Halloysite nanotubes (HNTs) have been proposed as a potential support to immobilize enzymes. Improving enzyme loading on HNTs is critical to their practical applications. Herein, we reported a simple method on the preparation of high-enzyme-loading support by modification with dopamine on the surface of HNTs. The modified HNTs were characterized by transmission electron microscopy, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy analyses. The results showed that dopamine could self-polymerize to adhere to the surface of HNTs and form a thin active coating. While the prepared hybrid nanotubes were used to immobilize enzyme of laccase, they exhibited high loading ability of 168.8 mg/g support, which was greatly higher than that on the pristine HNTs (11.6 mg/g support). The immobilized laccase could retain more than 90% initial activity after 30 days of storage and the free laccase only 32%. The immobilized laccase could also maintain more than 90% initial activity after five repeated uses. In addition, the immobilized laccase exhibited a rapid degradation rate and high degradation efficiency for removal of phenol compounds. These advantages indicated that the new hybrid material can be used as a low-cost and effective support to immobilize enzymes.

Abstract. The main advancements of the Beijing Climate Center (BCC) climate system model from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to phase 6 (CMIP6) are presented, in terms of physical parameterizations and model performance. BCC-CSM1.1 and BCC-CSM1.1m are the two models involved in CMIP5, whereas BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models configured for CMIP6. Historical simulations from 1851 to 2014 from BCC-CSM2-MR (CMIP6) and from 1851 to 2005 from BCC-CSM1.1m (CMIP5) are used for models assessment. The evaluation matrices include the following: (a) the energy budget at top-of-atmosphere; (b) surface air temperature, precipitation, and atmospheric circulation for the global and East Asia regions; (c) the sea surface temperature (SST) in the tropical Pacific; (d) sea-ice extent and thickness and Atlantic Meridional Overturning Circulation (AMOC); and (e) climate variations at different timescales, such as the global warming trend in the 20th century, the stratospheric quasi-biennial oscillation (QBO), the Madden–Julian Oscillation (MJO), and the diurnal cycle of precipitation. Compared with BCC-CSM1.1m, BCC-CSM2-MR shows significant improvements in many aspects including the tropospheric air temperature and circulation at global and regional scales in East Asia and climate variability at different timescales, such as the QBO, the MJO, the diurnal cycle of precipitation, interannual variations of SST in the equatorial Pacific, and the long-term trend of surface air temperature.

International audienceThis paper reviews recent progress in the development of the Beijing Climate Center Climate System Model (BCC_CSM) and its four component models (atmosphere, land surface, ocean, and sea ice). Two recent versions are described: BCC_CSM1.1 with coarse resolution (approximately 2.8125°×2.8125°) and BCC_CSM1.1(m) with moderate resolution (approximately 1.125°×1.125°). Both versions are fully coupled climate-carbon cycle models that simulate the global terrestrial and oceanic carbon cycles and include dynamic vegetation. Both models well simulate the concentration and temporal evolution of atmospheric CO2 during the 20th century with anthropogenic CO2 emissions prescribed. Simulations using these two versions of the BCC_CSM model have been contributed to the Coupled Model Intercomparison Project phase five (CMIP5) in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). These simulations are available for use by both national and international communities for investigating global climate change and for future climate projections.Simulations of the 20th century climate using BCC_CSM1.1 and BCC_CSM1.1(m) are presented and validated, with particular focus on the spatial pattern and seasonal evolution of precipitation and surface air temperature on global and continental scales. Simulations of climate during the last millennium and projections of climate change during the next century are also presented and discussed. Both BCC_CSM1.1 and BCC_CSM1.1(m) perform well when compared with other CMIP5 models. Preliminary analyses indicate that the higher resolution in BCC_CSM1.1(m) improves the simulation of mean climate relative to BCC_CSM1.1, particularly on regional scales

[1] The paper examines terrestrial and oceanic carbon budgets from preindustrial time to present day in the version of Beijing Climate Center Climate System Model (BCC_CSM1.1) which is a global fully coupled climate-carbon cycle model. Atmospheric CO 2 concentration is calculated from a prognostic equation taking into account global anthropogenic CO 2 emissions and the interactive CO 2 exchanges of land-atmosphere and ocean-atmosphere. When forced by prescribed historical emissions of CO 2 from combustion of fossil fuels and land use change, BCC_CSM1.1 can reproduce the trends of observed atmospheric CO 2 concentration and global surface air temperature from 1850 to 2005. Simulated interannual variability and long-term trend of global carbon sources and sinks and their spatial patterns generally agree with other model estimates and observations, which shows the following: (1) Both land and ocean in the last century act as net carbon sinks. The ability of carbon uptake by land and ocean is enhanced at the end of last century. (2) Interannual variability of the global atmospheric CO 2 concentration is closely correlated with the El Niño-Southern Oscillation cycle, in agreement with observations. (3) Interannual variation of the land-to-atmosphere net carbon flux is positively correlated with surface air temperature while negatively correlated with soil moisture over low and midlatitudes. The relative contribution of soil moisture to the interannual variation of land-atmosphere CO 2 exchange is more important than that of air temperature over tropical regions, while surface air temperature is more important than soil moisture over other regions of the globe.Citation: Wu, T., et al. (2013), Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century,

Background: Hypoxia is a key feature of breast cancer, which affects cancer development, metastasis and metabolism. Previous studies suggested that circular RNAs (circRNAs) could participate in cancer progression and hypoxia regulation. This study aimed to investigate the role of circRNA differentially expressed in normal cells and neoplasia domain containing 4C (circDENND4C) in breast cancer progression under hypoxia. Methods: Forty-three patients with breast cancer were involved in this study. Breast cancer cell lines MDA-MB-453 and SK-BR-3 were cultured under hypoxia (1% O 2) for experiments in vitro. The expression levels of circDENND4C, microRNA-200b (miR-200b) and miR-200c were measured by quantitative real-time polymerase chain reaction. Glycolysis was investigated by glucose consumption, lactate production and hexokinase II (HK2) protein level. Migration and invasion were evaluated via trans-well assay and protein levels of matrix metallopeptidase 9 (MMP9) and MMP2. The interaction between circDENND4C and miR-200b or miR-200c was explored by bioinformatics analysis, luciferase assay and RNA immunoprecipitation. Murine xenograft model was established to investigate the anti-cancer role of circDENND4C in vivo. Results: circDENND4C highly expressed in breast cancer was up-regulated in response to hypoxia. Knockdown of circDENND4C decreased glycolysis, migration and invasion in breast cancer cells under hypoxia. circDENND4C was validated as a sponge of miR-200b and miR-200c. Deficiency of miR-200b or miR-200c reversed the suppressive effect of circDENND4C knockdown on breast cancer progression. Moreover, silence of circDENND4C reduced xenograft tumor growth by increasing miR-200b and miR-200c. Conclusion: circDENND4C silence suppresses glycolysis, migration and invasion in breast cancer cells under hypoxia by increasing miR-200b and miR-200c.

Environmental DNA (eDNA) can be used to identify macroorganisms and describe biodiversity, and thus has promise to supplement biological monitoring in marine ecosystems. Despite this promise, scaling sample acquisition to the spatial and temporal scales needed for effective monitoring would require prohibitively large investments in time and human resources. To address this challenge, we evaluated the efficacy of an autonomous eDNA sampling system and compare results obtained to traditional eDNA sampling methods. The autonomous sampling instrument consisted of the Environmental Sample Processor (ESP) coupled to an autonomous underwater vehicle (AUV). We tested equivalency between the ESP and traditional eDNA sampling techniques by comparing the quantification of eDNA across a broad range of taxa, from microbes (SAR11), phytoplankton (Pseudo-nitzschia spp.), and invertebrates (krill: Euphausia pacifica) to vertebrates (anchovy: Engraulis mordax). No significant differences in eDNA densities were observed between the two sample collection and filtration methods. eDNA filters collected by the ESP were preserved and stable for 21 days, the typical deployment length of the instrumentation. Finally, we demonstrated the unique capabilities of an autonomous, mobile ESP during a deployment near Monterey Bay, CA, by remotely and repeatedly sampling a water mass over 12 h. The development of a mobile ESP demonstrates the promise of utilizing eDNA measurements to observe complex biological processes in the ocean absent a human presence.

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Abstract. Changes in forest cover have a strong effect on climate through the alteration of surface biogeophysical and biogeochemical properties that affect energy, water and carbon exchange with the atmosphere. To quantify biogeophysical and biogeochemical effects of deforestation in a consistent setup, nine Earth system models (ESMs) carried out an idealized experiment in the framework of the Coupled Model Intercomparison Project, phase 6 (CMIP6). Starting from their pre-industrial state, models linearly replace 20×106 km2 of forest area in densely forested regions with grasslands over a period of 50 years followed by a stabilization period of 30 years. Most of the deforested area is in the tropics, with a secondary peak in the boreal region. The effect on global annual near-surface temperature ranges from no significant change to a cooling by 0.55 ∘C, with a multi-model mean of -0.22±0.21 ∘C. Five models simulate a temperature increase over deforested land in the tropics and a cooling over deforested boreal land. In these models, the latitude at which the temperature response changes sign ranges from 11 to 43∘ N, with a multi-model mean of 23∘ N. A multi-ensemble analysis reveals that the detection of near-surface temperature changes even under such a strong deforestation scenario may take decades and thus longer than current policy horizons. The observed changes emerge first in the centre of deforestation in tropical regions and propagate edges, indicating the influence of non-local effects. The biogeochemical effect of deforestation are land carbon losses of 259±80 PgC that emerge already within the first decade. Based on the transient climate response to cumulative emissions (TCRE) this would yield a warming by 0.46 ± 0.22 ∘C, suggesting a net warming effect of deforestation. Lastly, this study introduces the “forest sensitivity” (as a measure of climate or carbon change per fraction or area of deforestation), which has the potential to provide lookup tables for deforestation–climate emulators in the absence of strong non-local climate feedbacks. While there is general agreement across models in their response to deforestation in terms of change in global temperatures and land carbon pools, the underlying changes in energy and carbon fluxes diverge substantially across models and geographical regions. Future analyses of the global deforestation experiments could further explore the effect on changes in seasonality of the climate response as well as large-scale circulation changes to advance our understanding and quantification of deforestation effects in the ESM frameworks.