Carbon and sulfur isotopes in tree rings as a proxy for volcanic degassing

Geology ◽  
2019 ◽  
Vol 47 (9) ◽  
pp. 825-828 ◽  
Author(s):  
Fiona D’Arcy ◽  
Étienne Boucher ◽  
J. Maarten De Moor ◽  
Jean-François Hélie ◽  
Robert Piggott ◽  
...  
Keyword(s):  
Tree Rings ◽  
Large Scale ◽  
Sulfur Isotopes ◽  
Isotopic Analysis ◽  
Atmospheric Conditions ◽  
Gas Emission ◽  
Volcanic Degassing ◽  
Alnus Acuminata ◽  
Novel Method ◽  
Turrialba Volcano

Abstract Trees are useful archives of past atmospheric conditions. They have most commonly been used to infer large-scale changes in climate, industrial pollution, and the magnitude and frequency of geological hazards. While geochemical changes in tree rings have been linked to localized anthropogenic smelter pollution, their potential to track geochemical changes in volcanic degassing has not yet been fully realized. Here, we applied a new proxy using sulfur and carbon isotopes in tree rings to examine fluctuations in gas emission at Turrialba volcano, Costa Rica. Since 2009, Turrialba has emitted a persistent gas plume and increasingly frequent explosions and ash eruptions as activity has accelerated. We collected cores from a species of alder tree, Alnus acuminata, at several locations surrounding the volcano. Biannual isotopic analysis of rings demonstrated a notable δ34S shift of –5.2‰ and a similarly sharp δ13C shift of +1.3‰ in trees downwind of the plume following the onset of strong degassing in 2009. We propose that these shifts in the isotopic values of the tree correspond to those of the volcanic SO2 and CO2, and in the case of the δ13C, an additional fractionation caused by leaf impairment from exposure to volcanic SO2. This new proxy can be applied to other volcanoes as a novel method of obtaining a temporal record of degassing, a crucial tool for volcano monitoring.

Enzymatic Synthesis of 15N-L-aspartic Acid Using Recombinant Aspartase from Escherichia Coli K12

2008 ◽  
Vol 59 (11) ◽  
Author(s):  
Iulia Lupan ◽  
Sergiu Chira ◽  
Maria Chiriac ◽  
Nicolae Palibroda ◽  
Octavian Popescu
Keyword(s):  
Amino Acids ◽  
Aspartic Acid ◽  
Enzymatic Synthesis ◽  
Large Scale ◽  
Recombinant Dna ◽  
Industrial Enzymes ◽  
Recombinant Dna Technology ◽  
Bacterial Fermentation ◽  
Natural Protein ◽  
Novel Method

Amino acids are obtained by bacterial fermentation, extraction from natural protein or enzymatic synthesis from specific substrates. With the introduction of recombinant DNA technology, it has become possible to apply more rational approaches to enzymatic synthesis of amino acids. Aspartase (L-aspartate ammonia-lyase) catalyzes the reversible deamination of L-aspartic acid to yield fumaric acid and ammonia. It is one of the most important industrial enzymes used to produce L-aspartic acid on a large scale. Here we described a novel method for [15N] L-aspartic synthesis from fumarate and ammonia (15NH4Cl) using a recombinant aspartase.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

scholarly journals A Novel Method Facilitating the Simple and Low-Cost Preparation of Human Osteochondral Slice Explants for Large-Scale Native Tissue Analysis

2021 ◽  
Vol 22 (12) ◽  
pp. 6394
Author(s):  
Jacob Spinnen ◽  
Lennard K. Shopperly ◽  
Carsten Rendenbach ◽  
Anja A. Kühl ◽  
Ufuk Sentürk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Viability ◽  
Large Scale ◽  
Marker Gene ◽  
Cell Swelling ◽  
Tissue Cell ◽  
Joint Research ◽  
Sample Number ◽  
Tnf Α ◽  
Novel Method

For in vitro modeling of human joints, osteochondral explants represent an acceptable compromise between conventional cell culture and animal models. However, the scarcity of native human joint tissue poses a challenge for experiments requiring high numbers of samples and makes the method rather unsuitable for toxicity analyses and dosing studies. To scale their application, we developed a novel method that allows the preparation of up to 100 explant cultures from a single human sample with a simple setup. Explants were cultured for 21 days, stimulated with TNF-α or TGF-β3, and analyzed for cell viability, gene expression and histological changes. Tissue cell viability remained stable at >90% for three weeks. Proteoglycan levels and gene expression of COL2A1, ACAN and COMP were maintained for 14 days before decreasing. TNF-α and TGF-β3 caused dose-dependent changes in cartilage marker gene expression as early as 7 days. Histologically, cultures under TNF-α stimulation showed a 32% reduction in proteoglycans, detachment of collagen fibers and cell swelling after 7 days. In conclusion, thin osteochondral slice cultures behaved analogously to conventional punch explants despite cell stress exerted during fabrication. In pharmacological testing, both the shorter diffusion distance and the lack of need for serum in the culture suggest a positive effect on sensitivity. The ease of fabrication and the scalability of the sample number make this manufacturing method a promising platform for large-scale preclinical testing in joint research.

scholarly journals A Novel Method for the Preparation of Fibrous CeO2–ZrO2–Y2O3 Compacts for Thermochemical Cycles

Crystals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 885
Author(s):  
Nicole Knoblauch ◽  
Peter Mechnich
Keyword(s):  
Large Scale ◽  
Preparation Method ◽  
Cost Effective ◽  
High Porosity ◽  
Thermochemical Cycles ◽  
Redox Cycles ◽  
Redox Kinetics ◽  
Direct Infiltration ◽  
Novel Method ◽  
Functional Components

Zirconium-Yttrium-co-doped ceria (Ce0.85Zr0.13Y0.02O1.99) compacts consisting of fibers with diameters in the range of 8–10 µm have been successfully prepared by direct infiltration of commercial YSZ fibers with a cerium oxide matrix and subsequent sintering. The resulting chemically homogeneous fiber-compacts are sinter-resistant up to 1923 K and retain a high porosity of around 58 vol% and a permeability of 1.6–3.3 × 10−10 m² at a pressure gradient of 100–500 kPa. The fiber-compacts show a high potential for the application in thermochemical redox cycling due its fast redox kinetics. The first evaluation of redox kinetics shows that the relaxation time of oxidation is five times faster than that of dense samples of the same composition. The improved gas exchange due to the high porosity also allows higher reduction rates, which enable higher hydrogen yields in thermochemical water-splitting redox cycles. The presented cost-effective fiber-compact preparation method is considered very promising for manufacturing large-scale functional components for solar-thermal high-temperature reactors.

scholarly journals Using Sentinel-2 Images to Estimate Topography, Tidal-Stage Lags and Exposure Periods over Large Intertidal Areas

2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
José P. Granadeiro ◽  
João Belo ◽  
Mohamed Henriques ◽  
João Catalão ◽  
Teresa Catry
Keyword(s):  
Large Scale ◽  
Exposure Period ◽  
Intertidal Area ◽  
Digital Elevation ◽  
Geographical Bias ◽  
Novel Method ◽  
Tidal Stage ◽  
Sentinel 2

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

scholarly journals Atmospheric Conditions Associated with Labrador Sea Deep Convection: New Insights from a Case Study of the 2006/07 and 2007/08 Winters

2016 ◽  
Vol 29 (14) ◽  
pp. 5281-5297 ◽  
Author(s):  
Who M. Kim ◽  
Stephen Yeager ◽  
Ping Chang ◽  
Gokhan Danabasoglu
Keyword(s):  
North America ◽  
North Atlantic ◽  
Large Scale ◽  
Initial Conditions ◽  
Deep Convection ◽  
La Niña ◽  
Labrador Sea ◽  
Atmospheric Conditions ◽  
Circulation Anomaly ◽  
La Nina

Abstract Deep convection in the Labrador Sea (LS) resumed in the winter of 2007/08 under a moderately positive North Atlantic Oscillation (NAO) state. This is in sharp contrast with the previous winter with weak convection, despite a similar positive NAO state. This disparity is explored here by analyzing reanalysis data and forced-ocean simulations. It is found that the difference in deep convection is primarily due to differences in large-scale atmospheric conditions that are not accounted for by the conventional NAO definition. Specifically, the 2007/08 winter was characterized by an atmospheric circulation anomaly centered in the western North Atlantic, rather than the eastern North Atlantic that the conventional NAO emphasizes. This anomalous circulation was also accompanied by anomalously cold conditions over northern North America. The controlling influence of these atmospheric conditions on LS deep convection in the 2008 winter is confirmed by sensitivity experiments where surface forcing and/or initial conditions are modified. An extended analysis for the 1949–2009 period shows that about half of the winters with strong heat losses in the LS are associated with such a west-centered circulation anomaly and cold conditions over northern North America. These are found to be accompanied by La Niña–like conditions in the tropical Pacific, suggesting that the atmospheric response to La Niña may have a strong influence on LS deep convection.

scholarly journals Separation of Fe from Mn in the Cryogenian Sedimentary Mn Deposit, South China: Insights from Ore Mineral Chemistry and S Isotopes from the Dawu Deposit

Minerals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 446
Author(s):  
Zhiming Xu ◽  
Chengquan Wu ◽  
Zhengwei Zhang ◽  
Jinhong Xu ◽  
Xiyao Li ◽  
...  
Keyword(s):  
South China ◽  
Large Scale ◽  
Early Stage ◽  
Sulfur Isotopes ◽  
Mineral Chemistry ◽  
Manganese Carbonate ◽  
Separation Mechanism ◽  
Geochemical Properties ◽  
Stage Separation ◽  
Fe Content

Manganese and Fe have similar geochemical properties in the supergene environment. Separation of Mn and Fe is an important process for the formation of high-grade sedimentary manganese deposits. Large-scale manganese carbonate deposits (total reserves of approximately 700 Mt) were formed during the interglacial of the Sturtian and Marinoan in South China. The orebodies are hosted in the black rock series at the basal Datangpo Formation of the Cryogenian period. The Fe contents in ores range from 1.15 to 7.18 wt.%, with an average of 2.80 wt.%, and the average Mn/Fe ratio is 8.9, indicating a complete separation of Mn and Fe during the formation of manganese ores. Here, we present element data of manganese carbonates and sulfur isotopes of pyrite from the Dawu deposit, Guizhou, China, aiming to investigate the separation mechanism of Mn and Fe and the ore genesis. The Fe in ores mainly occurs as carbonate (FeCO3) and pyrite (FeS2). The Mn, Ca, Mg and Fe exist in the form of isomorphic substitutions in manganese carbonate. The contents of FeCO3 in manganese carbonates are similar in different deposits, with averages of 2.6–2.8 wt.%. The whole-rock Fe and S contents have an obvious positive correlation (R = 0.69), indicating that the difference of whole-rock Fe content mainly comes from the pyrite content. The δ34SV-CDT of pyrite varies from 40.0 to 48.3‰, indicating that the pyrite formed in a restricted basin where sulfate supply was insufficient and the sulfate concentrations were extremely low. Additionally, the whole-rock Fe content is negatively correlated with the δ34S values of the whole-rock and pyrite, with correlation coefficients of −0.78 and −0.83, respectively. Two stages of separations of Mn and Fe might have occurred during the mineralization processes. The reduced seawater became oxidized gradually after the Sturtian glaciation, and Fe2+ was oxidized and precipitated before Mn2+, which resulted in the first-stage separation of Mn and Fe. The residual Mn-rich and Fe-poor seawater flowed into the restricted rift basin. Mn and Fe were then precipitated in sediments as oxyhydroxide as the seawater was oxidized. At the early stage of diagenesis, organic matter was oxidized, and manganese oxyhydroxide was reduced, forming the manganese carbonate. H2S was insufficient in the restricted basin due to the extremely low sulfate concentration. The Fe2+ was re-released due to the lack of H2S, resulting in the second-stage separation of Mn and Fe. Finally, the manganese carbonate deposit with low Fe and very high δ34S was formed in the restricted basin after the Sturtian glaciation.

scholarly journals Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2564
Author(s):  
Mauro Martini ◽  
Vittorio Mazzia ◽  
Aleem Khaliq ◽  
Marcello Chiaberge
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Domain Adaptation ◽  
Learning Approaches ◽  
Atmospheric Conditions ◽  
Practical Applications ◽  
Area Of Interest ◽  
Multi Temporal ◽  
Significant Performance ◽  
Adversarial Training

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time-consuming solution that pose strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.

scholarly journals Cloud-to-ground lightning in Portugal: patterns and dynamical forcing

2012 ◽  
Vol 12 (3) ◽  
pp. 639-649 ◽  
Author(s):  
J. A. Santos ◽  
M. A. Reis ◽  
J. Sousa ◽  
S. M. Leite ◽  
S. Correia ◽  
...  
Keyword(s):  
Large Scale ◽  
Bimodal Distribution ◽  
Lightning Activity ◽  
Atmospheric Conditions ◽  
Remote Forcing ◽  
Cloud To Ground Lightning ◽  
Using Data ◽  
Number Of Discharges ◽  
Low Pressures ◽  
Large Scale Atmospheric Circulation

Abstract. An analysis of the cloud-to-ground discharges (CGD) over Portugal is carried out using data collected by a network of sensors maintained by the Portuguese Meteorological Institute for 2003–2009 (7 yr). Only cloud-to-ground flashes are considered and negative polarity CGD are largely dominant. The total number of discharges reveals a considerable interannual variability and a large irregularity in their distribution throughout the year. However, it is shown that a large number of discharges occur in the May–September period (71%), with a bimodal distribution that peaks in May and September, with most of the lightning activity recorded in the afternoon (from 16:00 to 18:00 UTC). In spring and autumn the lightning activity tends to be scattered throughout the country, whereas in summer it tends to be more concentrated over northeastern Portugal. Winter generally presents low lightning activity. Furthermore, two significant couplings between the monthly number of days with discharges and the large-scale atmospheric circulation are isolated: a regional forcing, predominantly in summer, and a remote forcing. In fact, the identification of daily lightning regimes revealed three important atmospheric conditions for triggering lightning activity: regional cut-off lows, cold troughs induced by remote low pressure systems and summertime regional low pressures at low-tropospheric levels combined with a mid-tropospheric cold trough.

scholarly journals Topology assisted self-organization of colloidal nanoparticles: application to 2D large-scale nanomastering

2014 ◽  
Vol 5 ◽  
pp. 1203-1209 ◽  
Author(s):  
Hind Kadiri ◽  
Serguei Kostcheev ◽  
Daniel Turover ◽  
Rafael Salas-Montiel ◽  
Komla Nomenyo ◽  
...  
Keyword(s):  
Large Scale ◽  
The Self ◽  
Surface Topology ◽  
Self Organization ◽  
Hydrogen Silsesquioxane ◽  
Polystyrene Beads ◽  
Structure Topology ◽  
Self Organized ◽  
Novel Method ◽  
Long Range Ordering

Our aim was to elaborate a novel method for fully controllable large-scale nanopatterning. We investigated the influence of the surface topology, i.e., a pre-pattern of hydrogen silsesquioxane (HSQ) posts, on the self-organization of polystyrene beads (PS) dispersed over a large surface. Depending on the post size and spacing, long-range ordering of self-organized polystyrene beads is observed wherein guide posts were used leading to single crystal structure. Topology assisted self-organization has proved to be one of the solutions to obtain large-scale ordering. Besides post size and spacing, the colloidal concentration and the nature of solvent were found to have a significant effect on the self-organization of the PS beads. Scanning electron microscope and associated Fourier transform analysis were used to characterize the morphology of the ordered surfaces. Finally, the production of silicon molds is demonstrated by using the beads as a template for dry etching.

Sign in / Sign up

Export Citation Format

Share Document