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Against the backdrop of globalization, interpreting, a translation communicative activity in a verbal way, plays an increasingly important role in international communications and exchanges. In response to this world pattern, the Chinese government attaches great importance to the interpreting industry. However, due to the national condition of uneven regional development, the English interpreting level across China is also unbalanced. Confronting this circumstance, previous research only stagnates at the level of recognizing the problem, but very few studies have attempted to solve the problem. Thus, the current study aims to figure out the regional interpreting level in mainland China by establishing and utilizing an innovative indicator system based on statistics and geography technologies. Based on the literature review and empirical questionnaire survey from different stakeholders, the study proposes an indicator system containing 3 first-level factors and 7 second-level factors to measure regional English interpreting levels. The weight of each indicator and scoring method is laid down based on factor analysis and interval marking. In addition, putting the innovative indicator system into practice, a total of 38 groups of regional data are collected to rank the regional interpreting level across China. Integrating with GIS and statistical techniques, the result visually shows that the English interpreting level across China is uneven at present: higher in the southern and eastern parts of China compared to that of northern and western China, which is unfriendly to sustainable development in the future. Facing this reality, a following-up analysis has been made for offering explanations of the results and suggestions for regional interpreting sustainable development.

The COVID-19 outbreak has caused two waves and spread to more than 90% of Canada's provinces since it was first reported more than a year ago. During the COVID-19 epidemic, Canadian provinces have implemented many Non-Pharmaceutical Interventions (NPIs). However, the spread of the COVID-19 epidemic continues due to the complex dynamics of human mobility. We develop a meta-population network model to study the transmission dynamics of COVID-19. The model takes into account the heterogeneity of mitigation strategies in different provinces of Canada, such as the timing of implementing NPIs, the human mobility in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences due to work and recreation. To determine which activity is most closely related to the dynamics of COVID-19, we use the cross-correlation analysis to find that the positive correlation is the highest between the mobility data of parks and the weekly number of confirmed COVID-19 from February 15 to December 13, 2020. The average effective reproduction numbers in nine Canadian provinces are all greater than one during the time period, and NPIs have little impact on the dynamics of COVID-19 epidemics in Ontario and Saskatchewan. After November 20, 2020, the average infection probability in Alberta became the highest since the start of the COVID-19 epidemic in Canada. We also observe that human activities around residences do not contribute much to the spread of the COVID-19 epidemic. The simulation results indicate that social distancing and constricting human mobility is effective in mitigating COVID-19 transmission in Canada. Our findings can provide guidance for public health authorities in projecting the effectiveness of future NPIs.

Rift Valley fever is a vector-borne zoonotic disease which causes high morbidity and mortality in livestock. In the event Rift Valley fever virus is introduced to the United States or other non-endemic areas, understanding the potential patterns of spread and the areas at risk based on disease vectors and hosts will be vital for developing mitigation strategies. Presented here is a general network-based mathematical model of Rift Valley fever. Given a lack of empirical data on disease vector species and their vector competence, this discrete time epidemic model uses stochastic parameters following several PERT distributions to model the dynamic interactions between hosts and likely North American mosquito vectors in dispersed geographic areas. Spatial effects and climate factors are also addressed in the model. The model is applied to a large directed asymmetric network of 3,621 nodes based on actual farms to examine a hypothetical introduction to some counties of Texas, an important ranching area in the United States of America. The nodes of the networks represent livestock farms, livestock markets, and feedlots, and the links represent cattle movements and mosquito diffusion between different nodes. Cattle and mosquito (Aedes and Culex) populations are treated with different contact networks to assess virus propagation. Rift Valley fever virus spread is assessed under various initial infection conditions (infected mosquito eggs, adults or cattle). A surprising trend is fewer initial infectious organisms result in a longer delay before a larger and more prolonged outbreak. The delay is likely caused by a lack of herd immunity while the infection expands geographically before becoming an epidemic involving many dispersed farms and animals almost simultaneously. Cattle movement between farms is a large driver of virus expansion, thus quarantines can be efficient mitigation strategy to prevent further geographic spread.

FoxM1 is a forkhead box transcription factor and a known master regulator required for different phases of the cell cycle. In cell lines, FoxM1 deficient cells exhibit delayed S phase entry, aneuploidy, polyploidy and can't complete mitosis. In vivo, FoxM1 is expressed mostly in proliferating cells but is surprisingly also found in non-proliferating CD4(+)CD8(+) double positive thymocytes. Here, we addressed the role of FoxM1 in T cell development by generating and analyzing two different lines of T-cell specific FoxM1 deficient mice. As expected, FoxM1 is required for proliferation of early thymocytes and activated mature T cells. Defective expression of many cell cycle proteins was detected, including cyclin A, cyclin B1, cdc2, cdk2, p27 and the Rb family members p107 and p130 but surprisingly not survivin. Unexpectedly, loss of FoxM1 only affects a few cell cycle proteins in CD4(+)CD8(+) thymocytes and has little effect on their sensitivity to apoptosis and the subsequent steps of T cell differentiation. Thus, regulation of cell cycle genes by FoxM1 is stage- and context-dependent.

Endophytic bacteria, which are common in plant tissues, may help to control plant pathogens and enhance plant growth. Camellia oleifera, an oil-producing plant, is widely grown in warm, subtropical, hilly regions in China. However, C. oleifera is strongly negatively affected by C. oleifera anthracnose, which is caused by Colletetrichum fructicola. To find a suitable biocontrol agent for C. oleifera anthracnose, 41 endophytes were isolated from the stems, leaves, and roots of C. oleifera. Bacterial cultures were identified based on analyses of 16S rDNA sequences; most strains belonged to the genus Bacillus. The antagonistic effects of these strains on C. fructicola were tested in vitro. In total, 16 strains inhibited C. fructicola growth, with B. subtilis strain 1-L-29 being the most efficient. Strain 1-L-29 demonstrated antagonistic activity against C. siamense, C. asianum, Fusarium proliferatum, Agaricodochium camellia, and Pseudomonas syringae. In addition, this strain produced indole acetic acid, solubilized phosphate, grew on N-free media, and produced siderophores. To facilitate further microecological studies of this strain, a rifampicin-resistant, green fluorescent protein (GFP)-labeled strain, 1-L-29gfpr, was created using protoplast transformation. This plasmid had good segregational stability. Strain 1-L-29gfpr was re-introduced into C. oleifera and successfully colonized root, stem, and leaf tissues. This strain remained at a stable concentration in the root more than 20 d after inoculation. Fluorescence microscopic analysis showed that strain 1-L-29gfpr thoroughly colonized the root surfaces of C. fructicola as well as the root vascular tissues of Arabidopsis thaliana.

The highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) is still a serious threat to the swine industry. However, the pathogenic mechanism of HP-PRRSV remains unclear. We infected host porcine alveolar macrophages (PAMs) with the virulent HuN4 strain and the attenuated HuN4-F112 strain and then utilized fluorescent two-dimensional difference gel electrophoresis (2D-DIGE) to screen for intracellular proteins that were differentially expressed in host cells infected with the two strains. There were 153 proteins with significant different expression (P