in vitro it is arguable whether ATP-Ca formation and thus the corresponding transport can take place in vivo

Selective and rapid monitoring of dual platelet inhibition by aspirin and P2Y12 antagonists by using multiple electrode aggregometry

<p>Abstract</p> <p>Background</p> <p>Poor platelet inhibition by aspirin or clopidogrel has been associated with adverse outcomes in patients with cardiovascular diseases. A reliable and facile assay to measure platelet inhibition after treatment with aspirin and a P2Y₁₂antagonist is lacking. Multiple electrode aggregometry (MEA), which is being increasingly used in clinical studies, is sensitive to platelet inhibition by aspirin and clopidogrel, but a critical evaluation of MEA monitoring of dual anti-platelet therapy with aspirin and P2Y₁₂antagonists is missing.</p> <p>Design and Methods</p> <p>By performing <it>in vitro </it>and <it>ex vivo </it>experiments, we evaluated in healthy subjects the feasibility of using MEA to monitor platelet inhibition of P2Y₁₂antagonists (clopidogrel <it>in vivo</it>, cangrelor <it>in vitro</it>) and aspirin (100 mg per day <it>in vivo</it>, and 1 mM or 5.4 mM <it>in vitro</it>) alone, and in combination. Statistical analyses were performed by the Mann-Whitney rank sum test, student' t-test, analysis of variance followed by the Holm-Sidak test, where appropriate.</p> <p>Results</p> <p>ADP-induced platelet aggregation in hirudin-anticoagulated blood was inhibited by 99.3 ± 1.4% by <it>in vitro </it>addition of cangrelor (100 nM; p < 0.001) and by 64 ± 35% by oral clopidogrel (600 mg) intake (p < 0.05; values are means ± SD). Pre-incubation of blood with aspirin (1 mM) or oral aspirin intake (100 mg/day for 1 week) inhibited arachidonic acid (AA)-stimulated aggregation >95% and 100 ± 3.2%, respectively (p < 0.01). Aspirin did not influence ADP-induced platelet aggregation, either <it>in vitro </it>or <it>ex vivo</it>. Oral intake of clopidogrel did not significantly reduce AA-induced aggregation, but P2Y₁₂blockade by cangrelor (100 nM) <it>in vitro </it>diminished AA-stimulated aggregation by 53 ± 26% (p < 0.01). A feasibility study in healthy volunteers showed that dual anti-platelet drug intake (aspirin and clopidogrel) could be selectively monitored by MEA.</p> <p>Conclusions</p> <p>Selective platelet inhibition by aspirin and P2Y₁₂antagonists alone and in combination can be rapidly measured by MEA. We suggest that dual anti-platelet therapy with these two types of anti-platelet drugs can be optimized individually by measuring platelet responsiveness to ADP and AA with MEA before and after drug intake.</p

Detection and Characterization of Low Temperature Peat Fires during the 2015 Fire Catastrophe in Indonesia Using a New High-Sensitivity Fire Monitoring Satellite Sensor (FireBird)

Vast and disastrous fires occurred on Borneo during the 2015 dry season, pushing Indonesia into the top five carbon emitting countries. The region was affected by a very strong El Nino-Southern Oscillation (ENSO) climate phenomenon, on par with the last severe event in 1997/98. Fire dynamics in Central Kalimantan were investigated using an innovative sensor offering higher sensitivity to a wider range of fire intensities at a finer spatial resolution (160 m) than heretofore available. The sensor is onboard the TET-1 satellite, part of the German Aerospace Center (DLR) FireBird mission. TET-1 images (acquired every 2-3 days) from the middle infrared were used to detect fires continuously burning for almost three weeks in the protected peatlands of Sebangau National Park as well as surrounding areas with active logging and oil palm concessions. TET-1 detection capabilities were compared with MODIS active fire detection and Landsat burned area algorithms. Fire dynamics, including fire front propagation speed and area burned, were investigated. We show that TET-1 has improved detection capabilities over MODIS in monitoring low-intensity peatland fire fronts through thick smoke and haze. Analysis of fire dynamics revealed that the largest burned areas resulted from fire front lines started from multiple locations, and the highest propagation speeds were in excess of 500 m/day (all over peat > 2m deep). Fires were found to occur most often in concessions that contained drainage infrastructure but were not cleared prior to the fire season. Benefits of implementing this sensor system to improve current fire management techniques are discussed. Near real-time fire detection together with enhanced fire behavior monitoring capabilities would not only improve firefighting efforts, but also benefit analysis of fire impact on tropical peatlands, greenhouse gas emission estimations as well as mitigation measures to reduce severe fire events in the future

Prognostic model for survival of patients with abdominal aortic aneurysms treated with endovascular aneurysm repair.

The role of endovascular aneurysm repair (EVAR) in patients with asymptomatic abdominal aortic aneurysm (AAA) who are unfit for open surgical repair has been questioned. The impending risk of aneurysm rupture, the risk of elective repair, and the life expectancy must be balanced when considering elective AAA repair. This retrospective observational cohort study included all consecutive patients treated with standard EVAR for AAA at a referral centre between 2001 and 2020. A previously published predictive model for survival after EVAR in patients treated between 2001 and 2012 was temporally validated using patients treated at the same institution between 2013 and 2020 and updated using the overall cohort. 558 patients (91.2% males, mean age 74.9 years) were included. Older age, lower eGFR, and COPD were independent predictors for impaired survival. A risk score showed good discrimination between four risk groups (Harrel's C = 0.70). The 5-years survival probabilities were only 40% in "high-risk" patients, 68% in "moderate-to-high-risk" patients, 83% in "low-to-moderate-risk", and 89% in "low-risk" patients. Low-risk patients with a favourable life expectancy are likely to benefit from EVAR, while high-risk patients with a short life expectancy may not benefit from EVAR at the current diameter threshold

Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops

Recently, ground-based hyperspectral imaging has come to the fore, supporting the arduous task of mapping near-vertical, difficult-to-access geological outcrops. The application of outcrop sensing within a range of one to several hundred metres, including geometric corrections and integration with accurate terrestrial laser scanning models, is already developing rapidly. However, there are few studies dealing with ground-based imaging of distant targets (i.e., in the range of several kilometres) such as mountain ridges, cliffs, and pit walls. In particular, the extreme influence of atmospheric effects and topography-induced illumination differences have remained an unmet challenge on the spectral data. These effects cannot be corrected by means of common correction tools for nadir satellite or airborne data. Thus, this article presents an adapted workflow to overcome the challenges of long-range outcrop sensing, including straightforward atmospheric and topographic corrections. Using two datasets with different characteristics, we demonstrate the application of the workflow and highlight the importance of the presented corrections for a reliable geological interpretation. The achieved spectral mapping products are integrated with 3D photogrammetric data to create large-scale now-called “hyperclouds”, i.e., geometrically correct representations of the hyperspectral datacube. The presented workflow opens up a new range of application possibilities of hyperspectral imagery by significantly enlarging the scale of ground-based measurements

Non-Invasive Time-Lapsed Monitoring and Quantification of Engineered Bone-Like Tissue

The formation of bone-like tissue from human mesenchymal stem cells (hMSC) cultured in osteogenic medium on silk fibroin scaffolds was monitored and quantified over 44days in culture using non-invasive time-lapsed micro-computed tomography (μCT). Each construct was imaged nine times insitu. From μCT imaging, detailed morphometrical data on bone volume density, surface-to-volume ratio, trabecular thickness, trabecular spacing, and the structure model index and tissue mineral density were obtained. μCT irradiation did not impact the osteogenic performance of hMSCs based on DNA content, alkaline phosphatase activity, and calcium deposition when compared to non-exposed control samples. Bone-like tissue formation initiated at day 10 of the culture with the deposition of small mineralized clusters. Tissue mineral density increased linearly over time. The surface-to-volume ratio of the bone-like tissues converged asymptotically to 26mm−1. Although in vitro formation of bone-like tissue started from clusters, the overall bone volume was not predictable from the time, number, and size of initially formed bone-like clusters. Based on microstructural analysis, the morphometry of the tissue-engineered constructs was found to be in the range of human trabecular bone. In future studies, non-invasive, time-lapsed monitoring may enable researchers to culture tissues in vitro, right until the development of a desired morphology is accomplished. Our data demonstrate the feasibility of qualitatively and quantitatively detailing the spatial and temporal mineralization of bone-like tissue formation in tissue engineerin

Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic

Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote sensing sensors. Current outcrop mapping efforts rely on the interpretation of Terrestrial Laser Scanning and oblique photogrammetry, which have inadequate spectral resolution to allow for detection of subtle lithological differences. This study aims to integrate 3D-photogrammetry with vessel-based hyperspectral imaging to complement geological outcrop models with quantitative information regarding mineral variations and thus enables the differentiation of barren rocks from potential economic ore deposits. We propose an innovative workflow based on: (1) the correction of hyperspectral images by eliminating the distortion effects originating from the periodic movements of the vessel; (2) lithological mapping based on spectral information; and (3) accurate 3D integration of spectral products with photogrammetric terrain data. The method is tested using experimental data acquired from near-vertical cliff sections in two parts of Greenland, in Karrat (Central West) and Søndre Strømfjord (South West). Root-Mean-Square Error of (6.7, 8.4) pixels for Karrat and (3.9, 4.5) pixels for Søndre Strømfjord in X and Y directions demonstrate the geometric accuracy of final 3D products and allow a precise mapping of the targets identified using the hyperspectral data contents. This study highlights the potential of using other operational mobile platforms (e.g., unmanned systems) for regional mineral mapping based on horizontal viewing geometry and multi-source and multi-scale data fusion approaches

Time Period From Onset of Pain to Hospital Admission and Patients' Awareness in Acute Pancreatitis

Objectives: This study aimed to explore the period between onset of pain and hospital-admission (pain-to-admission time) in patients with acute pancreatitis (AP), to investigate the prognostic value and associated factors of this time, and to ascertain the knowledge about the pancreas in these patients. Methods: An analysis of a prospective multicenter study was done, which included 188 patients with AP. Results: Median pain-to-admission time was 27 hours (interquartile range, 6.0-72.0). Median pain-to-admission time was significantly shorter in intensive care unit (ICU) patients (10 hours) compared to non-ICU patients (36 hours) (P = 0.045). Short pain-to-admission time was associated with high pain level. Median pain level (0, no pain; 10, maximal pain) was 8.0 (interquartile range, 7.0-10.0). Older age correlated with lower pain level (r = -0.26; P = 0.002). Multiple logistic regression analysis including the admission values for serum lipase and C-reactive protein and the corresponding interactions to the pain-to-admission time showed substantial discriminative ability regarding ICU admission (concordance index, 0.706; P = 0.006). 86% (112/130) knew that they have a pancreas, 72% (81/112) of these patients knew that AP exists, and 56% (45/81) recognized that AP is potentially fatal. Conclusions: Knowledge about AP in hospitalized AP patients is poor. Serum lipase and C-reactive protein in dependency of the pain-to-admission time might be a suitable predictor for severity of AP

Tinto: Multisensor Benchmark for 3-D Hyperspectral Point Cloud Segmentation in the Geosciences

The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2-D image data, which is insufficient for 3-D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multisensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for nonstructured 3-D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground truth. The point cloud is dense and contains 3242964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3-D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256