The R Protein of SARS-CoV: Analyses of Structure and Function Based on Four Complete Genome Sequences of Isolates BJ01-BJ04

The R (replicase) protein is the uniquely defined non-structural protein (NSP) responsible for RNA replication, mutation rate or fidelity, regulation of transcription in coronaviruses and many other ssRNA viruses. Based on our complete genome sequences of four isolates (BJ01-BJ04) of SARS-CoV from Beijing, China, we analyzed the structure and predicted functions of the R protein in comparison with 13 other isolates of SARS-CoV and 6 other coronaviruses. The entire ORF (open-reading frame) encodes for two major enzyme activities, RNA-dependent RNA polymerase (RdRp) and proteinase activities. The R polyprotein undergoes a complex proteolytic process to produce 15 function-related peptides. A hydrophobic domain (HOD) and a hydrophilic domain (HID) are newly identified within NSP1. The substitution rate of the R protein is close to the average of the SARS-CoV genome. The functional domains in all NSPs of the R protein give different phylogenetic results that suggest their different mutation rate under selective pressure. Eleven highly conserved regions in RdRp and twelve cleavage sites by 3CLP (chymotrypsin-like protein) have been identified as potential drug targets. Findings suggest that it is possible to obtain information about the phylogeny of SARS-CoV, as well as potential tools for drug design, genotyping and diagnostics of SARS

Comparison of genetic impact on growth and wood traits between seedlings and clones from the same plus trees of Pinus koraiensis

To evaluate the relationships among clones and open pollinated families from the same plus trees and to select elite breeding materials, growth, and wood characteristics of 33-year-old Pinus koraiensis clones and families were measured and analyzed. The results show that growth and wood characters varied significantly. The variation due to clonal effects was higher than that of family effects. The ratio of genetic to phenotypic coefficient of variation of clones in growth and wood traits was above 90%, and the repeatability of these characteristics was more than 0.8, whereas the ratio of genetic to phenotypic coefficient of variation of families was above 90%. The broad-sense heritability of all characteristics exceeded 0.4, and the narrow-sense family heritability of growth traits was less than 0.3. Growth characteristics were positively correlated with each other, but most wood properties were weakly correlated in both clones and families. Fiber length and width were positively correlated between clones and families. Using the membership function method, eleven clones and four families were selected as superior material for improved diameter growth and wood production, and two families from clonal and open-pollinated trees showed consistently better performance. Generally, selection of the best clones is an effective alternative to deployment of families as the repeatability estimates from clonal trees were higher than narrow-sense heritability estimates from open pollinated families. The results provide valuable insight for improving P. koraiensis breeding programs and subsequent genetic improvement

Deep Transfer Learning Method Based on Automatic Domain Alignment and Moment Matching

Domain discrepancy is a key research problem in the field of deep domain adaptation. Two main strategies are used to reduce the discrepancy: the parametric method and the nonparametric method. Both methods have achieved good results in practical applications. However, research on whether the combination of the two can further reduce domain discrepancy has not been conducted. Therefore, in this paper, a deep transfer learning method based on automatic domain alignment and moment matching (DA-MM) is proposed. First, an automatic domain alignment layer is embedded in the front of each domain-specific layer of a neural network structure to preliminarily align the source and target domains. Then, a moment matching measure (such as MMD distance) is added between every domain-specific layer to map the source and target domain features output by the alignment layer to a common reproduced Hilbert space. The results of an extensive experimental analysis over several public benchmarks show that DA-MM can reduce the distribution discrepancy between the two domains and improve the domain adaptation performance

Analysis of Magnetic Field Intensity and Induced Current under Live Working Based on Charge Simulation Method

To the problem that safety distance is insufficient for 500 kV substation live working, a magnetic field analysis method for overhead line bus is given based on the charge simulation method. In the method, charge is calculated firstly, and the space field intensity distribution calculation is completed by overlying charge. The space field intensity distribution rule is carried out based on the appropriate analysis, and space field intensity distribution rule of substation is obtained. Then according to the calculation formula of inducing current, the human body induction current under a substation busbar is simulated based on MATLAB. The simulation results have a certain guidance function for actual live working

The Analytics of Bed Shortages:Coherent Metric, Prediction, and Optimization

Bed shortages in hospitals usually have a negative impact on patient satisfaction and medical outcomes. In practice, healthcare managers often use bed occupancy rates (BORs) as a metric to understand bed utilization, which is insufficient in capturing the risk of bed shortages. We propose the bed shortage index (BSI) to capture more facets of bed shortage risk than traditional metrics such as the occupancy rate, the probability of shortages, and expected shortages. The BSI is based on the riskiness index by Aumann and Serrano, and it is calibrated to coincide with BORs when the daily arrivals in the hospital unit are Poisson distributed. Our metric can be tractably computed and does not require additional assumptions or approximations. As such, it can be consistently used across the descriptive, predictive, and prescriptive analytical approaches. We also propose optimization models to plan for bed capacity via this metric. These models can be efficiently solved on a large scale via a sequence of linear optimization problems. The first maximizes total elective throughput while managing the metric under a specified threshold. The second determines the optimal scheduling policy by lexicographically minimizing the steady-state daily BSI for a given number of scheduled admissions. We validate these models using real data from a hospital and test them against data-driven simulations. We apply these models to study the real-world problem of long stayers to predict the impact of transferring them to community hospitals as a result of an aging population.</p

Robust Amino-Functionalized Mesoporous Silica Hollow Spheres Templated by CO₂ Bubbles

Hollow-structured mesoporous silica has wide applications in catalysis and drug delivery due to its high surface area, large hollow space, and short diffusion mesochannels. However, the synthesis of hollow structures usually requires sacrificial templates, leading to increased production costs and environmental problems. Here, for the first time, amino-functionalized mesoporous silica hollow spheres were synthesized by using CO2 gaseous bubbles as templates. The assembly of anionic surfactants, co-structure directing agents, and inorganic silica precursors around CO2 bubbles formed the mesoporous silica shells. The hollow silica spheres, 200–400 nm in size with 20–30 nm spherical shell thickness, had abundant amine groups on the surface of the mesopores, indicating excellent applications for CO2 capture, Knoevenagel condensation reaction, and the controlled release of Drugs

Analysis of Magnetic Field Intensity and Induced Current under Live Working Based on Charge Simulation Method

To the problem that safety distance is insufficient for 500 kV substation live working, a magnetic field analysis method for overhead line bus is given based on the charge simulation method. In the method, charge is calculated firstly, and the space field intensity distribution calculation is completed by overlying charge. The space field intensity distribution rule is carried out based on the appropriate analysis, and space field intensity distribution rule of substation is obtained. Then according to the calculation formula of inducing current, the human body induction current under a substation busbar is simulated based on MATLAB. The simulation results have a certain guidance function for actual live working

Increasing <i>para</i>-Xylene Selectivity in Making Aromatics from Methanol with a Surface-Modified Zn/P/ZSM‑5 Catalyst

We report a ZSM-5 based catalyst with surface modification of SiO₂ to increase the selectivity of <i>para</i>-xylene (PX) in xylene (X) in the methanol-to-aromatics process. The effect of acid strength and acid amount in HZSM-5, Zn/P/ZSM-5, and Zn/P/Si/ZSM-5 on the catalytic performance, including methanol conversion, aromatic yield, and PX selectivity, were studied. The total acid strength and acid amount of the catalyst were crucial for high methanol conversion (around 100%) and high yield of aromatics (>60%), whereas weak external acid sites present in a small amount played an important role in increasing the PX selectivity (in the X isomers) from the usual 23–24% to 89.6%. The results validated the use of a catalyst having a core with strong acid sites in a large amount and an external shell with weak acid sites in a small amount. The contribution of the external surface reaction, including alkylation, isomerization, and dealkylation, to the PX selectivity was evaluated by using PX or <i>ortho</i>-X separately as feedstock. A Zn/P/Si/ZSM-5 catalyst worked well in continuous reaction/catalyst-regeneration cycles, and it also converted recycled toluene into PX by an alkylation route