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In this letter, we introduce The Polar Vocabularies and Semantics Working Group, originally established as a joint effort between the joint SAON/IASC Arctic Data Committee and the Data Management Collaboration Team of the Interagency Arctic Research Policy Committee. We Invite, communities of practice to actively engage with us in our activities (described below), to advance the state of semantics-based applications in polar activities (and to increase interoperability between stakeholders and rights holders within existing and emerging digital ecosystems).

Contains all code and data for the GMD manuscript "Introducing ELSA v2.0: an isochronal model for ice-sheet layer tracing" ELSA: version 2.0 (https://git.app.uib.no/melt-team-bergen/elsa, tag v2.0) Yelmo: version 1.801 (https://github.com/palma-ice/yelmo, tag v1.801) with a minor alteration to the basal dragging scheme Yelmox: version 1.801 (https://github.com/palma-ice/yelmox, tag v1.801) Operation IceBridge data, processed and interpolated to a 16km x 16km grid (see https://icebridge.gsfc.nasa.gov/?page_id=732)

Abstract P2.11 | 2nd ISMRM Iberian Chapter Annual Meeting | 27-28 June 2022, Lisbon, Portugal

Abstract P1.09 | 2nd ISMRM Iberian Chapter Annual Meeting | 27-28 June 2022, Lisbon, Portugal

First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming essential to detect blow flow anomalies. However, the need for real-time acquisitions limits the achievable spatial resolution and coverage of the heart. To keep both within a reasonable range, FPP-CMR needs to be accelerated. A SElf-Supervised aCcelerated REconsTruction (SECRET) DL framework is presented to speed-up reconstruction of FPP-CMR images from undersampled (k,t)-space data. The physical reconstruction models are used to train deep neural networks without requiring fully sampled images. SECRET achieves good quality reconstructions at a variety of acceleration rates, with significant speed-ups compared to the state-of-the-art.

Explicação sobre o problema do Lixo Marinho, seus impactos e possíveis soluções integradas numa abordagem de gestão de resíduos (em inglês). info:eu-repo/semantics/publishedVersion

Optical projection tomography is the optical analogue of X-ray tomography, a 3D mesoscopic imaging modality of the optical attenuation or fluorescence intensity distribution within transparent samples. As current limitations, the requirement of several hundreds of angular projections for filtered backprojection reconstruction leads to acquisition times of several minutes. While iterative methods allow to reconstruct images from sparsely sampled datasets, they convey several seconds, thus precluding from real-time reconstruction. Here we present a model-based unrolled network with a CNN as a regularization prior, trained on four zebrafish datasets. Based on an alternating algorithm, our network rapidly reconstructs high quality images from few projections within milliseconds. Youtube: https://youtu.be/4sNyq7zFEC8 This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 867450. (OPTIMAR: OPTical Imaging of Molecular and signalling Activity in Real-time: application to flatfish metamorphosis, https://cordis.europa.eu/project/id/867450). The project was also supported by NVIDIA GPU hardware grant and FCT - Foundation for Science and Technology through projects UIDB/04326/2020, UIDP/04326/2020 and LA/P/0101/2020.

Gordon & Betty Moore FoundationGordon and Betty Moore Foundation [5596]; Canada Research Chairs FoundationCanada Research Chairs; European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant [747946]; Fundacao para a Ciencia e Tecnologia I.P. Portugal (FCT); Direcao-Geral de Politica do Mar (DGPM) [2/2017/001-MiningImpact 2]; FCTPortuguese Foundation for Science and TechnologyEuropean Commission [CEECIND005262017, UID/MAR/00350/2013, IF/01194/2013, IF/00029/2014/CP1230/CT0002, Mining2/0005/2017]; RF State Assignment [0149-2019-0009]; Horizon 2020 Agricultural Interoperability and Analysis System (ATLAS) projects [678760]; JM Kaplan Fund; National Science FoundationNational Science Foundation (NSF) [OCE 1634172]; JPI Oceans project Mining Impact -Environmental Impacts and Risks of Deep-Sea Mining Aug 2018-Feb 2022 (NWO-ALW) [856.18.001] info:eu-repo/semantics/publishedVersion

This study presents simulations of Greenland surface melt for the Eemian interglacial period (∼130 000 to 115 000 years ago) derived from regional climate simulations with a coupled surface energy balance model. Surface melt is of high relevance due to its potential effect on ice core observations, e.g., lowering the preserved total air content (TAC) used to infer past surface elevation. An investigation of surface melt is particularly interesting for warm periods with high surface melt, such as the Eemian interglacial period. Furthermore, Eemian ice is the deepest and most compressed ice preserved on Greenland, resulting in our inability to identify melt layers visually. Therefore, simulating Eemian melt rates and associated melt layers is beneficial to improve the reconstruction of past surface elevation. Estimated TAC, based on simulated melt during the Eemian, could explain the lower TAC observations. The simulations show Eemian surface melt at all deep Greenland ice core locations and an average of up to ∼30 melt days per year at Dye-3, corresponding to more than 600 mm water equivalent (w.e.) of annual melt. For higher ice sheet locations, between 60 and 150 mmw.e.yr-1 on average are simulated. At the summit of Greenland, this yields a refreezing ratio of more than 25 % of the annual accumulation. As a consequence, high melt rates during warm periods should be considered when interpreting Greenland TAC fluctuations as surface elevation changes. In addition to estimating the influence of melt on past TAC in ice cores, the simulated surface melt could potentially be used to identify coring locations where Greenland ice is best preserved.

Industrial-scale mining of deep-sea polymetallic nodules will remove nodules in large areas of the sea floor. The regrowth of the nodules by metal precipitation is estimated to take millions of years. Thus, for future mining impact studies, it is crucial to understand the role of nodules in shaping microbial diversity and function in deep-sea environments. Here we investigated microbial-community composition based on 16S rRNA gene sequences retrieved from sediments and nodules of the Peru Basin (4130–4198 m water depth). The nodule field of the Peru Basin showed a typical deep-sea microbiome, with dominance of the classes Gammaproteobacteria, Alphaproteobacteria, Deltaproteobacteria, and Acidimicrobiia. Nodules and sediments host distinct bacterial and archaeal communities, with nodules showing lower diversity and a higher proportion of sequences related to potential metal-cycling Bacteria (i.e. Magnetospiraceae, Hyphomicrobiaceae), bacterial and archaeal nitrifiers (i.e. AqS1, unclassified Nitrosomonadaceae, Nitrosopumilus, Nitrospina, Nitrospira), and bacterial sequences found in the oceanic crust, nodules, hydrothermal deposits, and sessile fauna. Sediment and nodule communities overall shared a low proportion of operational taxonomic units (OTUs; 21 % for Bacteria and 19 % for Archaea). Our results show that nodules represent a specific ecological niche (i.e. hard substrate, high metal concentrations, and sessile fauna), with a potentially relevant role in organic-carbon degradation. Differences in nodule community composition (e.g. Mn-cycling bacteria, nitrifiers) between the Clarion–Clipperton Fracture Zone (CCZ) and the Peru Basin suggest that changes in environmental setting (e.g. sedimentation rates) also play a significant role in structuring the nodule microbiome.