Nutlin-3 overcomes arsenic trioxide resistance and tumor metastasis mediated by mutant p53 in Hepatocellular Carcinoma

Background: Arsenic trioxide has been demonstrated as an effective anti-cancer drug against leukemia and solid tumors both in vitro and in vivo. However, recent phase II trials demonstrated that single agent arsenic trioxide was poorly effective against hepatocellular carcinoma (HCC), which might be due to drug resistance. Methods: Mutation detection of p53 gene in arsenic trioxide resistant HCC cell lines was performed. The therapeutic effects of arsenic trioxide and Nutlin-3 on HCC were evaluated both in vitro and in vivo. A series of experiments including MTT, apoptosis assays, co-Immunoprecipitation, siRNA transfection, lentiviral infection, cell migration, invasion, and epithelial-mesenchy-mal transition (EMT) assays were performed to investigate the underlying mechanisms. Results: The acquisition of p53 mutation contributed to arsenic trioxide resistance and enhanced metastatic potential of HCC cells. Mutant p53 (Mutp53) silence could re-sensitize HCC resistant cells to arsenic trioxide and inhibit the metastatic activities, while mutp53 overexpression showed the opposite effects. Neither arsenic trioxide nor Nutlin-3 could exhibit obvious effects against arsenic trioxide resistant HCC cells, while combination of them showed significant effects. Nutlin-3 can not only increase the intracellular arsenicals through inhibition of p-gp but also promote the p73 activation and mutp53 degradation mediated by arsenic trioxide. In vivo experiments indicated that Nutlin-3 can potentiate the antitumor activities of arsenic trioxide in an orthotopic hepatic tumor model and inhibit the metastasis to lung. Conclusions: Acquisitions of p53 mutations contributed to the resistance of HCC to arsenic trioxide. Nutlin-3 could overcome arsenic trioxide resistance and inhibit tumor metastasis through p73 activation and promoting mutant p53 degradation mediated by arsenic trioxide

Surface skyrmions and dual topological Hall effect in antiferromagnetic topological insulator EuCd2_2As2_2

In this work, we synthesized single crystal of EuCd$_2$As$_2$, which exhibits A-type antiferromagnetic (AFM) order with in-plane spin orientation below $T_N$ = 9.5~K.Optical spectroscopy and transport measurements suggest its topological insulator (TI) nature with an insulating gap around 0.1eV. Remarkably, a dual topological Hall resistivity that exhibits same magnitude but opposite signs in the positive to negative and negative to positive magnetic field hysteresis branches emerges below 20~K. With magnetic force microscopy (MFM) images and numerical simulations, we attribute the dual topological Hall effect to the N\'{e}el-type skyrmions stabilized by the interactions between topological surface states and magnetism, and the sign reversal in different hysteresis branches indicates potential coexistence of skyrmions and antiskyrmions. Our work uncovers a unique two-dimensional (2D) magnetism on the surface of intrinsic AFM TI, providing a promising platform for novel topological quantum states and AFM spintronic applications.Comment: 7 pages, 3 figure

Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height

The Indonesian Throughflow (ITF) connects the tropical Pacific and Indian Oceans and is critical to the regional and global climate systems. Previous research indicates that the Indo-Pacific pressure gradient is a major driver of the ITF, implying the possibility of forecasting ITF transport by the sea surface height (SSH) of the Indo-Pacific Ocean. Here we used a deep-learning approach with the convolutional neural network (CNN) model to reproduce ITF transport. The CNN model was trained with a random selection of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations and verified with residual components of the CMIP6 simulations. A test of the training results showed that the CNN model with SSH is able to reproduce approximately 90% of the total variance of ITF transport. The CNN model with CMIP6 was then transformed to the Simple Ocean Data Assimilation (SODA) dataset and this transformed model reproduced approximately 80% of the total variance of ITF transport in the SODA. A time series of ITF transport, verified by Monitoring the ITF (MITF) and International Nusantara Stratification and Transport (INSTANT) measurements of ITF, was then produced by the model using satellite observations from 1993 to 2021. We discovered that the CNN model can make a valid prediction with a lead time of 7 months, implying that the ITF transport can be predicted using the deep-learning approach with SSH data

Hydrochemical Fluxes in Bulk Precipitation, Throughfall, and Stemflow in a Mixed Evergreen and Deciduous Broadleaved Forest

Rainfall is one of the primary sources of chemical inputs in forest ecosystems, and the basis of forest nutrient cycling. Mixed evergreen and deciduous broadleaved forests are currently one of the most threatened ecosystems due to their sensitivity to anthropogenic climate change. As such, understanding the hydrochemical fluxes of these systems is critical for managing their dynamics in the future. We investigate the chemistry of bulk precipitation, stemflow and throughfall in a mixed evergreen and deciduous broadleaved forest in the Shennongjia region of Central China. Mean nutrient concentrations in throughfall and stemflow were higher than in bulk precipitation. Stemflow ion fluxes from deciduous tree species were greater than those for evergreen tree species because of the differences in bark morphology and branch architecture. Throughfall and stemflow chemistry fluctuated dramatically over the growing season. Nitrate nitrogen and ammonium nitrogen were retained, while other elements and compounds were washed off or leached via throughfall and stemflow pathways. Our findings will facilitate a greater understanding of nutrient balance in canopy water fluxes

Modelling interception loss using the revised Gash model: a case study in a mixed evergreen and deciduous broadleaved forest in China

Interception loss accounts for a substantial portion of incident precipitation and evapotranspiration in forest ecosystems. Hence, identifying its magnitude is crucial for our understanding of biogeochemical cycling and related hydrological processes. In this study, gross rainfall partitioning into interception loss, throughfall and stemflow were measured and modelled using the revised Gash model for a mixed evergreen and deciduous broadleaved forest over the 2014 growing season. Field survey results revealed that interception loss accounted for 14.3% of gross rainfall, while understory rainfall was 84.8% throughfall and 0.9% stemflow. The revised Gash model produced a fairly good agreement between observed and estimated rainfall partitioning. The model underestimated interception loss by only 6.6%, while throughfall and stemflow were also slightly misestimated. Hence the interception loss predictions from the model were robust and reliable for this mixed evergreen and deciduous broadleaved forest. As quantified by the model, the vast majority of interception loss occurred as evaporation from the canopy under saturated conditions: 54.9% evaporated during rainfall events, and 38.3% after rainfall ceased. The sensitivity analysis indicated that predictions from the revised Gash model were most affected by changes in canopy storage capacity (S), followed by the mean evaporation rate ((E) over bar) during rainfall events, the mean rainfall rate ((R) over bar) and last canopy cover (c). Model predictions were least sensitive to trunk parameters (St and pt). Copyright (C) 2016 John Wiley & Sons, Ltd

Variability of throughfall quantity in a mixed evergreen-deciduous broadleaved forest in central China

Mixed evergreen-deciduous broadleaved forest is the transitional type of evergreen broadleaved forest and deciduous broadleaved forest, and plays a unique eco-hydrologic role in terrestrial ecosystem. We investigated the spatiotemporal patterns of throughfall volume of the forest type in Shennongjia, central China. The results indicated that throughfall represented 84.8% of gross rainfall in the forest. The mean CV (coefficient of variation) of throughfall was 27.27%. Inter-event variability in stand-scale throughfall generation can be substantially altered due to changes in rainfall characteristics, throughfall CV decreased with increasing rainfall amount and intensity, and reached a quasi-constant level when rainfall amount reached 25 mm or rainfall intensity reached 2 mm h(-1). During the leafed period, the spatial pattern of throughfall was highly temporal stable, which may result in spatial heterogeneity of soil moisture