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Using (2712.4±14.3)×106 ψ(3686) events collected with the BESIII detector operating at the BEPCII collider, we search for the hadronic transition hc→π+π−J/ψ via ψ(3686)→π0hc. No significant signal is observed. We set the most stringent upper limits to date on the branching fractions B(ψ(3686)→π0hc)×B(hc→π+π−J/ψ) and B(hc→π+π−J/ψ) at the 90% confidence level, which are determined to be 6.7×10−7 and 9.4×10−4, respectively.
Extending the carotenoid pathway to astaxanthin in plants is of scientific and industrial interest. However, expression of a microbial beta-carotene ketolase (BKT) that catalyses the formation of ketocarotenoids in transgenic plants typically results in low levels of astaxanthin. The low efficiency of BKTs in ketolating zeaxanthin to astaxanthin is proposed to be the major limitation for astaxanthin accumulation in engineered plants. To verify this hypothesis, several algal BKTs were functionally characterized using an Escherichia coli system and three BKTs were identified, with high (up to 85%), moderate (~38%), and low (~1%) conversion rate from zeaxanthin to astaxanthin from Chlamydomonas reinhardtii (CrBKT), Chlorella zofingiensis (CzBKT), and Haematococcus pluvialis (HpBKT3), respectively. Transgenic Arabidopsis thaliana expressing the CrBKT developed orange leaves which accumulated astaxanthin up to 2 mg g -1 dry weight with a 1.8-fold increase in total carotenoids. In contrast, the expression of CzBKT resulted in much lower astaxanthin content (0.24 mg g -1 dry weight), whereas HpBKT3 was unable to mediate synthesis of astaxanthin in A. thaliana. The none-native astaxanthin was found mostly in a free form integrated into the light-harvesting complexes of photosystem II in young leaves but in esterified forms in senescent leaves. The alteration of carotenoids did not affect chlorophyll content, plant growth, or development significantly. The astaxanthin-producing plants were more tolerant to high light as shown by reduced lipid peroxidation. This study advances a decisive step towards the utilization of plants for the production of high-value astaxanthin. Keywords: Arabidopsis thaliana, astaxanthin, beta-carotene ketolase, carotenoid, Haematococcus pluvialis
We search for the di-photon decay of a light pseudoscalar axion-like particle, a, in radiative J/ψ decays, using 10 billion J/ψ events collected with the BESIII detector. We find no evidence of a signal and set upper limits at the 95% confidence level on the product branching fraction B(J/ψ→γa)×B(a→γγ) and the axion-like particle photon coupling constant gaγγ in the ranges of (3.7−48.5)×10−8 and (2.2−101.8)×10−4 GeV−1, respectively, for 0.18≤ma≤2.85 GeV/c2. These are the most stringent limits to date in this mass region.
Improved measurement of the branching fractions of the inclusive decays D⁺ → Kₛ⁰X and D⁰ → Kₛ⁰X
(2023)
By analyzing 2.93 fb−1 of 𝑒+𝑒− collision data taken at the center-of-mass energy of 3.773 GeV with the BESIII detector, the branching fractions of the inclusive decays 𝐷+→𝐾0 𝑆𝑋 and 𝐷0→𝐾0 𝑆𝑋 are measured to be (33.11±0.13±0.36)% and (20.75±0.12±0.20)%, respectively, where the first uncertainties are statistical and the second are systematic. These results are consistent with the world averages of previous measurements, but with much improved precision.
The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols
(2016)
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copy-right work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce, or limit each other.
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, either using well-founded empirical relationships or process-based models with good predictive skill. A large variety of models exist today and it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project - FireMIP, an international project to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we summarise the current state-of-the-art in fire regime modelling and model evaluation, and outline what lessons may be learned from FireMIP.
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model.
In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.