Advances in genetic variation in metabolism-related fatty liver disease

Metabolism-related fatty liver disease (MAFLD) is the most common form of chronic liver disease in the world. Its pathogenesis is influenced by both environmental and genetic factors. With the upgrading of gene screening methods and the development of human genome project, whole genome scanning has been widely used to screen genes related to MAFLD, and more and more genetic variation factors related to MAFLD susceptibility have been discovered. There are genetic variants that are highly correlated with the occurrence and development of MAFLD, and there are genetic variants that are protective of MAFLD. These genetic variants affect the development of MAFLD by influencing lipid metabolism and insulin resistance. Therefore, in-depth analysis of different mechanisms of genetic variation and targeting of specific genetic variation genes may provide a new idea for the early prediction and diagnosis of diseases and individualized precision therapy, which may be a promising strategy for the treatment of MAFLD

De Novo

The advent of cellular reprogramming technology has revolutionized biomedical research. De novo human cardiac myocytes can now be obtained from direct reprogramming of somatic cells (such as fibroblasts), from induced pluripotent stem cells (iPSCs, which are reprogrammed from somatic cells), and from human embryonic stem cells (hESCs). Such de novo human cardiac myocytes hold great promise for in vitro disease modeling and drug screening and in vivo cell therapy of heart disease. Here, we review the technique advancements for generating de novo human cardiac myocytes. We also discuss several challenges for the use of such cells in research and regenerative medicine, such as the immature phenotype and heterogeneity of de novo cardiac myocytes obtained with existing protocols. We focus on the recent advancements in addressing such challenges

NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts

Large language models (LLMs) have manifested strong ability to generate codes for productive activities. However, current benchmarks for code synthesis, such as HumanEval, MBPP, and DS-1000, are predominantly oriented towards introductory tasks on algorithm and data science, insufficiently satisfying challenging requirements prevalent in real-world coding. To fill this gap, we propose NaturalCodeBench (NCB), a challenging code benchmark designed to mirror the complexity and variety of scenarios in real coding tasks. NCB comprises 402 high-quality problems in Python and Java, meticulously selected from natural user queries from online coding services, covering 6 different domains. Noting the extraordinary difficulty in creating testing cases for real-world queries, we also introduce a semi-automated pipeline to enhance the efficiency of test case construction. Comparing with manual solutions, it achieves an efficiency increase of more than 4 times. Our systematic experiments on 39 LLMs find that performance gaps on NCB between models with close HumanEval scores could still be significant, indicating a lack of focus on practical code synthesis scenarios or over-specified optimization on HumanEval. On the other hand, even the best-performing GPT-4 is still far from satisfying on NCB. The evaluation toolkit and development set are available at https://github.com/THUDM/NaturalCodeBench

Simulation code

This file is the simulation code for an evolutionary game models.</p

Research on the "multi-agent co-governance" system of unfair competition on internet platforms: Based on the perspective of evolutionary game.

Unfair competition on internet platforms (UCIP) has become a critical issue restricting the platform economy's healthy development. This paper applies evolutionary game theory to study how to utilize multiple subjects' synergy to supervise UCIP effectively. First, the "multi-agent co-governance" mode of UCIP is constructed based on the traditional "unitary supervision" mode. Second, the government and internet platform evolutionary game models are built under two supervision modes. Finally, MATLAB is used to simulate and analyze the evolutionary stage and parameter sensitivity. In addition, we match the model's evolutionary stage with China's supervisory process. The results show that (1) the Chinese government's supervision of UCIP is in the transitional stage from "campaign-style" to "normalization." (2) Moderate government supervision intensity is essential to guide the game system to evolve toward the ideal state. If the supervision intensity is too high, it will inhibit the enthusiasm for supervision. If the supervision intensity is too low, it cannot form an effective deterrent to the internet platforms. (3) When the participation of industry associations and platform users is low, it can only slow down the evolutionary speed of the game system's convergence to the unfavorable state. Nevertheless, it cannot reverse the evolutionary result. (4) Maintaining the participation level of industry associations and platform users above a specific threshold value while increasing punishment intensity will promote the transition of government supervision from the "campaign-style" to the "normalization" stage. This paper provides ideas and references for the Chinese government to design a supervision mechanism for UCIP

An evolutionary game-theoretic analysis of the "multi-agent co-governance" system of unfair competition on internet platforms.

The increasingly prominent issue of unfair competition on Internet platforms (IPUC) severely restricts the healthy and sustainable development of the platform economy. Based on the IPUC "multi-agent co-governance" scenario, this paper introduces stochastic disturbances and continuous strategy set to improve the classical binary deterministic evolutionary game system. The results show that after considering stochastic disturbances, the positive state corresponding to the equilibrium point (1,1) is no longer stable, and the required parameter conditions are more stringent. The IPUC "multi-agent co-governance" system under stochastic disturbances exhibits specific vulnerability. In the continuous strategy set evolutionary game system, government departments and Internet platforms can flexibly make optimal decisions based on maximizing expected returns, and strategy selection has better elasticity. Regardless of the evolutionary game scenario, maintaining the participation level of NGOs and the public above a certain threshold while increasing the penalty intensity is conducive to the evolution of the game system toward the positive state. The analysis process and conclusions provide insights and guidance for the governments to design the IPUC regulatory system and frameworks

The strategy interaction payment matrix of the " unitary supervision " mode.

The strategy interaction payment matrix of the " unitary supervision " mode.</p

The Chinese government’s policies related to platform economy after 2018.

The Chinese government’s policies related to platform economy after 2018.</p

The strategy interaction payment matrix of the "multi-agent co-governance" mode.

The strategy interaction payment matrix of the "multi-agent co-governance" mode.</p

Simulation diagram of dynamic evolution of game system (1,1) stable point.

(a) The “unitary supervision” mode. (b) The “multi-agent co-governance” mode.</p