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ObjectiveTwo COVID-19 outbreaks occurred in Henan province in early 2022—one was a Delta variant outbreak and the other was an Omicron variant outbreak. COVID-19 vaccines used at the time of the outbreak were inactivated, 91.8%; protein subunit, 7.5%; and adenovirus5-vectored, 0.7% vaccines. The outbreaks provided an opportunity to evaluate variant-specific breakthrough infection rates and relative protective effectiveness of homologous inactivated COVID-19 vaccine booster doses against symptomatic infection and pneumonia.DesignRetrospective cohort studyMethodsWe evaluated relative vaccine effectiveness (rVE) with a retrospective cohort study of close contacts of infected individuals using a time-dependent Cox regression model. Demographic and epidemiologic data were obtained from the local Centers for Disease Control and Prevention; clinical and laboratory data were obtained from COVID-19-designated hospitals. Vaccination histories were obtained from the national COVID-19 vaccination dataset. All data were linked by national identification number.ResultsAmong 784 SARS-CoV-2 infections, 379 (48.3%) were caused by Delta and 405 (51.7%) were caused by Omicron, with breakthrough rates of 9.9% and 17.8%, respectively. Breakthrough rates among boosted individuals were 8.1% and 4.9%. Compared with subjects who received primary vaccination series ≥180 days before infection, Cox regression modelling showed that homologous inactivated booster vaccination was statistically significantly associated with protection from symptomatic infection caused by Omicron (rVE 59%; 95% CI 13% to 80%) and pneumonia caused by Delta (rVE 62%; 95% CI 34% to 77%) and Omicron (rVE 87%; 95% CI 3% to 98%).ConclusionsCOVID-19 vaccination in China provided good protection against symptomatic COVID-19 and COVID-19 pneumonia caused by Delta and Omicron variants. Protection declined 6 months after primary series vaccination but was restored by homologous inactivated booster doses given 6 months after the primary series.

In the present paper, the unsteady cavitating turbulent flow over the twisted NACA66 hydrofoil is investigated based on an modified shear stress transfer k - ω partially averaged Navier-Stokes (MSST PANS) model, i.e., new MSST PANS (NMSST PANS) model, where the production term of kinetic energy in the turbulence model is modified with helicity. Compared with the experimental data, cavitation evolution and its characteristic frequency are satisfactorily predicted by the proposed NMSST PANS model. It is revealed that the interaction among the main flow, the reentrant jets, and sheet cavitation causes the formation of the primary shedding cavity near the mid-span and the secondary shedding cavity at each side of the twisted hydrofoil, and further induces the remarkable pressure gradient around shedding cavities. Along with the development of the primary and the secondary shedding cavities, the great pressure gradient associated with large cavity volume variation promotes the vortical flow generation and the spatial deformation of vortex structure during cavitation evolution, and results in the primary and the secondary U-type vortices. Further, dynamic mode decomposition (DMD) analysis is utilized to confirm the interaction among the main flow, the main reentrant jet and two side reentrant jets, and cavitation. These results indicate that the proposed NMSST PANS model is suitable to simulate the complicated cavitating turbulent flow for various engineering applications.

The primary impediment to the recovery of heavy oil lies in its high viscosity, which necessitates a deeper understanding of the molecular mechanisms governing its dynamic behavior for enhanced oil recovery. However, there remains a dearth of understanding regarding the complex molecular composition inherent to heavy oil. In this study, we employed high-resolution mass spectrometry in conjunction with various chemical derivatization and ionization methods to obtain semi-quantitative results of molecular group compositions of 35 heavy oils. The gradient boosting (GB) model has been further used to acquire the feature importance rank (FIR). A feature is an independently observable property of the observed object. Feature importance can measure the contribution of each input feature to the model prediction result, indicate the degree of correlation between the feature and the target, unveil which features are indicative of certain predictions. We have developed a framework for utilizing physical insights into the impact of molecular group compositions on viscosity. The results of machine learning (ML) conducted by GB show that the viscosity of heavy oils is primarily influenced by light components, specifically small molecular hydrocarbons with low condensation degrees, as well as petroleum acids composed of acidic oxygen groups and neutral nitrogen groups. Additionally, large molecular aromatic hydrocarbons and sulfoxides also play significant roles in determine the viscosity.

A total of 18% of global breast cancer (BC) deaths are attributed to BC in China, making it one of the five most common cancers there. There has been a steady rise in BC morbidity and mortality in women in the last few years and it is now a leading cancer among Chinese women. Conventional treatments for BC are currently effective but have several limitations and disadvantages, and Traditional Chinese medicine (TCM) plays a vital role in the overall process of cancer prevention and therapy. It is known that TCM can treat a variety of conditions at a variety of sites and targets. In recent years, increasingly, research has been conducted on TCM's ability to treat BC. TCM has shown positive results in the treatment of breast cancer and the adverse effects of radiotherapy and chemotherapy. This review describes the progress of clinical observation and mechanism research of TCM in the treatment of breast cancer in recent years. It provides some ideas and theoretical basis for the treatment of BC with TCM. Keywords: formulas, acupuncture, mechanism of action, therapy

The COVID-19 pandemic, caused by SARS-CoV-2, demonstrated that zoonotic transmission of animal sarbecoviruses threatens human health but the determinants of transmission are incompletely understood. Here, we show that most spike (S) proteins of horseshoe bat and Malayan pangolin sarbecoviruses employ ACE2 for entry, with human and raccoon dog ACE2 exhibiting broad receptor activity. The insertion of a multibasic cleavage site into the S proteins increased entry into human lung cells driven by most S proteins tested, suggesting that acquisition of a multibasic cleavage site might increase infectivity of diverse animal sarbecoviruses for the human respiratory tract. In contrast, two bat sarbecovirus S proteins drove cell entry in an ACE2-independent, trypsin-dependent fashion and several ACE2-dependent S proteins could switch to the ACE2-independent entry pathway when exposed to trypsin. Several TMPRSS2-related cellular proteases but not the insertion of a multibasic cleavage site into the S protein allowed for ACE2-independent entry in the absence of trypsin and may support viral spread in the respiratory tract. Finally, the pan-sarbecovirus antibody S2H97 enhanced cell entry driven by two S proteins and this effect was reversed by trypsin while trypsin protected entry driven by a third S protein from neutralization by S2H97. Similarly, plasma from quadruple vaccinated individuals neutralized entry driven by all S proteins studied, and availability of the ACE2-independent, trypsin-dependent pathway reduced neutralization sensitivity. In sum, our study reports a pathway for entry into human cells that is ACE2-independent, can be supported by TMPRSS2-related proteases and may be associated with antibody evasion.

The primary impediment to the recovery of heavy oil lies in its high viscosity, which necessitates a deeper understanding of the molecular mechanisms governing its dynamic behavior for enhanced oil recovery. However, there remains a dearth of understanding regarding the complex molecular composition inherent to heavy oil. In this study, we employed high-resolution mass spectrometry in conjunction with various chemical derivatization and ionization methods to obtain semi-quantitative results of molecular group compositions of 35 heavy oils. The gradient boosting (GB) model has been further used to acquire the feature importance rank (FIR). A feature is an independently observable property of the observed object. Feature importance can measure the contribution of each input feature to the model prediction result, indicate the degree of correlation between the feature and the target, unveil which features are indicative of certain predictions. We have developed a framework for utilizing physical insights into the impact of molecular group compositions on viscosity. The results of machine learning (ML) conducted by GB show that the viscosity of heavy oils is primarily influenced by light components, specifically small molecular hydrocarbons with low condensation degrees, as well as petroleum acids composed of acidic oxygen groups and neutral nitrogen groups. Additionally, large molecular aromatic hydrocarbons and sulfoxides also play significant roles in determine the viscosity.

Background：The crush and the culotte stenting were both reported to be effective for complex bifurcation lesion treatment.However,their comparative performance remains elusive.Methods：A total of 300 patients with coronary bifurcation lesions were randomly assigned to crush （n =150） and culotte （n =150） treatment.The primary endpoint was the occurrence of major adverse cardiac events （MACEs） at 12 months including cardiac death,myocardial infarction,stent thrombosis,and target vessel revascularization.Index lesion restenosis at 12 months was a secondary endpoint.The surface integrals of time-averaged wall shear stress at bifurcation sites were also be quantified.Results：There were no significant differences in MACE rates between the two groups at 12-month follow-up：Crush 6.7%,culotte 5.3% （P =0.48）.The rates of index lesion restenosis were 12.7% versus 6.0% （P =0.047） in the crush and the culotte groups,respectively.At 12-month follow-up,the surface integrals of time-averaged wall shear stress at bifurcation sites in the crush group were significantly lower than the culotte group （[5.01 ± 0.95] × 10^-4 Newton and [6.08 ± 1.16] × 10^-4 Newton,respectively;P =0.003）.Conclusions：Both the crush and the culotte bifurcation stenting techniques showed satisfying clinical and angiographic results at 12-month follow-up.Bifurcation lesions treated with the culotte technique tended to have lower restenosis rates and more favorable flow patterns.

This paper introduces a well-used long range displacement measurement system. The advantages of the system are that the sensor's measurement range can be extended by optical linear scale with a simple structure, and the distance between the measured object and the sensor can be automatically adjusted. The feasibility of the measurement system is tested by simulation experiments, and the accuracy performance is evaluated with respect to both repeatability error and linearity error. The experimental results show that the displacement measurement system reaches a level of accuracy which is sufficient for operational requirements.

Our recent work on the detection of explosives by laser-induced breakdown spectroscopy (LIBS) is reviewed in this paper. We have studied the physical mechanism of laser-induced plasma of an organic explosive, TNT. The LIBS spectra of TNT under single-photon excitation are simulated using MATLAB. The variations of the atomic emission lines intensities of carbon, hydrogen, oxygen, and nitrogen versus the plasma temperature are simulated too. We also investigate the time-resolved LIBS spectra of a common inorganic explosive, black powder, in two kinds of surrounding atmospheres, air and argon, and find that the maximum value of the O atomic emission line SBR of black powder occurs at a gate delay of 596 ns. Another focus of our work is on using chemometic methods such as principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to distinguish the organic explosives from organic materials such as plastics. A PLS-DA model for classification is built. TNT and seven types of plastics are chosen as samples to test the model. The experimental results demonstrate that LIBS coupled with the chemometric techniques has the capacity to discriminate organic explosive from plastics.