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Dwarf bamboo (Fargesia denudata) is a crucial food source for the giant pandas. With its shallow root system and rapid growth, dwarf bamboo is highly sensitive to drought stress and nitrogen deposition, both major concerns of global climate change affecting plant growth and rhizosphere environments. However, few reports address the response mechanisms of the dwarf bamboo rhizosphere environment to these two factors. Therefore, this study investigated the effects of drought stress and nitrogen deposition on the physicochemical properties and microbial community composition of the arrow bamboo rhizosphere soil, using metagenomic sequencing to analyze functional genes involved in carbon and nitrogen cycles. Both drought stress and nitrogen deposition significantly altered the soil nutrient content, but their combination had no significant impact on these indicators. Nitrogen deposition increased the relative abundance of the microbial functional gene nrfA, while decreasing the abundances of nirK, nosZ, norB, and nifH. Drought stress inhibited the functional genes of key microbial enzymes involved in starch and sucrose metabolism, but promoted those involved in galactose metabolism, inositol phosphate metabolism, and hemicellulose degradation. NO3−-N showed the highest correlation with N-cycling functional genes (p < 0.01). Total C and total N had the greatest impact on the relative abundance of key enzyme functional genes involved in carbon degradation. This research provides theoretical and technical references for the sustainable management and conservation of dwarf bamboo forests in giant panda habitats under global climate change. [ABSTRACT FROM AUTHOR]

Primula medogensis W.B Ju, B. Xu & X.F. Gao 2023, a new species categorized under P. sect. Cordifoliae, was officially described in 2023. Given its recent classification, the genetic resources for this species are currently very limited. Here, we sequenced and assembled the first complete chloroplast genome of P. medogensis using Illumina sequencing technology. The complete chloroplast genome of P. medogensis is 151,486 bp in length, exhibiting a typical quadripartite structure. It consists of a large single-copy region (LSC; 83,407 bp) and a small single-copy region (SSC;17675 bp), separated by a pair of inverted repeat regions (IRs; 25202 bp). A total of 131 genes were annotated, including 86 protein-coding, 37 tRNA, and eight rRNA genes. The overall GC content was 37.1%. Phylogenetic analysis of 59 Primula species revealed a close relationship between P. medogensis and P. calliantha subsp. bryophila. [ABSTRACT FROM AUTHOR]

Background and Aims: Hepatocellular carcinoma (HCC) is a malignant tumour associated with high morbidity and mortality rates worldwide. Recently, TRPM8 was reported to play an important role in tumour progression. However, the precise role of TRPM8 in HCC remains unclear. In this study, we explored the expression levels, molecular functions and underlying mechanisms of TRPM8 in HCC. Methods: Tissue samples were used to analyse the expression of TRPM8 to assess its diagnostic value for prognosis. Cell Counting Kit‐8, EdU and colony formation assays were performed to evaluate the effects of TRPM8 on cell proliferation, whereas the Transwell assay was used to assess cell migration and invasion. The role of TRPM8 in vivo was evaluated using a mouse subcutaneous xenograft tumour model. We performed PPI network analyses to understand the possible mechanisms of TRPM8 action. Results: TRPM8 expression was decreased in HCC tissues and was correlated with histological grade and poor patient prognosis. Functionally, TRPM8 repressed the proliferation and metastasis of HCC cells both in vitro and in vivo by modulating the RTP3/STAT3 signalling pathway. Conclusion: Our findings underscore the critical role of the TRPM8‐RTP3‐STAT3 axis in maintaining the malignant progression of HCC. Moreover, our study demonstrates that AD80 is involved in anti‐tumour processes by upregulating the expression of TRPM8. [ABSTRACT FROM AUTHOR]

The impact of green technology innovation and the digital economy on the sustainability of the green economy is increasing. To delve deeper into this subject, this paper utilizes fixed- effect models and threshold effect models. It examines data from 34 provincial administrative regions of China. The aim is to uncover the patterns of influence the green technology innovation and the digital economy have on the sustainability of the green economy. The research findings are as follows: (1) The green technology innovation, digital economy, and their interaction contribute to promoting the high-quality sustainability of the green economy. The sustainability of the green economy relies on the support of green technology innovation and the digital economy. By optimizing the capabilities of green technology innovation and the level of digital economy, managers could enhance the high-quality sustainability of the green economy. (2) The digital economy exhibits a dual threshold effect in driving the sustainability of the green economy through green technology innovation. When the digital economy surpasses the first threshold, the influence of green technology innovation on the green economy experiences a notable increase. However, once the digital economy surpasses the second threshold, the impact of green technology innovation on the green economy begins to diminish significantly. (3) There are notable regional variations in the impact of green technology innovation and digital economy on the sustainability of the green economy across different regions of China. Considering these findings, it is vital for stakeholders in China to implement customized measures. These measures should aim to actively promote the sustainability of China's green economy. The relevant stakeholders include businesses and the government. [ABSTRACT FROM AUTHOR]

Mock‐Lie algebras, a unique class of commutative algebras that satisfy the Jacobi identity, are gaining attention for their potential applications in various mathematical contexts. This study presents a matrix representation of the centroid for Mock‐Lie algebras in dimensions up to four, offering insights into their structure and interactions. By applying the centroid concept and utilizing matrix operations, we explore the relationships between matrix elements, leading to a better understanding of these algebras. Our findings aim to contribute to the growing interest in Mock‐Lie algebras and their role within the broader algebraic landscape. [ABSTRACT FROM AUTHOR]

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The new digital infrastructure is the cornerstone of the digital transformation of the regional innovation ecosystem and the key engine driving green, low‐carbon, and intelligent development in the era of the digital economy. The findings demonstrate that substantive green innovation is significantly positively impacted by the new digital infrastructure, and its effect shows a "Central > Eastern > Western." New digital infrastructure construction can indirectly enhance substantive green innovation by improving the level of digital finance, entrepreneurial activity, and informatization index. Based on the characteristics of regional heterogeneity, new digital infrastructure construction has different threshold effects on substantive green innovation. [ABSTRACT FROM AUTHOR]

Anoikis is a distinct type of programmed cell death and a unique mechanism for tumor progress. However, its exact function in gastric cancer (GC) remains unknown. This study aims to investigate the function of anoikis-related lncRNA (ar-lncRNA) in the prognosis of GC and its immunological infiltration. The ar-lncRNAs were derived from RNA sequencing data and associated clinical information obtained from The Cancer Genome Atlas. Pearson correlation analysis, differential screening, LASSO and Cox regression were utilized to identify the typical ar-lncRNAs with prognostic significance, and the corresponding risk model was constructed, respectively. Comprehensive methods were employed to assess the clinical characteristics of the prediction model, ensuring the accuracy of the prediction results. Further analysis was conducted on the relationship between immune microenvironment and risk features, and sensitivity predictions were made about anticancer medicines. A risk model was built according to seven selected ar-lncRNAs. The model was validated and the calibration plots were highly consistent in validating nomogram predictions. Further analyses revealed the great accuracy of the model and its ability to serve as a stand-alone GC prognostic factor. We subsequently disclosed that high-risk groups display significant enrichment in pathways related to tumors and the immune system. Additionally, in tumor immunoassays, notable variations in immune infiltrates and checkpoints were noted between different risk groups. This study proposes, for the first time, that prognostic signatures of ar-lncRNA can be established in GC. These signatures accurately predict the prognosis of GC and offer potential biomarkers, suggesting new avenues for basic research, prognosis prediction and personalized diagnosis and treatment of GC. [ABSTRACT FROM AUTHOR]

Studies have been conducted on university students' continuous intention to learn online from the perspectives of learning motivation and capability, perceptions or attitudes, and online learning experiences. However, few have examined how the above factors will relate to each other and contribute to students' online learning intention. This research explored 94 university students' online learning attitudes and experiences in a blended course. The researchers investigated the changes in the participants' attitudes toward online learning and the relationships between their self-regulated learning capability, online interactions, attitudes, and online learning intention. These students participated in a pre- and post-survey at the beginning and end of the course. They also completed six weekly reports commenting on their learning activities of the week. At the end of the course, interviews were administered to eight participants to gather detailed information about their online learning experiences. It was found that (a) the participants' online learning attitudes were generally positive and increased when completing the course; and (b) the participants' continuous intention to learn online was significantly predicted by four self-regulatory factors and attitudes, mediated through perceived online social interactions. The analysis of the interviewees' further comments provided more insights about the potential factors contributing to their online learning attitude changes. The strategies for future online course design with a view of improving students' self-regulated learning skills are discussed in this paper.

Extensive studies have been conducted to diagnose and predict students' academic performance by analyzing a large amount of data related to their learning behaviors in a blended learning environment. But there is a lack of research examining how individualized learning interventions could improve students' academic performance in such a learning context. In this study, a quasi-experiment was designed to investigate the effect of an individualized intervention approach on students' course performance and learning behaviors in a blended course. Forty-nine Chinese tertiary students undertaking the course were randomly assigned into two groups -- the experimental and control groups. During the course, the experimental group received individualized interventions, while the control group received undifferentiated interventions. The data about these participants' learning behaviors were collected over 15 weeks. The results indicated that, compared with the control group, the experimental group showed significantly better academic performance, a higher level of learning motivation, attitude and self-efficacy, more active learning behaviors, and fewer passive learning behaviors. The control group revealed similar online learning time, but significantly more resource utilization and forum access. It is concluded that personalized learning intervention can effectively improve students' learning behaviors, attitude, motivation, self-efficacy, and academic performance in a blended learning environment.

1 000 MW coal-fired unit, as one of the best domestic advanced units, the performance evaluation and analysis of the key parameters of its denitrification unit after the ultra-low emission reformation can provide reference and guidance for stable and safe operation of selective catalytic reduction (SCR) flue gas denitrification unit under ultra-low emission of coal-fired units. Taking the SCR denitrification unit of a 1 000 MW coal-fired unit that had achieved ultra-low denitrification emission as an example, the key operating parameters of the SCR denitrification unit were studied, such as denitrification efficiency, NOx concentration distribution at the inlet and outlet, ammonia escape, etc., the main performance of the denitrification unit was analyzed. The test results show that the overall performance of the SCR denitrification unit is good after the ultra-low emission transformation, but the problems of poor flow field uniformity and excessive ammonia escape still exist. Therefore, the adjustment of low nitrogen combustion, the optimization of ammonia injection, the uniformity test of flow field and the life-cycle management of catalyst should be strengthened.