The Role of PSR in Zebrafish (Danio rerio) at Early Embryonic Development

During development, the role of the phosphatidylserine receptor (PSR) in the professional removal of apoptotic cells that have died is few understood. Programmed cell death (PCD) began during the shield stage (5.4 hpf), with dead cells being engulfed by a neighboring cell that showed a normal-looking nucleus and the nuclear condensation multi-micronuclei of an apoptotic cell. Recently, in the zebrafish model system, PS receptor played a new role on corpse cellular cleaning for further normal development during early embryonic development, which also correlated with tissues’ or organs’ complete development and organogenesis. In the present, we summary new story that a transcriptional factor, YY1a, in the upstream of PSR is how to regulate PS receptor expression that linked to function of PSR-phagocyte mediated apoptotic cell engulfment during development, especially the development of organs such as the brain and heart. YY1a/PSR-mediated engulfing system may involve in diseases and therapy. This engulfing system may provide new insight into phosphatidylserine receptor how to dynamitic interaction with apoptotic cell during priming programmed cell death

Holographic Operator Product Expansion of Loop Operators in N=4\mathcal{N}=4 SO(N)SO(N) Super Yang-Mills Theory

In this paper, we compute the correlation functions of Wilson(-'t~Hooft) loops with chiral primary operators in $\mathcal{N}=4$ supersymmetric Yang-Mills theory with $SO(N)$ gauge symmetry, which has a holographic dual description of Type IIB superstring theory on the $AdS_{5}\times\mathbf{RP}^{5}$ background. Specifically, we compute the coefficients of the chiral primary operators in the operator product expansion of Wilson loops in the fundamental representation, Wilson-'t Hooft loops in the symmetric representation, Wilson loops in the anti-fundamental representation and the spinor representation. We also compare these results to the $\mathcal{N}=4$ $SU(N)$ super Yang-Mills theory.Comment: 20 pages, no figures

AI-Generated Content (AIGC): A Survey

To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinctions between auxiliary generation and automatic generation within AIGC, providing examples of text generation. The paper also examines the potential integration of AIGC with the Metaverse. Lastly, the article highlights existing issues and suggests some future directions for application.Comment: Preprint. 14 figures, 4 table

The temporal trend of influenza-associated morbidity and the impact of early appearance of antigenic drifted strains in a Southeast Asian country

Globally, influenza infection is a major cause of morbidity and mortality in the elderly, who are suggested to be the major target group for trivalent influenza vaccine (TIV) vaccination by World Health Organization. In spite of an increasing trend in vaccine coverage rates in many countries, the effect of vaccination among the elderly in reducing hospitalization and mortality remains controversial. In this study, we conducted a historical cohort study to evaluate the temporal pattern of influenza-associated morbidity among persons older than 64 years over a decade. The temporal patterns of influenza-associated morbidity rates among the elderly older than 64 years indicated that Taiwan's elderly P&I outpatient visits have been decreasing since the beginning of the 1999-2000 influenza season; however, hospitalization has been increasing despite significant increases in vaccine coverage. The propensity score logistic regression model was implemented to evaluate the source of bias and it was found that the TIV-receiving group had a higher propensity score than the non-receiving group (P<0.0001). In order to investigate the major factors affecting the temporal pattern of influenza-associated morbidity, we then used the propensity score as a summary confounder in a multivariate Poisson regression model based on the trimmed data. Our final models suggested that the factors affected the temporal pattern of morbidity differently. The variables including co-morbidity, vaccination rate, influenza virus type A and B isolation rate were associated with increased outpatient visits and hospitalization (p<0.05). In contrast, variables including high propensity score, increased 1°C in temperature, matching vaccine strains of type A/H1N1 and type B were associated with decreased outpatient visits and hospitalization (p<0.05). Finally, we assessed the impact of early appearance of antigenic-drifted strains and concluded that an excess influenza-associated morbidity substantial trends toward higher P&I hospitalization, but not outpatient visits, during the influenza season with early appearance of antigenic-drifted strains