Research on the path of improving college teachers’ teaching ability in the information 2.0 era

education is the cornerstone of social progress, and the reform of education also needs to be in line with the times. With the advent of the information 2.0 era, college teachers are facing new challenges. Teachers need to make full use of information technology to improve teaching quality in practice. However, looking back at the education and teaching work in Colleges and Universities under the background of informatization, it is not difficult to find that there are many problems that affect the development process of the modernization of higher education. Based on this, this paper explores the path to improve the teaching ability of College Teachers in the information 2.0 era, hoping to provide a valuable reference for promoting the construction of college teachers

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

Semi-Centralised Multi-Agent Reinforcement Learning with Policy-Embedded Training

Centralised training (CT) is the basis for many popular multi-agent reinforcement learning (MARL) methods because it allows agents to quickly learn high-performing policies. However, CT relies on agents learning from one-off observations of other agents' actions at a given state. Because MARL agents explore and update their policies during training, these observations often provide poor predictions about other agents' behaviour and the expected return for a given action. CT methods therefore suffer from high variance and error-prone estimates, harming learning. CT methods also suffer from explosive growth in complexity due to the reliance on global observations, unless strong factorisation restrictions are imposed (e.g., monotonic reward functions for QMIX). We address these challenges with a new semi-centralised MARL framework that performs policy-embedded training and decentralised execution. Our method, policy embedded reinforcement learning algorithm (PERLA), is an enhancement tool for Actor-Critic MARL algorithms that leverages a novel parameter sharing protocol and policy embedding method to maintain estimates that account for other agents' behaviour. Our theory proves PERLA dramatically reduces the variance in value estimates. Unlike various CT methods, PERLA, which seamlessly adopts MARL algorithms, scales easily with the number of agents without the need for restrictive factorisation assumptions. We demonstrate PERLA's superior empirical performance and efficient scaling in benchmark environments including StarCraft Micromanagement II and Multi-agent Mujoc

PPAR-α Agonist Fenofibrate Upregulates Tetrahydrobiopterin Level through Increasing the Expression of Guanosine 5′-Triphosphate Cyclohydrolase-I in Human Umbilical Vein Endothelial Cells

Tetrahydrobiopterin (BH4) is an essential cofactor for endothelial nitric oxide (NO) synthase. Guanosine 5′-triphosphate cyclohydrolase-I (GTPCH-I) is a key limiting enzyme for BH4 synthesis. In the present in vitro study, we investigated whether peroxisome proliferator-activated receptor α (PPAR-α) agonist fenofibrate could recouple eNOS by reversing low-expression of intracellular BH4 in endothelial cells and discussed the potential mechanisms. After human umbilical vein endothelial cells (HUVECs) were treated with lipopolysaccharide (LPS) for 24 hours, the levels of cellular eNOS, BH4 and cell supernatant NO were significantly reduced compared to control group. And the fluorescence intensity of intracellular ROS was significantly increased. But pretreated with fenofibrate (10 umol/L) for 2 hours before cells were induced by LPS, the levels of eNOS, NO, and BH4 were significantly raised compared to LPS treatment alone. ROS production was markedly reduced in fenofibrate group than LPS group. In addition, our results showed that the level of intracellular GTPCH-I detected by western blot was increased in a concentration-dependent manner after being treated with fenofibrate. These results suggested that fenofibrate might help protect endothelial function and against atherosclerosis by increasing level of BH4 and decreasing production of ROS through upregulating the level of intracellular GTPCH-I

From Few to More: Large-scale Dynamic Multiagent Curriculum Learning

A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explosion of state-action space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. We propose three transfer mechanisms across curricula to accelerate the learning process. Moreover, due to the fact that the state dimension varies across curricula,, and existing network structures cannot be applied in such a transfer setting since their network input sizes are fixed. Therefore, we design a novel network structure called Dynamic Agent-number Network (DyAN) to handle the dynamic size of the network input. Experimental results show that DyMA-CL using DyAN greatly improves the performance of large-scale multiagent learning compared with state-of-the-art deep reinforcement learning approaches. We also investigate the influence of three transfer mechanisms across curricula through extensive simulations.Comment: Accepted by AAAI202

Synthetic engineering of a new biocatalyst encapsulating [NiFe]-hydrogenases for enhanced hydrogen production

Hydrogenases are microbial metalloenzymes capable of catalyzing the reversible interconversion between molecular hydrogen and protons with high efficiency, and have great potential in the development of new electrocatalysts for renewable...</jats:p

Development, validation, and evaluation of a risk assessment tool for personalized screening of gastric cancer in Chinese populations

Background Effective risk prediction models are lacking for personalized endoscopic screening of gastric cancer (GC). We aimed to develop, validate, and evaluate a questionnaire-based GC risk assessment tool for risk prediction and stratification in the Chinese population. Methods In this three-stage multicenter study, we first selected eligible variables by Cox regression models and constructed a GC risk score (GCRS) based on regression coefficients in 416,343 subjects (aged 40–75 years) from the China Kadoorie Biobank (CKB, development cohort). In the same age range, we validated the GCRS effectiveness in 13,982 subjects from another independent Changzhou cohort (validation cohort) as well as in 5348 subjects from an endoscopy screening program in Yangzhou. Finally, we categorized participants into low (bottom 20%), intermediate (20–80%), and high risk (top 20%) groups by the GCRS distribution in the development cohort. Results The GCRS using 11 questionnaire-based variables demonstrated a Harrell’s C-index of 0.754 (95% CI, 0.745–0.762) and 0.736 (95% CI, 0.710–0.761) in the two cohorts, respectively. In the validation cohort, the 10-year risk was 0.34%, 1.05%, and 4.32% for individuals with a low (≤ 13.6), intermediate (13.7~30.6), and high (≥ 30.7) GCRS, respectively. In the endoscopic screening program, the detection rate of GC varied from 0.00% in low-GCRS individuals, 0.27% with intermediate GCRS, to 2.59% with high GCRS. A proportion of 81.6% of all GC cases was identified from the high-GCRS group, which represented 28.9% of all the screened participants. Conclusions The GCRS can be an effective risk assessment tool for tailored endoscopic screening of GC in China. Risk Evaluation for Stomach Cancer by Yourself (RESCUE), an online tool was developed to aid the use of GCRS

Reprogramming bacterial protein organelles as a nanoreactor for hydrogen production

Compartmentalization is a ubiquitous building principle in cells, which permits segregation of biological elements and reactions. The carboxysome is a specialized bacterial organelle that encapsulates enzymes into a virus-like protein shell and plays essential roles in photosynthetic carbon fixation. The naturally designed architecture, semi-permeability, and catalytic improvement of carboxysomes have inspired rational design and engineering of new nanomaterials to incorporate desired enzymes into the protein shell for enhanced catalytic performance. Here, we build large, intact carboxysome shells (over 90 nm in diameter) in the industrial microorganism Escherichia coli by expressing a set of carboxysome protein-encoding genes. We develop strategies for enzyme activation, shell self-assembly, and cargo encapsulation to construct a robust nanoreactor that incorporates catalytically active [FeFe]-hydrogenases and functional partners within the empty shell for the production of hydrogen. We show that shell encapsulation and the internal microenvironment of the new catalyst facilitate hydrogen production of the encapsulated oxygen-sensitive hydrogenases. The study provides insights into the assembly and formation of carboxysomes and paves the way for engineering carboxysome shell-based nanoreactors to recruit specific enzymes for diverse catalytic reactions