Pinyin input experiments in early Chinese literacy instruction in China: Implications for Chinese curricular and pedagogic reform in Singapore

Abstract In this paper we intend to provide a review of pinyin input experiment studies in Chinese literacy instruction in China and to inform the current reform of Chinese education in Singapore. We first provide a brief account of some major pinyin-based methods for inputting characters into the computer. This is important because the skill of inputting characters into the computer constitutes beginning electronic literacy and it is essential for the pinyin input experiments in Chinese literacy instruction. Secondly we review some studies on the use of pinyin input methods in Chinese literacy instruction. In conclusion, we discuss the major points of the pinyin input experiment in relation to the Singapore context and highlight implications for the current reform of Chinese literacy education envisioned by the Chinese Curriculum and Pedagogy Review Committee in Singapore

Genomic Characterization Provides New Insights Into the Biosynthesis of the Secondary Metabolite Huperzine a in the Endophyte Colletotrichum gloeosporioides Cg01

A reliable source of Huperzine A (HupA) meets an urgent need due to its wide use in Alzheimer's disease treatment. In this study, we sequenced and characterized the whole genomes of two HupA-producing endophytes, Penicillium polonicum hy4 and Colletotrichum gloeosporioides Cg01, to clarify the mechanism of HupA biosynthesis. The whole genomes of hy4 and Cg01 were 33.92 and 55.77 Mb, respectively. We compared the differentially expressed genes (DEGs) between the induced group (with added extracts of Huperzia serrata) and a control group. We focused on DEGs with similar expression patterns in hy4 and Cg01. The DEGs identified in GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were primarily located in carbon and nitrogen metabolism and nucleolus, ribosome, and rRNA processing. Furthermore, we analyzed the gene expression for HupA biosynthesis genes proposed in plants, which include lysine decarboxylase (LDC), copper amine oxidase (CAO), polyketides synthases (PKS), etc. Two LDCs, one CAO, and three PKSs in Cg01 were selected as prime candidates for further validation. We found that single candidate biosynthesis-gene knock-out did not influence the HupA production, while both LDC gene knock-out led to increased HupA production. These results reveal that HupA biosynthesis in endophytes might differ from that proposed in plants, and imply that the HupA-biosynthesis genes in endophytic fungi might co-evolve with the plant machinery rather than being acquired through horizontal gene transfer (HGT). Moreover, we analyzed the function of the differentially expressed epigenetic modification genes. HupA production of the histone acetyltransferase (HAT) deletion mutant ΔCgSAS-2 was not changed, while that of the histone methyltransferase (HMT) and histone deacetylase (HDAC) deletion mutants ΔCgClr4, ΔCgClr3, and ΔCgSir2-6 was reduced. Recovery of HupA-biosynthetic ability can be achieved by retro-complementation, demonstrating that HMT and HDACs associated with histone modification are involved in the regulation of HupA biosynthesis in endophytic fungi. This is the first report on epigenetic modification in high value secondary metabolite- producing endophytes. These findings shed new light on HupA biosynthesis and regulation in HupA-producing endophytes and are crucial for industrial production of HupA from fungi

Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model

Many crucial biological processes rely on networks of protein-protein interactions. Predicting the effect of amino acid mutations on protein-protein binding is vital in protein engineering and therapeutic discovery. However, the scarcity of annotated experimental data on binding energy poses a significant challenge for developing computational approaches, particularly deep learning-based methods. In this work, we propose SidechainDiff, a representation learning-based approach that leverages unlabelled experimental protein structures. SidechainDiff utilizes a Riemannian diffusion model to learn the generative process of side-chain conformations and can also give the structural context representations of mutations on the protein-protein interface. Leveraging the learned representations, we achieve state-of-the-art performance in predicting the mutational effects on protein-protein binding. Furthermore, SidechainDiff is the first diffusion-based generative model for side-chains, distinguishing it from prior efforts that have predominantly focused on generating protein backbone structures

Research on Crisis Management Mechanism of College Student Group Events

University is an important part of society, also a collection of social trends of thoughts and contradictions. College students are the cornerstone of harmony and the stability of colleges and universities. At the same time, they are the most active, the most sensitive, the most moving, and the most unstable special group in the university. In recent years, college students group incidents have occurred constantly, and this incidents in unpredictable ways or inadvertently causes break out. Effectively preventing and dealing with this kind of unexpected events, which has became a priority among priorities with relevant parties. This paper aim at the existence crisis of college students group events, which are based on empirical analysis and constantly improve the mechanism of crisis management

Assessment of heterotrophic growth supported by soluble microbial products in anammox biofilm using multidimensional modeling

Anaerobic ammonium oxidation (anammox) is known to autotrophically convert ammonium to dinitrogen gas with nitrite as the electron acceptor, but little is known about their released microbial products and how these are relative to heterotrophic growth in anammox system. In this work, we applied a mathematical model to assess the heterotrophic growth supported by three key microbial products produced by bacteria in anammox biofilm (utilization associated products (UAP), biomass associated products (BAP), and decay released substrate). Both One-dimensional and two-dimensional numerical biofilm models were developed to describe the development of anammox biofilm as a function of the multiple bacteria-substrate interactions. Model simulations show that UAP of anammox is the main organic carbon source for heterotrophs. Heterotrophs are mainly dominant at the surface of the anammox biofilm with small fraction inside the biofilm. 1-D model is sufficient to describe the main substrate concentrations/fluxes within the anammox biofilm, while the 2-D model can give a more detailed biomass distribution. The heterotrophic growth on UAP is mainly present at the outside of anammox biofilm, their growth on BAP (HetB) are present throughout the biofilm, while the growth on decay released substrate (HetD) is mainly located in the inner layers of the biofilm

Improvement and Mechanism of Ganoderma lucidum Polysaccharides and Its Flora Metabolites on Insulin Resistance in HepG2 Cells

Objective: To investigate the effects of Ganoderma lucidum polysaccharides and its flora metabolites on the insulin resistance status of HepG2 cells and its mechanisms. Methods: Insulin resistant HepG2 (IR-HepG2) model was established with the combination of insulin (10−3 µmol/L) and dexamethasone (10 µmol/L). The cytotoxicity of Ganoderma lucidum polysaccharides (GLP) and Ganoderma lucidum polysaccharide flora metabolite (GLP-F) was evaluated using the CCK-8 method. The effects of GLP and GLP-F on glucose consumption and glycogen synthesis in IR-HepG2 cells were evaluated using the glucose kit and glycogen kit methods. Western blot assay was used to detect the effects of GLP and GLP-F on the phosphorylation or expression of IRS-1, AKT, GSK-3β, GLUT2, and PEPCK, key proteins in the insulin signaling cascade in IR-HepG2 cells. Results: Both GLP and GLP-F significantly increased glucose uptake and glycogen synthesis in IR-HepG2 cells (P<0.05). GLP-F promoted glucose consumption in IR-HepG2 cells significantly more than GLP (P<0.05). Western blot experiments showed that both GLP and GLP-F promoted IR-HepG2 cells IRS-1, P-AKT, P-GSK-3β, GLUT2 protein expression and inhibited PEPCK protein expression, and the inhibitory utility of GLP-F on PEPCK was significantly higher than that of GLP (P<0.05). Conclusions: GLP and its metabolism by intestinal flora-mediated production of GLP-F have the same biological effect of alleviating hepatic insulin resistance, and GLP-F has a more significant effect than GLP in promoting glucose consumption in IR-HepG2 cells and inhibiting their gluconeogenic rate-limiting enzyme activity

GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts

In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts. These models have been trained on an extensive dataset that encompasses both simplified and traditional Chinese characters, allowing them to effectively handle various natural language processing tasks related to ancient books, including but not limited to automatic sentence segmentation, punctuation, word segmentation, part-of-speech tagging, entity recognition, and automatic translation. Notably, these models have exhibited exceptional performance across a range of validation tasks using publicly available datasets. Our research findings highlight the efficacy of employing self-supervised methods to further train the models using classical text corpora, thus enhancing their capability to tackle downstream tasks. Moreover, it is worth emphasizing that the choice of font, the scale of the corpus, and the initial model selection all exert significant influence over the ultimate experimental outcomes. To cater to the diverse text processing preferences of researchers in digital humanities and linguistics, we have developed three distinct categories comprising a total of nine model variations. We believe that by sharing these foundational language models specialized in the domain of ancient texts, we can facilitate the intelligent processing and scholarly exploration of ancient literary works and, consequently, contribute to the global dissemination of China's rich and esteemed traditional culture in this new era.Comment: 22pages,0 figur