The stability of transgene expression and effect of DNA methylation on post transcriptional gene silencing (PTGS) in birch

In this paper, we selected transgenic birch (Betula platyphylla Suk) plants, which included nonsilencing plants, transcriptional silence plants including TP96, TP74, TP73 and the post-transcriptional silence ones (TP67 and TP72). The transcription of the bgt gene in different tissues and organs were significantly different. The transcriptional level of bgt gene in the different tissues and organs was in the following order: leaf > female flower and male flower > branch bark > phloem > root. The transgenic lines were monitored for foreign gene expression for a long-term period of 8 years during their continuous growth under field conditions. GUS protein expression was not reactivated in the transgene silencing lines TP72 and TP67 when cultured in field conditions for long-term period. Meanwhile, no cases of gene silencing were observed again during the study period in the field conditions. Our results suggest that transgene expression in transgenic birch plants appears to be stable under field conditions. The frequencies of methylated cytosines in the code regions of gus gene was studied. Relation of transgene expression and DNA methylation was analysed. The data of restriction enzyme digestion (HpaII and MspI) indicated that DNA methylation resulted in post transcriptional gene silencing (PTGS) in transgenic birch.Key words: Transgenic birch, DNA methylation, gene silencing

Effects of livestock grazing on soil nitrogen mineralization on Hulunber meadow steppe, China

Soil nitrogen (N) cycling is an important factor in terrestrial ecosystems, including grasslands. Understanding the effects of grazing on nitrogen cycling in grassland ecosystems is critical for better management and for improving knowledge of the mechanisms underlying grassland degradation and can provide basic information for sustainable development in grassland ecosystems. In this study, in situ incubation in intact soil cores was used to measure seasonal changes in soil nitrogen mineralization and nitrification in the meadow steppe of the Hulunber grasslands of northeastern China. Soil plots were subjected to varying intensities of cattle grazing, and soil characteristics including several aspects of the nitrogen cycle were analysed. The findings demonstrate that soil inorganic N pools and nitrogen mineralization peaked in August and that moderate grazing intensity produced higher seasonal mean net N mineralization (Amin); net nitrogen mineralization rate (Rmin); net ammonification rate (Ramm) and net nitrification rate (Rnit). Seasonal mean net mineralization rate was increased by 6–15% in the lightly and moderately grazed plots (0.34–0.46 AU cow/ha) and by 4–5% in the heavily grazed plots (0.69–0.92 AU cow/ha). Also it was found that soil moisture was significantly positively correlated with inorganic N, Amin, Ramm and Rmin and significantly negatively correlated with Rnit, while soil temperature exhibited the opposite effect. The obtained results demonstrated net nitrogen mineralization and ammonium rates, which were strongly linked to grazing intensity, soil temperature and soil moisture

Reconstruction of 5D5D Cosmological Models From Equation of State of Dark Energy

We consider a class of five-dimensional cosmological solutions which contains two arbitrary function $\mu(t)$ and $\nu(t)$. We found that the arbitrary function $\mu(t)$ contained in the solutions can be rewritten in terms of the redshift $z$ as a new arbitrary function $f(z)$. We further showed that this new arbitrary function $f(z)$ could be solved out for four known parameterized equations of state of dark energy. Then the $5D$ models can be reconstructed and the evolution of the density and deceleration parameters of the universe can be determined.Comment: 10 pages, 4 eps figures, ws-ijmpd.cls styl

Object Picture of Quasinormal Modes for Stringy Black Holes

We study the quasinormal modes (QNMs) for stringy black holes. By using numerical calculation, the relations between the QNMs and the parameters of black holes are minutely shown. For (1+1)-dimensional stringy black hole, the real part of the quasinormal frequency increases and the imaginary part of the quasinormal frequency decreases as the mass of the black hole increases. Furthermore, the dependence of the QNMs on the charge of the black hole and the flatness parameter is also illustrated. For (1+3)-dimensional stringy black hole, increasing either the event horizon or the multipole index, the real part of the quasinormal frequency decreases. The imaginary part of the quasinormal frequency increases no matter whether the event horizon is increased or the multipole index is decreased.Comment: 4 pages, 5 figure

Heat Diffusion-Induced Gradient Energy Level in Multishell Bisulfides for Highly Efficient Photocatalytic Hydrogen Production

© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Insufficient light absorption and low carrier separation/transfer efficiency constitute two key issues that hinder the development of efficient photocatalytic hydrogen production. Here, multishell ZnS/CoS2 bisulfide microspheres with gradient distribution of Zn based on the heat diffusion theory are designed. The Zn distribution can be adjusted by regulating the heating rate and manipulating the diffusion coefficients of the different elements conforming the multishell photocatalyst. Because of the unique structure, a gradient energy level is created from the core to the exterior of the multishell microspheres, which effectively facilitates the exciton separation and electron transfer. In addition, stronger light absorption and larger specific surface area have been achieved in the multishell ZnS/CoS2 photocatalysts. As a result, the multishell ZnS/CoS2 microspheres with gradient distribution of Zn exhibit a remarkable hydrogen production rate of 8001 µmol g−1 h−1, which is 3.5 times higher than that of the normal multishell ZnS/CoS2 particles with well-distributed Zn and 11.3 times higher than that of the mixed nonshell ZnS and CoS2 particles. This work demonstrates for the first time that controlling the diffusion rate of the different elements in the semiconductor is an effective route to simultaneously regulate morphology and structure to design highly efficient photocatalysts

Multi-level adaptive active learning for scene classification

Semantic scene classification is a challenging problem in computer vision. In this paper, we present a novel multi-level active learning approach to reduce the human annotation effort for training robust scene classification models. Different from most existing active learning methods that can only query labels for selected instances at the target categorization level, i.e., the scene class level, our approach establishes a semantic framework that predicts scene labels based on a latent object-based semantic representation of images, and is capable to query labels at two different levels, the target scene class level (abstractive high level) and the latent object class level (semantic middle level). Specifically, we develop an adaptive active learning strategy to perform multi-level label query, which maintains the default label query at the target scene class level, but switches to the latent object class level whenever an "unexpected" target class label is returned by the labeler. We conduct experiments on two standard scene classification datasets to investigate the efficacy of the proposed approach. Our empirical results show the proposed adaptive multi-level active learning approach can outperform both baseline active learning methods and a state-of-the-art multi-level active learning method

Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data

COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. Understanding the mechanisms driving disease severity is crucial for developing effective treatments and reducing mortality rates. One way to gain such understanding is using a multi-class classification framework, in which patients' biological features are used to predict patients' severity classes. In this severity classification problem, it is beneficial to prioritize the identification of more severe classes and control the "under-classification" errors, in which patients are misclassified into less severe categories. The Neyman-Pearson (NP) classification paradigm has been developed to prioritize the designated type of error. However, current NP procedures are either for binary classification or do not provide high probability controls on the prioritized errors in multi-class classification. Here, we propose a hierarchical NP (H-NP) framework and an umbrella algorithm that generally adapts to popular classification methods and controls the under-classification errors with high probability. On an integrated collection of single-cell RNA-seq (scRNA-seq) datasets for 864 patients, we explore ways of featurization and demonstrate the efficacy of the H-NP algorithm in controlling the under-classification errors regardless of featurization. Beyond COVID-19 severity classification, the H-NP algorithm generally applies to multi-class classification problems, where classes have a priority order

Comprehensive Characterization of the Transmitted/Founder env Genes From a Single MSM Cohort in China

Background: The men having sex with men (MSM) population has become one of the major risk groups for HIV-1 infection in China. However, the epidemiological patterns, function of the env genes, and autologous and heterologous neutralization activity in the same MSM population have not been systematically characterized. Methods: The env gene sequences were obtained by the single genome amplification. The time to the most recent common ancestor was estimated for each genotype using the Bayesian Markov Chain Monte Carlo approach. Coreceptor usage was determined in NP-2 cells. Neutralization was analyzed using Env pseudoviruses in TZM-bl cells. Results: We have obtained 547 full-length env gene sequences by single genome amplification from 30 acute/early HIV-1–infected individuals in the Beijing MSM cohort. Three genotypes (subtype B, CRF01_AE, and CRF07_BC) were identified and 20% of the individuals were infected with multiple transmitted/founder (T/F) viruses. The tight clusters of the MSM sequences regardless of geographic origins indicated nearly exclusive transmission within the MSM population and limited number of introductions. The time to the most recent common ancestor for each genotype was 10–15 years after each was first introduced in China. Disparate preferences for coreceptor usages among 3 genotypes might lead to the changes in percentage of different genotypes in the MSM population over time. The genotype-matched and genotype-mismatched neutralization activity varied among the 3 genotypes. Conclusions: The identification of unique characteristics for transmission, coreceptor usage, neutralization profile, and epidemic patterns of HIV-1 is critical for the better understanding of transmission mechanisms, development of preventive strategies, and evaluation of vaccine efficacy in the MSM population in China