Relativistic Hartree approach with exact treatment of vacuum polarization for finite nuclei

We study the relativistic Hartree approach with the exact treatment of the vacuum polarization in the Walecka sigma-omega model. The contribution from the vacuum polarization of nucleon-antinucleon field to the source term of the meson fields is evaluated by performing the energy integrals of the Dirac Green function along the imaginary axis. With the present method of the vacuum polarization in finite system, the total binding energies and charge radii of 16O and 40Ca can be reproduced. On the other hand, the level-splittings in the single-particle level, in particular the spin-orbit splittings, are not described nicely because the inclusion of vacuum effect provides a large effective mass with small meson fields. We also show that the derivative expansion of the effective action which has been used to calculate the vacuum contribution for finite nuclei gives a fairly good approximation.Comment: 15 pages, 8 figure

Report from the Expert Panel on the evaluation of the VRZs during the 2018/19 fishing season

In July 2018 ARK (the Association of Responsible Krill harvesting companies) launched a set of voluntary measures, known as ARK’s Commitment, which were proposed to improve the long-term sustainability of the krill fishery. The Commitment was initiated with support from Greenpeace, WWF and The Pew Charitable Trusts as a precautionary action whilst CCAMLR developed spatial management of the krill fishery in Area 48. The Commitment, which took the form of Voluntary Restriction Zones (VRZs), was implemented for the 2018-19 fishing season. The krill fishing fleet associated with ARK agreed to avoid fishing in an area of up to 40 km from penguin colonies in Subarea 48.1 during the penguin breeding season

2021 updated analysis of the sea ice concentration (SIC) in research blocks 4 (RB4), and 5 (RB5) of Subarea 48.6 with sea surface temperature (SST) and winds

Ice condition in the subarea 48.6, Southern OceanIn RB5, the SICs in Feb. 2021 were the highest and the SSTs were the lowest for the years 2016-2021. In March 2021, the highest SICs decreased to nearly the longterm average while the SST increased accordingly. In the same year, the SICs and SSTs had two peaks in Feb. and March respectively. In RB4, the SICs during Jan.- Feb (Austral summer) in 2021 were also the highest since 2016. The sharp spikes of SST (rapid increasing SST) had become smaller year by year from 2017 to 2021, which indicates that the SSTs had a cooling phase in 5-6 year periodical cycles corresponding to an increasing trend in SICs. Spatial dynamics of SICs with SSTs contour of -1.8°C and -0.8°C were analyzed. It was found that the ice edges are at approximately -1.8°C and partially broken ices exist between -1.8°C and -0.8°C when comparing imagery by GIBS and SICs distribution by AMSRs with SSTs by NOAA. Daily wind stick plots indicate that the eastward winds could encourage the off-shore Ekman transport at the end of Feb. and the beginning of Mar. which resulted in late (slow) ice retrieval in 2021

Epitope Density Influences CD8+ Memory T Cell Differentiation

The generation of long-lived memory T cells is critical for successful vaccination but the factors controlling their differentiation are still poorly defined. We tested the hypothesis that the strength of T cell receptor (TCR) signaling contributed to memory CD8(+) T cell generation.We manipulated the density of antigenic epitope presented by dendritic cells to mouse naïve CD8(+) T cells, without varying TCR affinity. Our results show that a two-fold decrease in antigen dose selectively affects memory CD8(+) T cell generation without influencing T cell expansion and acquisition of effector functions. Moreover, we show that low antigen dose alters the duration of the interaction between T cells and dendritic cells and finely tunes the expression level of the transcription factors Eomes and Bcl6. Furthermore, we demonstrate that priming with higher epitope density results in a 2-fold decrease in the expression of Neuron-derived orphan nuclear receptor 1 (Nor-1) and this correlates with a lower level of conversion of Bcl-2 into a pro-apoptotic molecule and an increased number of memory T cells.Our results show that the amount of antigen encountered by naïve CD8(+) T cells following immunization with dendritic cells does not influence the generation of functional effector CD8(+) T cells but rather the number of CD8(+) memory T cells that persist in the host. Our data support a model where antigenic epitope density sensed by CD8(+) T cells at priming influences memory generation by modulating Bcl6, Eomes and Nor-1 expression

Quantitative and Qualitative Urinary Cellular Patterns Correlate with Progression of Murine Glomerulonephritis

The kidney is a nonregenerative organ composed of numerous functional nephrons and collecting ducts (CDs). Glomerular and tubulointerstitial damages decrease the number of functional nephrons and cause anatomical and physiological alterations resulting in renal dysfunction. It has recently been reported that nephron constituent cells are dropped into the urine in several pathological conditions associated with renal functional deterioration. We investigated the quantitative and qualitative urinary cellular patterns in a murine glomerulonephritis model and elucidated the correlation between cellular patterns and renal pathology

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) grants nos. NNX12AP74G, NNX10AG01A, and NNX11AO08A. M. Altaf Arain thanks the support of Natural Sciences and Engineering Research Council (NSREC) of Canada. Penelope Serrano Ortiz was partially supported by the GEISpain project (CGL2014-52838-C2-1-R) funded by the Spanish Ministry of Economy and Competitiveness and the European Union ERDF funds. Sebastian Wolf acknowledges support from a Marie Curie International Outgoing Fellowship (European Commission, grant 300083). The FLUXCOM initiative is coordinated by Martin Jung, Max Planck Institute for Biogeochemistry (Jena, Germany). This work used eddy-covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FluxnetCanada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy-covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, the Max Planck Institute for Biogeochemistry, the National Science Foundation, the University of Tuscia and the US Department of Energy, and the databasing and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, the University of California - Berkeley, and the University of Virginia.Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2  0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.European Union (EU) GA 283080 283080 640176European Research Council (ERC) 647423Ministry of the Environment, Japan 2-1401JAXA Global Change Observation Mission (GCOM) project 115National Aeronautics & Space Administration (NASA) NNX12AP74G NNX10AG01A NNX11AO08ANatural Sciences and Engineering Research Council of CanadaGEISpain project - Spanish Ministry of Economy and Competitiveness CGL2014-52838-C2-1-REuropean Commission Joint Research Centre 300083United States Department of Energy (DOE) DE-FG02-04ER63917 DE-FG02-04ER63911FAO-GTOS-TCOiLEAPSMax Planck Institute for BiogeochemistryNational Science Foundation (NSF)University of Tusci

Regional carbon fluxes from land use and land cover change in Asia, 1980–2009

This is the final version of the article. Available from IOP Publishing via the DOI in this record.We present a synthesis of the land-atmosphere carbon flux from land use and land cover change (LULCC) in Asia using multiple data sources and paying particular attention to deforestation and forest regrowth fluxes. The data sources are quasi-independent and include the U.N. Food and Agriculture Organization-Forest Resource Assessment (FAO-FRA 2015; country-level inventory estimates), the Emission Database for Global Atmospheric Research (EDGARv4.3), the 'Houghton' bookkeeping model that incorporates FAO-FRA data, an ensemble of 8 state-of-the-art Dynamic Global Vegetation Models (DGVM), and 2 recently published independent studies using primarily remote sensing techniques. The estimates are aggregated spatially to Southeast, East, and South Asia and temporally for three decades, 1980–1989, 1990–1999 and 2000–2009. Since 1980, net carbon emissions from LULCC in Asia were responsible for 20%–40% of global LULCC emissions, with emissions from Southeast Asia alone accounting for 15%–25% of global LULCC emissions during the same period. In the 2000s and for all Asia, three estimates (FAO-FRA, DGVM, Houghton) were in agreement of a net source of carbon to the atmosphere, with mean estimates ranging between 0.24 to 0.41 Pg C yr−1, whereas EDGARv4.3 suggested a net carbon sink of −0.17 Pg C yr−1. Three of 4 estimates suggest that LULCC carbon emissions declined by at least 34% in the preceding decade (1990–2000). Spread in the estimates is due to the inclusion of different flux components and their treatments, showing the importance to include emissions from carbon rich peatlands and land management, such as shifting cultivation and wood harvesting, which appear to be consistently underreported.This work was supported by the Asia Pacific Network for Global Change Research (ARCP2013-01CMY-Patra/Canadell). LC was supported by the National Science Foundation East Asia Pacific Summer Institute (EAPSI) Fellowship. KI and PP were supported by the Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan. JGC thanks the support from the Australian Climate Change Science Program. AI and EK were supported by ERTDF (S-10) by the Ministry of the Environment, Japan. CK is supported by DOE-BER through BGC-Feedbacks SFA and NGEE-Tropics. AW was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101) and EU FP7 Funding through project LUC4C (603542)

Regional carbon fluxes from land use and land cover change in Asia, 1980-2009

Wepresent a synthesis of the land-atmosphere carbon flux from land use and land cover change (LULCC) in Asia usingmultiple data sources and paying particular attention to deforestation and forest regrowth fluxes. The data sources are quasi-independent and include the U.N. Food andAgriculture Organization-Forest Resource Assessment (FAO-FRA2015; country-level inventory estimates), the Emission Database forGlobalAtmospheric Research (EDGARv4.3), the ‘Houghton’ bookkeepingmodel that incorporates FAO-FRA data, an ensemble of 8 state-of-the-artDynamic Global Vegetation Models (DGVM), and2 recently published independent studies using primarily remote sensing techniques.The estimates are aggregated spatially to Southeast, East, and SouthAsia and temporally for three decades, 1980–1989, 1990–1999 and 2000–2009. Since 1980, net carbon emissions from LULCCin Asia were responsible for 20%–40%of global LULCCemissions, with emissions from Southeast Asia alone accounting for15%–25%of global LULCCemissions during the same period. In the 2000s and for allAsia, three estimates (FAO-FRA,DGVM,Houghton) were in agreement of a net source of carbon to the atmosphere,with meanestimates rangingbetween0.24 to0.41PgCyr$^{-1}$,whereasEDGARv4.3 suggested a net carbon sink of−0.17 Pg C yr$^{-1}$. Three of 4 estimates suggest that LULCCcarbon emissions declined by at least 34%in the preceding decade (1990–2000). Spread in the estimates is due to the inclusion of different flux components and their treatments, showing the importance to includeemissions fromcarbon rich peatlands and land management, such as shifting cultivation andwood harvesting, which appear to be consistently underreported