ALBERT

Description: Eucommia ulmoides (E. ulmoides) is a significant national strategic resource in China. It is a natural high quality rubber resource, with great development potential. We found large differences in rooting ability during adventitious root (AR) formation in two E. ulmoides improved varieties. Therefore, we used two improved varieties of E. ulmoides, ‘Huazhong 6’ (H6, with rooting rate 85.3%) and ‘Huazhong 8’ (H8, with rooting rate 22.5%) to explore the cutting rooting mechanism. In this study, we mainly determined the morphological development process of E. ulmoides cutting rooting, and compared the rooting-related indexes of the two improved varieties, and the changes in physiological indexes closely related to rooting, which include endogenous hormones, oxidases and nutrients in the phloem of the basal stem. The results showed that indole–3–acetic acid (IAA), zeatin riboside (ZR), IAA/ZR and indoleacetic acid oxidase (IAAO) were the key factors that caused big differences in rooting ability between the two E. ulmoides improved varieties. The increase in endogenous hormone IAA content and IAA/ZR value were necessary for the formation of AR. The increase in IAA content was beneficial to AR formation. The activity of IAAO was significantly negatively correlated with the rooting ability of the E. ulmoides cuttings. The high IAAO activity of the H8 cuttings led to the consumption of IAA. Although the content of IAA increased, the rooting conditions were not reached. The accumulation of nutrients before rooting also has an important effect on rooting; it is easy for cuttings to root when the carbon–nitrogen ratio (C/N) value is high. This research provides an improved understanding of the cellular and physiological underpinnings of the AR process in woody plants. In addition, it provides a theoretical basis and foundation for subsequent research on E. ulmoides cuttage technology.

Description: The Coronavirus Disease 2019 (COVID-19) infectious pneumonia pandemic highlights the importance of emergency management of public health emergencies (EMPHE). This paper addresses the challenge of building a knowledge system for EMPHE research that may contribute to understand the spatial and temporal characteristics of knowledge distribution, research status, cutting-edge research and development trends, and helps to identify promising research topics and guide research and practice of EMPHE. Based on the Web of Science, this paper retrieves 1467 articles about EMPHE published from 2010 to date. Then, based on high-frequency keywords, we use CiteSpace to analyze their knowledge co-occurrence network, clustering network and knowledge evolution. Furthermore, we summarize the features and gaps in EMPHE research, providing references for future research directions. Based on the above analysis, this work constructs a knowledge system about EMPHE research, providing a comprehensive visual summary of the existing research in the field of EMPHE, with the aim to guide future research and practice.

Description: Manganese dioxide (MnO2) nanosheets have shown exciting potential in nanomedicine because of their ultrathin thickness, large surface area, high near-infrared (NIR) absorbance and good biocompatibility. However, the effect of MnO2 nanosheets on bacteria is still unclear. In this study, MnO2 nanosheets were shown for the first time to possess highly efficient antibacterial activity by using Salmonella as a model pathogen. The growth curve and surface plate assay uncovered that 125 μg/mL MnO2 nanosheets could kill 99.2% of Salmonella, which was further verified by fluorescence-based live/dead staining measurement. Mechanism analysis indicated that MnO2 nanosheet treatment could dramatically induce reactive oxygen species production, increase ATPase activity and cause the leakage of electrolytes and protein contents, leading to bacterial death. These results uncover the previously undefined role of MnO2 nanosheets and provide novel strategies for developing antimicrobial agents.

Description: Saccharomyces cerevisiae and Lactobacillus plantarum are responsible for alcoholic and malolactic fermentation, respectively. Successful completion of both fermentations is essential for many styles of wine, and an understanding of how these species interact with each other, as well as the development of compatible pairings of these species, will help to manage the process. However, targeted improvements of species interactions are difficult to perform, in part because of the chemical and biological complexity of natural grape juice. Synthetic ecological systems reduce this complexity and can overcome these difficulties. In such synthetic systems, mutualistic growth of different species can be enforced through the reciprocal exchange of essential nutrients. Here, we implemented a novel approach to evolve mutualistic traits by establishing a co-dependent relationship between S. cerevisiae BY4742Δthi4 and Lb. plantarum IWBT B038 by omitting different combinations of amino acids from a chemically defined synthetic medium simulating standard grape juice. After optimization, the two species were able to support the growth of each other when grown in the absence of appropriate combinations of amino acids. In these obligatory mutualistic conditions, BY4742Δthi4 and IWBT B038 were co-evolved for approximately 100 generations. The selected evolved isolates showed improved mutualistic growth and the growth patterns under non-selective conditions indicate the emergence of mutually beneficial adaptations independent of the synthetic selection pressure. The combined use of synthetic ecology and co-evolution is a promising strategy to better understand and biotechnologically improve microbial interactions.

Description: As a new-type of ultra-supercritical HI-IP rotor steel, X12CrMoWVNbN10-1-1 alloy steel has excellent integrative performance, which can effectively improve the power generation efficiency of the generator set. In this paper, uniaxial thermal compression tests were carried out over a temperature range of 950–1200 °C and strain rates of 0.05–5 s−1 with a Gleeble-1500D thermal simulation testing machine. Moreover, based on hot compression experimental data and the theory of processing diagrams, in combination with the dynamic material model, a three-dimensional (3-D) thermal processing map considering the effect of strain was constructed. It was concluded that optimum thermal deformation conditions were as follows: the temperature range of 1150–1200 °C, the strain rate range of 0.05–0.634 s−1. Through secondary development of the finite element (FE) software FORGE®, three-dimensional thermal processing map data were integrated into finite element software FORGE®. The distributions of instability coefficient and power dissipation coefficient were obtained over various strain rates and temperatures of the Ø 8 × 12 mm cylinder specimen by using finite element simulation. It is shown that simulation results are consistent with the microstructure photos. The method proposed in this paper, which integrates the three-dimensional processing map into the finite element software FORGE® (Forge NxT 2.1, Transvalor, Nice, France), can effectively predict the formability of X12CrMoWVNbN10-1-1 alloy steel.

Description: The fast and accurate creation of land use/land cover maps from very-high-resolution (VHR) remote sensing imagery is crucial for urban planning and environmental monitoring. Geographic object-based image analysis methods (GEOBIA) provide an effective solution using image objects instead of individual pixels in VHR remote sensing imagery analysis. Simultaneously, convolutional neural networks (CNN) have been widely used in the image processing field because of their powerful feature extraction capabilities. This study presents a patch-based strategy for integrating deep features into GEOBIA for VHR remote sensing imagery classification. To extract deep features from irregular image objects through CNN, a patch-based approach is proposed for representing image objects and learning patch-based deep features, and a deep features aggregation method is proposed for aggregating patch-based deep features into object-based deep features. Finally, both object and deep features are integrated into a GEOBIA paradigm for classifying image objects. We explored the influences of segmentation scales and patch sizes in our method and explored the effectiveness of deep and object features in classification. Moreover, we performed 5-fold stratified cross validations 50 times to explore the uncertainty of our method. Additionally, we explored the importance of deep feature aggregation, and we evaluated our method by comparing it with three state-of-the-art methods in a Beijing dataset and Zurich dataset. The results indicate that smaller segmentation scales were more conducive to VHR remote sensing imagery classification, and it was not appropriate to select too large or too small patches as the patch size should be determined by imagery and its resolution. Moreover, we found that deep features are more effective than object features, while object features still matter for image classification, and deep feature aggregation is a critical step in our method. Finally, our method can achieve the highest overall accuracies compared with the state-of-the-art methods, and the overall accuracies are 91.21% for the Beijing dataset and 99.05% for the Zurich dataset.

Description: Based on mechanical properties of Polyamide 66 (PA66) under complex loading conditions, a Drucker–Prager yield criterion was employed to characterize its yield behavior. Then, a one-dimensional model, which contains a viscoelastic regime and a viscoplastic regime, was introduced and converted into a three-dimensional constitutive model. The three-dimensional model was implemented into a LS-DYNA software, which was used to predict the dynamic response of PA66 under Taylor impact conditions, whose corresponding tests were conducted by gas gun and recorded by high-speed camera. By contrasting the simulation results and these of the corresponding tests, the deformed shapes including the residual length, the maximum diameter and the shape of the mushroom head of the PA66 bars were found to be similar to these obtained from the tests, which verified the accuracy of the three-dimensional constitutive model, and proved that the model was able to be applied to high-rate impact loading conditions.

Description: A new strategy for preparing amphibious ZnO quantum dots (QDs) with blue fluorescence within hyper-branched poly(ethylenimine)s (HPEI) was proposed in this paper. By changing [Zn2+]/[OH−] molar ratio and heating time, ZnO QDs with a quantum yields (QY) of 30% in ethanol were obtained. Benefiting from the amphibious property of HPEI, the ZnO/HPEI nanocomposites in ethanol could be dissolved in chloroform and water, acquiring a QY of 53%, chloroform and 11% in water. By this strategy, the ZnO/HPEI nano-composites could be applied in not only in optoelectronics, but also biomedical fields (such as bio-imaging and gene transfection). The bio-imaging application of water-soluble ZnO/HPEI nanocomposites was investigated and it was found that they could easily be endocytosed by the COS-7 cells, without transfection reagent, and they exhibited excellent biological imaging behavior.

Description: In the production of acrylic acid, the concentration of acrylic acid solution from the adsorption tower was low, which would lead to significant energy consumption in the distillation process to purify acrylic acid, along with the production of a large amount of wastewater. Reverse osmosis (RO) was proposed to concentrate the acrylic acid aqueous solution taken from a specific tray in the absorption tower. The effects of operating conditions on the permeate flux and acid retention were studied with two commercial RO membranes (SWC5 and SWC6). When the operating pressure was 4 MPa and the temperature was 25 °C, the permeate fluxes of two membranes were about 20 L·m−2·h−1. The acrylic acid and acetic acid retentions were about 80% and 78%, respectively. After being immersed in the acid solutions for several months, the characteristics of the two membranes were tested to evaluate their acid resistance. After six months of exposure to the acid solution containing 2.5% acrylic acid and 2.5% acetic acid, the retentions of acrylic acid and acetic acid were decreased by 5.7% and 4.1% for SWC5 and 4.9% and 2.2% for SWC6, respectively. The changes of membrane surface morphology and chemical composition showed the hydrolysis of some amide bonds.