A Network Celebrity Identification and Evaluation Model Based on Hybrid Trust Relation

Trust-based celebrity user identification is the key to the industry\u27s reputation for electronic word of mouth. However, trust and mistrust are independent and coexistent concepts. In this context, we need to consider the existence of the two kinds of user relations brought about by the impact. This paper analyzes the characteristics of trust and distrust in social networks, and gives formal descriptions of trust networks, untrusted networks, and mixed trust networks. Based on the indicators such as degree distribution, correlation coefficient, and matching coefficient, the structural properties of mixed trust networks are studied. Based on the PageRank algorithm, the HTMM metrics affecting users under the mixed trust network environment are proposed. Finally, the validity of HTMM is verified through a real data set containing trust and distrust. Experimental results show that the results of HTMM\u27s celebrity user identification method still have a low level of trust

Fumarase mediates transcriptional response to nutrient stress

Limited supply of nutrient normally causes cell growth arrest. Our recent study (Nat Cell Biol. (7):833-843) shows that fumarase (FH), a key enzyme responsible for the conversion between fumarate and malate in tricarboxylic acid cycle, is importantly involved in the cellular response to nutrient condition

Continual Task Allocation in Meta-Policy Network via Sparse Prompting

How to train a generalizable meta-policy by continually learning a sequence of tasks? It is a natural human skill yet challenging to achieve by current reinforcement learning: the agent is expected to quickly adapt to new tasks (plasticity) meanwhile retaining the common knowledge from previous tasks (stability). We address it by "Continual Task Allocation via Sparse Prompting (CoTASP)", which learns over-complete dictionaries to produce sparse masks as prompts extracting a sub-network for each task from a meta-policy network. By optimizing the sub-network and prompts alternatively, CoTASP updates the meta-policy via training a task-specific policy. The dictionary is then updated to align the optimized prompts with tasks' embedding, thereby capturing their semantic correlations. Hence, relevant tasks share more neurons in the meta-policy network via similar prompts while cross-task interference causing forgetting is effectively restrained. Given a trained meta-policy with updated dictionaries, new task adaptation reduces to highly efficient sparse prompting and sub-network finetuning. In experiments, CoTASP achieves a promising plasticity-stability trade-off without storing or replaying any past tasks' experiences and outperforms existing continual and multi-task RL methods on all seen tasks, forgetting reduction, and generalization to unseen tasks.Comment: Accepted by ICML 202

Rational design of microRNA-siRNA chimeras for multifunctional target suppression

MicroRNAs (miRNAs) are involved in a variety of human diseases by simultaneously suppressing many gene targets. Thus, the therapeutic value of miRNAs has been intensely studied. However, there are potential limitations with miRNA-based therapeutics such as a relatively moderate impact on gene target regulation and cellular phenotypic control. To address these issues, we proposed to design new chimeric small RNAs (aiRNAs) by incorporating sequences from both miRNAs and siRNAs. These aiRNAs not only inherited functions from natural miRNAs, but also gained new functions of gene knockdown in an siRNA-like fashion. The improved efficacy of multifunctional aiRNAs was demonstrated in our study by design and testing of an aiRNA that inherited the functions of both miR-200a and an AKT1-targeting siRNA for simultaneous suppression of cancer cell motility and proliferation. The general principles of aiRNA design were further validated by engineering new aiRNAs mimicking another miRNA, miR-9. By regulating multiple cellular functions, aiRNAs could be used as an improved tool over miRNAs to target disease-related genes, thus alleviating our dependency on a limited number of miRNAs for the development of RNAi-based therapeutics

INDENTATION COEFFICIENT AND INDENTATION BEHAVIOR OF BAMBOO

Bamboo hardness test standards are not available. The study aimed to develop a new method of testing bamboo indentation hardness. With the V-shaped prismatic head, bamboo rings with different lengths were tested. The V-shaped indentation coefficient (IC) was defined. The results showed that the IC had a good correlation with compression strength. The V-shaped IC increased with the increase in the longitudinal height of the bamboo pole, and the variance analysis showed significant differences in different axial directions of the same bamboo ring. In addition, the correlation between density and IC is good. The V-shaped IC can be applied in bamboo gr

A moving least square immersed boundary method for SPH with thin-walled structures

This paper presents a novel method for smoothed particle hydrodynamics (SPH) with thin-walled structures. Inspired by the direct forcing immersed boundary method, this method employs a moving least square method to guarantee the smoothness of velocity near the structure surface. It simplifies thin-walled structure simulations by eliminating the need for multiple layers of boundary particles, and improves computational accuracy and stability in three-dimensional scenarios. Supportive three-dimensional numerical results are provided, including the impulsively started plate and the flow past a cylinder. Results of the impulsively started test demonstrate that the proposed method obtains smooth velocity and pressure in the, as well as a good match to the references results of the vortex wake development. In addition, results of the flow past cylinder test show that the proposed method avoids mutual interference on both side of the boundary, remains stable for three-dimensional simulations while accurately calculating the forces acting on structure.Comment: 15 pages,11 figure