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The major variant of concerns (VOCs) have shared mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins, mostly on the S1 unit and resulted in higher transmissibility rate and affect viral virulence and clinical outcome. The spike protein mutations and other non-structural protein mutations in the VOCs may lead to escape approved vaccinations in certain extend. We will discuss these VOC mutations and discuss the need for combination therapeutic strategies targeting viral cycle and immune host responses.

Keywords: Lung injury, Renin-angiotensin system, Clinical trial

This paper combines virtual simulation technology to improve the traditional foreign language teaching mode. Aiming at the problems of traditional phase-shift shadow Moiré profiler, a three-step quadrature-phase demodulation technique using unknown phase shift was developed. Then, the paper compares the proposed method with the GS method using MATLAB simulation. It can be seen from the simulation and demodulation results that the two methods obtain similar results, thus proving the equivalence of the two methods.

Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.

Liver fibrosis is a common pathological feature of end stage liver failure, a severe life-threatening disease worldwide. Nonalcoholic fatty liver disease (NAFLD), especially its more severe form with steatohepatitis (NASH), results from obesity, type 2 diabetes and metabolic syndrome and becomes a leading cause of liver fibrosis. Genetic factor, lipid overload/toxicity, oxidative stress and inflammation have all been implicated in the development and progression of NASH. Both innate immune response and adaptive immunity contribute to NASH-associated inflammation. Innate immunity may cause inflammation and subsequently fibrosis via danger-associated molecular patterns. Increasing evidence indicates that T cell-mediated adaptive immunity also provokes inflammation and fibrosis in NASH via cytotoxicity, cytokines and other proinflammatory and profibrotic mediators. Recently, the single-cell transcriptome profiling has revealed that the populations of CD4 + T cells, CD8 + T cells, γδ T cells, and TEMs are expanded in the liver with NASH. The activation of T cells requires antigen presentation from professional antigen-presenting cells (APCs), including macrophages, dendritic cells, and B-cells. However, since hepatocytes express MHCII molecules and costimulators, they may also act as an atypical APC to promote T cell activation. Additionally, the phenotypic switch of hepatocytes to proinflammatory cells in NASH contributes to the development of inflammation. In this review, we focus on T cells and in particular CD4 + T cells and discuss the role of different subsets of CD4 + T cells including Th1, Th2, Th17, Th22, and Treg in NASH-related liver inflammation and fibrosis.

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha.

The effective fulfillment of teachers' responsibilities is crucial to the governance of school bullying. Guided by Grounded Theory and utilizing NVivo11 qualitative analysis software, this paper used 316 texts from 55 cases of school bullying in China to conduct open coding, spindle coding and selective coding, analyzed the behavior of teachers, explored the impetus factors and resistance factors influencing teachers' fulfillment of their responsibilities, and constructed a \"two-factors model\". The reasons why these factors in the model affect school bullying governance responsibility of teachers were analyzed through Street-Level Bureaucracy Theory. On the one hand, teachers are legally obligated to provide fair and equitable educational services to students. The media and the public are important forces in monitoring teachers' fulfillment of their responsibilities. Additionally, leaders from school and the local government such as police department utilize interdependency and gaming relationships with teachers to promote teachers' fulfillment of their responsibilities. Furthermore, the formulation of laws and policies is an important basis for supervising teachers' behavior. These factors collectively promote teachers' fulfillment of school bullying governance responsibilities. On the other hand, teachers have discretion and are difficult to supervise and hold accountable. The interests of different governance subjects may diverge or conflict. Moreover, the complexity and diversity of school bullying exacerbate governance challenges. Furthermore, systems such as laws and policies often fall short in restraining undesirable behavior. These reasons present obstacles to teachers' fulfilling their responsibilities of school bullying governance. In order to strengthen the impetus factors for teachers to fulfill their responsibilities and mitigate resistance, China needs to optimize the governance path. This involves improving the relevant systems, enhancing publicity, providing education and training for teachers, strengthening the supervision of the local government, the media and the public on teachers, and improving the overall governance environment for school bullying. These measures will encourage teachers to hold themselves to higher standards and effectively fulfill their responsibilities in the governance process of school bullying.

Pyroelectric energy harvesting has received increasing attention due to its ability to convert low-grade waste heat into electricity. However, the low output energy density driven by low-grade temperature limits its practical applications. Here, we show a high-performance hybrid BNT-BZT- x GaN thermal energy harvesting system with environmentally friendly lead-free BNT-BZT pyroelectric matrix and high thermal conductivity GaN as dopant. The theoretical analysis of BNT-BZT and BNT-BZT- x GaN with x  = 0.1 wt% suggests that the introduction of GaN facilitates the resonance vibration between Ga and Ti, O atoms, which not only contributes to the enhancement of the lattice heat conduction, but also improves the vibration of TiO 6  octahedra, resulting in simultaneous improvement of thermal conductivity and pyroelectric coefficient. Therefore, a thermoelectric coupling enhanced energy harvesting density of 80 μJ cm −3 has been achieved in BNT-BZT- x GaN ceramics with x  = 0.1 wt% driven by a temperature variation of 2 o C, at the optical load resistance of 600 MΩ. Pyroelectric energy harvesting has received increasing attention due to its ability to convert low-grade waste heat into electricity. Here, authors report an enhanced thermoelectric coupling BNT-BZT-xGaN pyroelectric energy harvester by facilitating resonance vibration between Ga and Ti, O atoms.

More men than women have died from COVID-19. Genes encoded on X chromosomes, and sex hormones may explain the decreased fatality of COVID-19 in women. The angiotensin-converting enzyme 2 gene is located on X chromosomes. Men, with a single X chromosome, may lack the alternative mechanism for cellular protection after exposure to SARS-CoV-2. Some Toll-like receptors encoded on the X chromosomes can sense SARS-CoV-2 nucleic acids, leading to a stronger innate immunity response in women. Both estrogen and estrogen receptor-α contribute to T cell activation. Interventional approaches including estrogen-related compounds and androgen receptor antagonists may be considered in patients with COVID-19.

Piezoelectricity in native bones has been well recognized as the key factor in bone regeneration. Thus, bio-piezoelectric materials have gained substantial attention in repairing damaged bone by mimicking the tissue’s electrical microenvironment (EM). However, traditional manufacturing strategies still encounter limitations in creating personalized bio-piezoelectric scaffolds, hindering their clinical applications. Three-dimensional (3D)/four-dimensional (4D) printing technology based on the principle of layer-by-layer forming and stacking of discrete materials has demonstrated outstanding advantages in fabricating bio-piezoelectric scaffolds in a more complex-shaped structure. Notably, 4D printing functionality-shifting bio-piezoelectric scaffolds can provide a time-dependent programmable tissue EM in response to external stimuli for bone regeneration. In this review, we first summarize the physicochemical properties of commonly used bio-piezoelectric materials (including polymers, ceramics, and their composites) and representative biological findings for bone regeneration. Then, we discuss the latest research advances in the 3D printing of bio-piezoelectric scaffolds in terms of feedstock selection, printing process, induction strategies, and potential applications. Besides, some related challenges such as feedstock scalability, printing resolution, stress-to-polarization conversion efficiency, and non-invasive induction ability after implantation have been put forward. Finally, we highlight the potential of shape/property/functionality-shifting smart 4D bio-piezoelectric scaffolds in bone tissue engineering (BTE). Taken together, this review emphasizes the appealing utility of 3D/4D printed biological piezoelectric scaffolds as next-generation BTE implants. Significant findings in bio-piezoelectric material-guided cell behavior and bone tissue engineering are presented. Benefits and limitations associated with the latest 3D printing strategies for bio-piezoelectric materials are discussed. Challenges and future perspectives on 4D printing smart bio-piezoelectric scaffolds for potential biological applications are outlined.