Optimierung der kalten Lagerung von Blutgefäßen

Dem Einsatz und der Lagerung von Gefäßtransplantaten kommt in der Klinik eine wachsende Bedeutung zu. Die Gefäße werden dabei in den momentan klinisch gebräuchlichen Konservierungslösungen gelagert, wobei es zu einer Schädigung der funktionell bedeutsamen Endothelzellen kommt. Die Sequenz Kälte/Wiedererwärmung führt in kultivierten Endothelzellen über einen Anstieg des chelatisierbaren Eisenpools mit konsekutiver Freisetzung reaktiver Sauerstoffspezies zu einer kälteinduzierten Schädigung. Ob diese Schädigung auch am Endothel im Gefäßmodell auftritt und wie die kalte Lagerung von Blutgefäßen optimiert werden kann, war Gegenstand dieser Arbeit. Analog zur Schädigung isolierter Endothelzellen, war auch die Schädigung von Endothelzellen in Schweineaortensegmenten in allen Lösungen (partiell) eisenabhängig (14 Tage 4°C, 3 Stunden 37°C: Histidin-Tryptophan-Ketoglutarat (HTK)-Lösung 66 ± 7%, HTK + 1 mM Desferal 40 ± 10% Propidiumjodid-positive Endothelzellen) Anschließend wurde eine aminosäurehaltige, N-Acetylhistidin-gepufferte Basislösung optimiert. Diese optimierte Lösung mit pH 7,0 und Chlorid sowie Kalium als Hauptionen führte zu einer deutlichen Verbesserung des Endothelzellüberlebens. Die Kombination von Desferal und dem neuen lipophilen Eisenchelator LK 614 führte zu einer weiteren Verbesserung, so dass nach 21 Tagen Kaltlagerung und 3 Stunden Wiedererwärmung nur noch 10 ± 1% der Endothelzellen Propidiumjodid-positiv waren. In dieser optimierten Lösung waren sowohl das Endothelzellüberleben, als auch das mitochondriale Membranpotential signifikant besser erhalten als in den klinisch eingesetzten Lösungen HTK, University of Wisconsin, Perfadex und physiologischer Kochsalzlösung. Die Thrombozytenadhäsion war ebenfalls signifikant vermindert verglichen mit in HTK-Lösung gelagerten Aortensegmenten. Die kälteinduzierte Schädigung am Endothel von Schweineaorten ist, wie die Schädigung kultivierter Endothelzellen, z.T. über chelatisierbares Eisen vermittelt. Die hier entwickelte Gefäßprotektionslösung (optimierte Basislösung mit Eisenchelatoren in optimierter Konzentration) verbessert das Endothelzell-überleben und die Endothelzellfunktion bei/nach Kaltlagerung von Gefäßen erheblich und soll nun für einen klinischen Einsatz weiter evaluiert werden

Analog information decoding of bosonic quantum LDPC codes

Quantum error correction is crucial for scalable quantum information processing applications. Traditional discrete-variable quantum codes that use multiple two-level systems to encode logical information can be hardware-intensive. An alternative approach is provided by bosonic codes, which use the infinite-dimensional Hilbert space of harmonic oscillators to encode quantum information. Two promising features of bosonic codes are that syndrome measurements are natively analog and that they can be concatenated with discrete-variable codes. In this work, we propose novel decoding methods that explicitly exploit the analog syndrome information obtained from the bosonic qubit readout in a concatenated architecture. Our methods are versatile and can be generally applied to any bosonic code concatenated with a quantum low-density parity-check (QLDPC) code. Furthermore, we introduce the concept of quasi-single-shot protocols as a novel approach that significantly reduces the number of repeated syndrome measurements required when decoding under phenomenological noise. To realize the protocol, we present a first implementation of time-domain decoding with the overlapping window method for general QLDPC codes, and a novel analog single-shot decoding method. Our results lay the foundation for general decoding algorithms using analog information and demonstrate promising results in the direction of fault-tolerant quantum computation with concatenated bosonic-QLDPC codes.Comment: 30 pages, 15 figure

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0):Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).</p

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%-31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency similar to 0.52-0.63 and 0.53). UpCH(4) estimated annual global wetland CH4 emissions of 146 +/- 43 TgCH4 y(-1) for 2001-2018 which agrees closely with current bottom-up land surface models (102-181 TgCH4 y(-1)) and overlaps with top-down atmospheric inversion models (155-200 TgCH4 y -1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25 degrees from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ ORNLDAAC/2253).Plain Language Summary Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (similar to 30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001-2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state-of-the-art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground-based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

Paper Trails: Following the Money

In many recent elections, the candidates who raise the most money have a better shot at winning, so candidates must raise millions of dollars to win an election. A top question to consider in all elections: Where is the money coming from? Posting about the financing behind federal elections from In All Things - an online hub committed to the claim that the life, death, and resurrection of Jesus Christ has implications for the entire world. http://inallthings.org/paper-trails-following-the-money