Routine Processing and Automatic Detection of Volcanic Ground Deformation Using Sentinel-1 InSAR Data: Insights from African Volcanoes

Since the launch of Sentinel-1 mission, automated processing systems have been developed for near real-time monitoring of ground deformation signals. Here, we perform a regional analysis of 5 years over 64 volcanic centres located along the East African Rift System (EARS). We show that the correction of atmospheric signals for the arid and low-elevation EARS volcanoes is less important than for other volcanic environments. We find that the amplitude of the cumulative displacements exceeds three times the temporal noise of the time series (3σ) for 16 of the 64 volcanoes, which includes previously reported deformation signals, and two new ones at Paka and Silali volcanoes. From a 5-year times series, uncertainties in rates of deformation are ∼0.1 cm/yr, whereas uncertainties associated with the choice of reference pixel are typically 0.3–0.6 cm/yr. We fit the time series using simple functional forms and classify seven of the volcano time series as ‘linear’, six as ‘sigmoidal’ and three as ‘hybrid’, enabling us to discriminate between steady deformation and short-term pulses of deformation. This study provides a framework for routine volcano monitoring using InSAR on a continental scale. Here, we focus on Sentinel-1 data from the EARS, but the framework could be expanded to include other satellite systems or global coverage

Large-scale demonstration of machine learning for the detection of volcanic deformation in Sentinel-1 satellite imagery

Radar (SAR) satellites systematically acquire imagery that can be used for volcano monitoring, characterising magmatic systems and potentially forecasting eruptions on a global scale. However, exploiting the large dataset is limited by the need for manual inspection, meaning timely dissemination of information is challenging. Here we automatically process ~ 600,000 images of > 1000 volcanoes acquired by the Sentinel-1 satellite in a 5-year period (2015–2020) and use the dataset to demonstrate the applicability and limitations of machine learning for detecting deformation signals. Of the 16 volcanoes flagged most often, 5 experienced eruptions, 6 showed slow deformation, 2 had non-volcanic deformation and 3 had atmospheric artefacts. The detection threshold for the whole dataset is 5.9 cm, equivalent to a rate of 1.2 cm/year over the 5-year study period. We then use the large testing dataset to explore the effects of atmospheric conditions, land cover and signal characteristics on detectability and find that the performance of the machine learning algorithm is primarily limited by the quality of the available data, with poor coherence and slow signals being particularly challenging. The expanding dataset of systematically acquired, processed and flagged images will enable the quantitative analysis of volcanic monitoring signals on an unprecedented scale, but tailored processing will be needed for routine monitoring applications

Co-eruptive, endogenous edifice growth, uplift during 4 years of eruption at Sangay Volcano, Ecuador

We report sustained uplift throughout Volcan Sangay's most recent period of eruption (2019–22), moderated only by transient excursions during some of its largest explosions. Volcan Sangay (Amazonia, Ecuador), has been erupting since 2019, impacting both local communities and distant cities with ash fall and lahars. We analyzed ascending and descending Sentinel-1 radar imagery, constructing a robust network of interferograms spanning this eruptive period to measure relative ground displacements across the volcano. Our time series reveals a consistent uplift pattern (∼68 mm/yr) on the western and northern flanks of the volcano, which we attribute to volume increases in a body of magma located within the volcano's edifice beneath its western flank. This source appears to be vertically extensive, and is best fit by a quadrangular magma pathway, dipping towards the west and increasing in volume by 1.1 × 10⁶ m³ between 2019 and 2022. We additionally identify non-magmatic deformation, including subsidence of fresh deposits and downslope displacement (∼50 mm/year) in the southeastern sector of the volcano. Co-eruptive uplift at Sangay is a rare observation of endogenous growth during an eruption and indicates that stratovolcano edifice stability is sensitive to both magma flux into the edifice and shallow controls on eruption rate

Strategies for improving the communication of satellite-derived InSAR data for geohazards through the analysis of Twitter and online data portals

Satellite-based earth observation sensors are increasingly able to monitor geophysical signals related to natural hazards, and many groups are working on rapid data acquisition, processing, and dissemination to data users with a wide range of expertise and goals. A particular challenge in the meaningful dissemination of Interferometric Synthetic Aperture Radar (InSAR) data to non-expert users is its unique differential data structure and sometimes low signal-to-noise ratio. In this study, we evaluate the online dissemination of ground deformation measurements from InSAR through Twitter, alongside the provision of open-access InSAR data from the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) processing system. Our aim is to evaluate (1) who interacts with disseminated InSAR data, (2) how the data are used, and (3) to discuss strategies for meaningful communication and dissemination of open InSAR data. We found that the InSAR Twitter community was primarily composed of non-scientists (62 %), although this grouping included earth observation experts in applications such as commercial industries. Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where users disseminated InSAR measurements of ground deformation, often using wrapped and unwrapped interferograms. For earthquake events, Sentinel-1 data were acquired, processed, and tweeted within 4.7±2.8 d (the shortest was 1 d). Open-access Sentinel-1 data dominated the InSAR tweets and were applied to volcanic and earthquake events in the most engaged-with (retweeted) content. Open-access InSAR data provided by LiCSAR were widely accessed, including automatically processed and tweeted interferograms and interactive event pages revealing ground deformation following earthquake events. The further work required to integrate dissemination of InSAR data into longer-term disaster risk-reduction strategies is highly specific, to both hazard type and international community of practice, as well as to local political setting and civil protection mandates. Notably, communication of uncertainties and processing methodologies are still lacking. We conclude by outlining the future direction of COMET LiCSAR products to maximize their useability.</p

BALL - biochemical algorithms library 1.3

<p>Abstract</p> <p>Background</p> <p>The Biochemical Algorithms Library (BALL) is a comprehensive rapid application development framework for structural bioinformatics. It provides an extensive C++ class library of data structures and algorithms for molecular modeling and structural bioinformatics. Using BALL as a programming toolbox does not only allow to greatly reduce application development times but also helps in ensuring stability and correctness by avoiding the error-prone reimplementation of complex algorithms and replacing them with calls into the library that has been well-tested by a large number of developers. In the ten years since its original publication, BALL has seen a substantial increase in functionality and numerous other improvements.</p> <p>Results</p> <p>Here, we discuss BALL's current functionality and highlight the key additions and improvements: support for additional file formats, molecular edit-functionality, new molecular mechanics force fields, novel energy minimization techniques, docking algorithms, and support for cheminformatics.</p> <p>Conclusions</p> <p>BALL is available for all major operating systems, including Linux, Windows, and MacOS X. It is available free of charge under the Lesser GNU Public License (LPGL). Parts of the code are distributed under the GNU Public License (GPL). BALL is available as source code and binary packages from the project web site at <url>http://www.ball-project.org</url>. Recently, it has been accepted into the debian project; integration into further distributions is currently pursued.</p

Milk and dairy products: good or bad for human health? An assessment of the totality of scientific evidence

Background: There is scepticism about health effects of dairy products in the public, which is reflected in an increasing intake of plant-based drinks, for example, from soy, rice, almond, or oat. Objective: This review aimed to assess the scientific evidence mainly from meta-analyses of observational studies and randomised controlled trials, on dairy intake and risk of obesity, type 2 diabetes, cardiovascular disease, osteoporosis, cancer, and all-cause mortality. Results: The most recent evidence suggested that intake of milk and dairy products was associated with reduced risk of childhood obesity. In adults, intake of dairy products was shown to improve body composition and facilitate weight loss during energy restriction. In addition, intake of milk and dairy products was associated with a neutral or reduced risk of type 2 diabetes and a reduced risk of cardiovascular disease, particularly stroke. Furthermore, the evidence suggested a beneficial effect of milk and dairy intake on bone mineral density but no association with risk of bone fracture. Among cancers, milk and dairy intake was inversely associated with colorectal cancer, bladder cancer, gastric cancer, and breast cancer, and not associated with risk of pancreatic cancer, ovarian cancer, or lung cancer,while the evidence for prostate cancer risk was inconsistent.Finally,consumption of milk and dairy products was not associated with all-cause mortality. Calcium-fortified plant-based drinks have been included as an alternative to dairy products in the nutrition recommendations in several countries. However, nutritionally, cow’s milk and plant-based drinks are completely different foods,and an evidence-based conclusion on the health value of the plant-based drinks requires more studies in humans. Conclusion: The totality of available scientific evidence supports that intake of milk and dairy products contribute to meet nutrient recommendations, and may protect against the most prevalent chronic diseases, whereas very few adverse effects have been reported

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Upstream Supply Chain Visibility and Complexity Effect on Focal Company’s Sustainable Performance: Indian Manufacturers’ Perspective

Understanding supply chain sustainability performance is increasingly important for supply chain researchers and managers. Literature has considered supply chain sustainability and the antecedents of performance from a triple bottom line (economic, social, and environmental) perspective. However, the role of supply chain visibility and product complexity contingency in achieving sustainable supply chain performance has not been explored in depth. To address this gap, this study utilizes a contingent resource-based view theory perspective to understand the role of product complexity in shaping the relationship between upstream supply chain visibility (resources and capabilities) and the social, environmental, and economic performance dimensions. We develop and test a theoretical model using survey data gathered from 312 Indian manufacturing organizations. Our findings indicate that supply chain visibility (SCV) has significant influence on social and environmental performance under the moderation effect of product complexity. Hence, the study makes significant contribution to the extant literature by examining the impact of SCV under moderating effect of product complexity on social performance and environmental performance

Evaluation of the effect of polyphenol of escin compared with ibuprofen and dexamethasone in synoviocyte model for osteoarthritis: an in vitro study

Osteoarthritis (OA) is a chronic degenerative joint disease with inflammatory component. It is associated with progressive histological alterations and disabling symptoms. Today, drugs such as glucocorticoids (GCs) and nonsteroidal anti-inflammatory drugs (NSIADs) are commonly employed for treatment of osteoarthritis, but have serious and life-threatening side effects. The aim of the current study is to evaluate the effects of escin on cyclooxygenase-2 (COX-2, isoform), inducible nitric oxide synthase (iNOS), interleukin-1β (IL-1β), interleukin-18 (IL-18), tumor necrosis factor-alpha (TNF-α), and nitric oxide (NO) (1), as well as prostaglandin E2 (PGE2) on inflammatory cells, similar osteoarthritis in synoviocytes, and monocytes/macrophages, and to compare it with dexamethasone (DEX) and ibuprofen (IBP). Synovial cells were isolated from synovial membrane of the radiocarpal joint cartilage of an 8-month-old Holstein cow. THP-1 cells were prepared from Pasteur Institute of Iran. Cells were cultivated and exposed to lipopolysaccharide (LPS) stimulation without, or in the presence of, DEX, IBP, or escin. The gene expressions of IL-1β, TNF-α, IL-18, COX-2, and iNOS were evaluated by real-time PCR. Concentrations of NO and PGE2 were measured by ELISA methods. Our cells secreted an increased amounts of IL-1β, TNF-α, IL-18, COX-2, iNOS, NO, and PGE2 in response to LPS stimulation in all conditions. Escin can quench the gene expression of COX-2, iNOS, IL-1β, IL-18, and TNF-α in synoviocyte cells and production of NO and PGE2 in monocyte/macrophage cells alike DEX and IBP. We can use from escin for the treatment of osteoarthritis as an anti-inflammatory agent in the latter but further studies to support the results from such a model are needed. © 2018, International League of Associations for Rheumatology (ILAR)