Egg Yolk Antibody for Passive Immunization: Status, Challenges, and Prospects.

The immunoglobulin Y (IgY) derived from hyperimmune egg yolk is a promising passive immune agent to combat microbial infections in humans and livestock. Numerous studies have been performed to develop specific egg yolk IgY for pathogen control, but with limited success. To date, the efficacy of commercial IgY products, which are all delivered through an oral route, has not been approved or endorsed by any regulatory authorities. Several challenging issues of the IgY-based passive immunization, which were not fully recognized and holistically discussed in previous publications, have impeded the development of effective egg yolk IgY products for humans and animals. This review summarizes major challenges of this technology, including in vivo stability, purification, heterologous immunogenicity, and repertoire diversity of egg yolk IgY. To tackle these challenges, potential solutions, such as encapsulation technologies to stabilize IgY, are discussed. Exploration of this technology to combat the COVID-19 pandemic is also updated in this review.

RPocket: an intuitive database of RNA pocket topology information with RNA-ligand data resources.

BACKGROUND: RNA regulates a variety of biological functions by interacting with other molecules. The ligand often binds in the RNA pocket to trigger structural changes or functions. Thus, it is essential to explore and visualize the RNA pocket to elucidate the structural and recognition mechanism for the RNA-ligand complex formation. RESULTS: In this work, we developed one user-friendly bioinformatics tool, RPocket. This database provides geometrical size, centroid, shape, secondary structure element for RNA pocket, RNA-ligand interaction information, and functional sites. We extracted 240 RNA pockets from 94 non-redundant RNA-ligand complex structures. We developed RPDescriptor to calculate the pocket geometrical property quantitatively. The geometrical information was then subjected to RNA-ligand binding analysis by incorporating the sequence, secondary structure, and geometrical combinations. This new approach takes advantage of both the atom-level precision of the structure and the nucleotide-level tertiary interactions. The results show that the higher-level topological pattern indeed improves the tertiary structure prediction. We also proposed a potential mechanism for RNA-ligand complex formation. The electrostatic interactions are responsible for long-range recognition, while the Van der Waals and hydrophobic contacts for short-range binding and optimization. These interaction pairs can be considered as distance constraints to guide complex structural modeling and drug design. CONCLUSION: RPocket database would facilitate RNA-ligand engineering to regulate the complex formation for biological or medical applications. RPocket is available at http://zhaoserver.com.cn/RPocket/RPocket.html .

Risk Spillover Network Structure Learning for Correlated Financial Assets: A Directed Acyclic Graph Approach

Using cross-asset return data in global financial markets, we propose a novel empirical framework to identify the causal structure of the asset risk spillover network. The joint return distribution of the global financial system can be characterized using a directed acyclic graph approach. However, since assets tend to be highly correlated during market turbulence, when adopting a nodewise penalized regression approach for neighborhood estimation, parameter estimates will receive large standard errors, and edges cannot be reliably estimated. In this work, we propose a two-stage approach for directed acyclic graph skeleton estimation for highly correlated variables. In the first stage, a variable screening ensemble is incorporated into the sparse partial least squares regression method to both reduce the size of the active variables set and impose an adaptive penalization on the weight vectors. In the second stage, a modified PC algorithm based on Gram-Schmidt orthogonalization is applied to remove the false positive edges. Simulation studies are conducted to demonstrate the effectiveness of the proposed method. Finally, we apply our method to analyze the asset risk spillover channels for international financial assets during the COVID-19 pandemic.