Slabs in the lower mantle and their modulation of plume formation

Numerical mantle convection models indicate that subducting slabs can reach the core-mantle boundary (CMB) for a wide range of assumed material properties and plate tectonic histories. An increase in lower mantle viscosity, a phase transition at 660 km depth, depth-dependent thermal expansivity, and depth-dependent thermal diffusivity do not preclude model slabs from reaching the CMB. We find that ancient slabs could be associated with lateral temperature anomalies ~500°C cooler than ambient mantle. Plausible increases of thermal conductivity with depth will not cause slabs to diffuse away. Regional spherical models with actual plate evolutionary models show that slabs are unlikely to be continuous from the upper mantle to the CMB, even for radially simple mantle structures. The observation from tomography showing only a few continuous slab-like features from the surface to the CMB may be a result of complex plate kinematics, not mantle layering. There are important consequences of deeply penetrating slabs. Our models show that plumes preferentially develop on the edge of slabs. In areas on the CMB free of slabs, plume formation and eruption are expected to be frequent while the basal thermal boundary layer would be thin. However, in areas beneath slabs, the basal thermal boundary layer would be thicker and plume formation infrequent. Beneath slabs, a substantial amount of hot mantle can be trapped over long periods of time, leading to “mega-plume” formation. We predict that patches of low seismic velocity may be found beneath large-scale high seismic velocity structures at the core-mantle boundary. We find that the location, buoyancy, and geochemistry of mega-plumes will differ from those plumes forming at the edge of slabs. Various geophysical and geochemical implications of this finding are discussed

Lactobacillus pentosus expressing porcine lactoferrin elevates antibacterial activity and improves the efficacy of vaccination against Aujeszky’s disease

In this study, Lactobacillus pentosus expressing porcine lactoferrin (pLF) was tested for in vitro antibacterial activity and for its ability to enhance immunity induced by an orally administered Aujeszky’s disease virus (ADV) vaccine. The cDNA encoding N-terminus of pLF was cloned into a Lactobacillus-specific plasmid to produce L. pentosus pLF expressing transformants (pPG612.1-pLFN/ L. pentosus). The antimicrobial activity of the recombinant pLF protein inhibited bacterial growth in vitro. The supernatant of pPG612.1-pLF-N/L. pentosus had an inhibitory effect on Staphylococcus aureus strain CVCC26003, Bacillus subtilis strain CVCC63501, Escherichia coli strain CVCC10141 and Salmonella enterica ssp. enterica Choleraesuis strain CVCC79102, while it did not inhibit the growth of Lactobacillus casei strain ATCC393. A mouse model was established to test the effectiveness of the orally administered probiotic L. pentosus recombinant strain in the gastrointestinal tract. Mice were immunised with an attenuated porcine Aujeszky’s disease virus (ADV) vaccine. Serum antibody levels determined using a mouse Aujeszky’s disease IgG ELISA showed that IgG levels were significantly higher in the pPG612.1-pLFN/L. pentosus group than in the PBS and Lactobacillus pentosus groups at days 7 and 21 (P < 0.01) and at day 14 (P < 0.05), indicating that this oral recombinant strain can improve the effectiveness of the vaccine and play a role in immune enhancement through humoral immunity. These results suggest that the recombinant Lactobacillus pentosus not only has the beneficial characteristics of lactic acid bacteria but also produces biologically functional lactoferrin

Phosphorylation of TGB1 by protein kinase CK2 promotes barley stripe mosaic virus movement in monocots and dicots.

The barley stripe mosaic virus (BSMV) triple gene block 1 (TGB1) protein is required for virus cell-to-cell movement. However, little information is available about how these activities are regulated by post-translational modifications. In this study, we showed that the BSMV Xinjiang strain TGB1 (XJTGB1) is phosphorylated in vivo and in vitro by protein kinase CK2 from barley and Nicotiana benthamiana. Liquid chromatography tandem mass spectrometry analysis and in vitro phosphorylation assays demonstrated that Thr-401 is the major phosphorylation site of the XJTGB1 protein, and suggested that a Thr-395 kinase docking site supports Thr-401 phosphorylation. Substitution of Thr-395 with alanine (T395A) only moderately impaired virus cell-to-cell movement and systemic infection. In contrast, the Thr-401 alanine (T401A) virus mutant was unable to systemically infect N. benthamiana but had only minor effects in monocot hosts. Substitution of Thr-395 or Thr-401 with aspartic acid interfered with monocot and dicot cell-to-cell movement and the plants failed to develop systemic infections. However, virus derivatives with single glutamic acid substitutions at Thr-395 and Thr-401 developed nearly normal systemic infections in the monocot hosts but were unable to infect N. benthamiana systemically, and none of the double mutants was able to infect dicot and monocot hosts. The mutant XJTGB1T395A/T401A weakened in vitro interactions between XJTGB1 and XJTGB3 proteins but had little effect on XJTGB1 RNA-binding ability. Taken together, our results support a critical role of CK2 phosphorylation in the movement of BSMV in monocots and dicots, and provide new insights into the roles of phosphorylation in TGB protein functions

A High Throughput Barley Stripe Mosaic Virus Vector for Virus Induced Gene Silencing in Monocots and Dicots

Barley stripe mosaic virus (BSMV) is a single-stranded RNA virus with three genome components designated alpha, beta, and gamma. BSMV vectors have previously been shown to be efficient virus induced gene silencing (VIGS) vehicles in barley and wheat and have provided important information about host genes functioning during pathogenesis as well as various aspects of genes functioning in development. To permit more effective use of BSMV VIGS for functional genomics experiments, we have developed an Agrobacterium delivery system for BSMV and have coupled this with a ligation independent cloning (LIC) strategy to mediate efficient cloning of host genes. Infiltrated Nicotiana benthamiana leaves provided excellent sources of virus for secondary BSMV infections and VIGS in cereals. The Agro/LIC BSMV VIGS vectors were able to function in high efficiency down regulation of phytoene desaturase (PDS), magnesium chelatase subunit H (ChlH), and plastid transketolase (TK) gene silencing in N. benthamiana and in the monocots, wheat, barley, and the model grass, Brachypodium distachyon. Suppression of an Arabidopsis orthologue cloned from wheat (TaPMR5) also interfered with wheat powdery mildew (Blumeria graminis f. sp. tritici) infections in a manner similar to that of the A. thaliana PMR5 loss-of-function allele. These results imply that the PMR5 gene has maintained similar functions across monocot and dicot families. Our BSMV VIGS system provides substantial advantages in expense, cloning efficiency, ease of manipulation and ability to apply VIGS for high throughput genomics studies

On the double-band luminescence of ZnO nanoparticles

Two luminescence bands from zinc oxide (ZnO) nanoparticles are known and have been experimentally observed previously. The unanswered question concerns the mechanism leading to the visible spectrum in the blue or green region. So far there have been many postulations trying to elucidate this phenomenon, but none of them gives a mathematical expression that simultaneously explain these two spectra. Here we interpret this phenomenon as the combination of distribution functions and the density of states of electrons and holes, precisely the product of both. From the analysis, the narrow UV emission is predominantly attributed to the quantum confinement, and the product of the density of states and the distribution functions determines the visible spectrum. We find that varying the density and the effective mass of holes causes a pronounced effect on both UV and visible emissions, which reflects the fact that acceptors take the main responsibility in the experimental observations

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.</p

Lack of Airborne Transmission during Outbreak of Pandemic (H1N1) 2009 among Tour Group Members, China, June 2009

This outbreak was caused by droplet transmission