Quantifying rapid permafrost thaw with computer vision and graph theory

With the Earth’s climate rapidly warming, the Arctic represents one of the most vulnerable regions to environmental change. Permafrost, as a key element of the Arctic system, stores vast amounts of organic carbon that can be microbially decomposed into the greenhouse gases CO2 and CH4 upon thaw. Extensive thawing of these permafrost soils therefore has potentially substantial consequences to greenhouse gas concentrations in the atmosphere. In addition, thaw of ice-rich permafrost lastingly alters the surface topography and thus the hydrology. Fires represent an important disturbance in boreal permafrost regions and increasingly also in tundra regions as they combust the vegetation and upper organic soil layers that usually provide protective insulation to the permafrost below. Field studies and local remote sensing studies suggest that fire disturbances may trigger rapid permafrost thaw, with consequences often already observable in the first years post-disturbance. In polygonal ice-wedge landscapes, this becomes most prevalent through melting ice wedges and degrading troughs. The further these ice wedges degrade; the more troughs will likely connect and build an extensive hydrological network with changing patterns and degrees of connectivity that influences hydrology and runoff throughout large regions. While subsiding troughs over melting ice wedges may host new ponds, an increasing connectivity may also subsequently lead to more drainage of ponds, which in turn can limit further thaw and help stabilize the landscape. Whereas fire disturbances may accelerate the initiation of this process, the general warming of permafrost observed across the Arctic will eventually result in widespread degradation of polygonal landscapes. To quantify the changes in such dynamic landscapes over large regions, remote sensing data offers a valuable resource. However, considering the vast and ever-growing volumes of Earth observation data available, highly automated methods are needed that allow extracting information on the geomorphic state and changes over time of ice-wedge trough networks. In this study, we investigate these changing landscapes and their environmental implications in fire scars in Northern and Western Alaska. We developed a computer vision algorithm to automatically extract ice-wedge polygonal networks and the microtopography of the degrading troughs from high-resolution, airborne laserscanning-based digital terrain models (1 m spatial resolution; full-waveform Riegl Q680i LiDAR sensor). To derive information on the availability of surface water, we used optical and near-infrared aerial imagery at spatial resolutions of up to 5 cm captured by the Modular Aerial Camera System (MACS) developed by DLR. We represent the networks as graphs (a concept from the computer sciences to describe complex networks) and apply methods from graph theory to describe and quantify hydrological network characteristics of the changing landscape. Due to a lack of historical very-high-resolution data, we cannot investigate a dense time series of a single representative study area on the evolution of the microtopographic and hydrologic network, but rather leverage the possibilities of a space-for-time substitution. We thus investigate terrain models and multispectral data from 2019 and 2021 of ten study areas located in ten fire scars of different ages (up to 120 years between date of disturbance and date of data acquisition). With this approach, we can infer past and future states of degradation from the currently prevailing spatial patterns and show how this type of disturbed landscape evolves over time. Representing such polygonal landscapes as graphs and reducing large amounts of data into few quantifiable metrics, supports integration of results into i.e., numerical models and thus largely facilitates the understanding of the underlying complex processes of GHG emissions from permafrost thaw. We highlight these extensive possibilities but also illustrate the limitations encountered in the study that stem from a reduced availability and accessibility to pan-Arctic very-high-resolution Earth observation datasets

A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes

In response to increasing Arctic temperatures, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowlands, polygonal ice wedges are especially prone to degradation. Melting of ice wedges results in deepening troughs and the transition from low-centered to high-centered ice-wedge polygons. This process has important implications for surface hydrology, as the connectivity of such troughs determines the rate of drainage for these lowland landscapes. In this study, we present a comprehensive, modular, and highly automated workflow to extract, to represent, and to analyze remotely sensed ice-wedge polygonal trough networks as a graph (i.e., network structure). With computer vision methods, we efficiently extract the trough locations as well as their geomorphometric information on trough depth and width from high-resolution digital elevation models and link these data within the graph. Further, we present and discuss the benefits of graph analysis algorithms for characterizing the erosional development of such thaw-affected landscapes. Based on our graph analysis, we show how thaw subsidence has progressed between 2009 and 2019 following burning at the Anaktuvuk River fire scar in northern Alaska, USA. We observed a considerable increase in the number of discernible troughs within the study area, while simultaneously the number of disconnected networks decreased from 54 small networks in 2009 to only six considerably larger disconnected networks in 2019. On average, the width of the troughs has increased by 13.86%, while the average depth has slightly decreased by 10.31%. Overall, our new automated approach allows for monitoring ice-wedge dynamics in unprecedented spatial detail, while simultaneously reducing the data to quantifiable geometric measures and spatial relationships.BMBF PermaRiskNational Science FoundationPeer Reviewe

From Images to Hydrologic Networks - Understanding the Arctic Landscape with Graphs

Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gathered from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited

Bioactive Thymosin Alpha-1 Does Not Influence F508del-CFTR Maturation and Activity.

Deletion of phenylalanine 508 (F508del) in the cystic fibrosis transmembrane conductance regulator (CFTR) anion channel is the most frequent mutation causing cystic fibrosis (CF). F508del-CFTR is misfolded and prematurely degraded. Recently thymosin a-1 (Tα-1) was proposed as a single molecule-based therapy for CF, improving both F508del-CFTR maturation and function by restoring defective autophagy. However, three independent laboratories failed to reproduce these results. Lack of reproducibility has been ascribed by the authors of the original paper to the use of DMSO and to improper handling. Here, we address these potential issues by demonstrating that Tα-1 changes induced by DMSO are fully reversible and that Tα-1 peptides prepared from different stock solutions have equivalent biological activity. Considering the negative results here reported, six independent laboratories failed to demonstrate F508del-CFTR correction by Tα-1. This study also calls into question the autophagy modulator cysteamine, since no rescue of mutant CFTR function was detected following treatment with cysteamine, while deleterious effects were observed when bronchial epithelia were exposed to cysteamine plus the antioxidant food supplement EGCG. Although these studies do not exclude the possibility of beneficial immunomodulatory effects of thymosin α-1, they do not support its utility as a corrector of F508del-CFTR

Case Report Multicentric Giant Cell Tumor of Bone: Synchronous and Metachronous Presentation

A 27-year-old man treated 2.5 years ago for synchronous multicentric giant cell tumor of bone located at the right proximal humerus and the right 5th finger presented now with complaints of pain in his right hip and wrist of two-month duration. Radiology and magnetic resonance revealed multicentric giant cell tumor lesions of the right proximal femur, the left ileum, the right distal radius, and the left distal tibia. The patient has an eighteen-year history of a healed osteosarcoma of the right tibia that was treated with chemotherapy, resection, and allograft reconstruction. A literature review establishes this as the first reported case of a patient with synchronous and metachronous multicentric giant cell tumor who also has a history of osteosarcoma

Preanalytical variables and performance of diagnostic RNA-based gene expression analysis in breast cancer

Prognostic multigene expression assays have become widely available to provide additional information to standard clinical parameters and to support clinicians in treatment decisions. In this study, we analyzed the impact of variations in tissue handling on the diagnostic EndoPredict test results. EndoPredict is a quantitative reverse transcription PCR assay conducted on RNA from formalin-fixed, paraffin-embedded (FFPE) tissue that predicts the likelihood of distant recurrence in patients with ER-positive/HER2-negative breast cancer. In this study, we performed a total of 138 EndoPredict assays to study the effects of preanalytical variables such as time to fixation, fixation time, tumor cell content, and section storage time on the EndoPredict test results. A time to fixation of up to 12 h and fixation of up to 5 days did not affect the results of the gene expression test. Paired samples of FFPE sections with tumor cell content ranging from 15 to 95 % and tumor-enriched samples showed a correlation coefficient of 0.97. Test results of tissue sections that have been stored for 12 months at +4 or +20 °C showed a correlation of 0.99 when compared to results of nonstored sections. In conclusion, preanalytical tissue handling is not a critical factor for diagnostic gene expression analysis with the EndoPredict assay. The test can therefore be easily integrated into the standard workflow of molecular pathology

Some gating potentiators, including VX-770, diminish ΔF508-CFTR functional expression.

Cystic fibrosis (CF) is caused by mutations in the CF transmembrane regulator (CFTR) that result in reduced anion conductance at the apical membrane of secretory epithelia. Treatment of CF patients carrying the G551D gating mutation with the potentiator VX-770 (ivacaftor) largely restores channel activity and has shown substantial clinical benefit. However, most CF patients carry the ΔF508 mutation, which impairs CFTR folding, processing, function, and stability. Studies in homozygous ΔF508 CF patients indicated little clinical benefit of monotherapy with the investigational corrector VX-809 (lumacaftor) or VX-770, whereas combination clinical trials show limited but significant improvements in lung function. We show that VX-770, as well as most other potentiators, reduces the correction efficacy of VX-809 and another investigational corrector, VX-661. To mimic the administration of VX-770 alone or in combination with VX-809, we examined its long-term effect in immortalized and primary human respiratory epithelia. VX-770 diminished the folding efficiency and the metabolic stability of ΔF508-CFTR at the endoplasmic reticulum (ER) and post-ER compartments, respectively, causing reduced cell surface ΔF508-CFTR density and function. VX-770-induced destabilization of ΔF508-CFTR was influenced by second-site suppressor mutations of the folding defect and was prevented by stabilization of the nucleotide-binding domain 1 (NBD1)-NBD2 interface. The reduced correction efficiency of ΔF508-CFTR, as well as of two other processing mutations in the presence of VX-770, suggests the need for further optimization of potentiators to maximize the clinical benefit of corrector-potentiator combination therapy in CF