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INTRODUCTION: The average carbon storage of Pinus massoniana is much higher than the average carbon storage of Chinese forests, an important carbon sink tree species in subtropical regions of China. However, there are few studies on the differences in rhizosphere microorganisms of P. massoniana with different carbon storages. METHODS: To clarify the relationships between plant carbon storage level, environmental parameters and microbial community structure, we identified three carbon storage levels from different P. massoniana provenances and collected rhizosphere soil samples. We determined chemical properties of soil, extracellular enzyme activity, and microbial community structures at different carbon storage levels and examined how soil factors affect rhizosphere microorganisms under different carbon storage levels. RESULTS: The results revealed that soil organic carbon (SOC), nitrate nitrogen (NO3--N), ammonium nitrogen (NH4+-N) contents all increased with increasing carbon storage levels, while pH decreased accordingly. In contrast, the available phosphorus (AP) content did not change significantly. The soil AP content was within the range of 0.91 ~ 1.04 mg/kg. The microbial community structure of P. massoniana changed with different carbon storage, with Acidobacteria (44.27%), Proteobacteria (32.57%), and Actinobacteria (13.43%) being the dominant bacterial phyla and Basidiomycota (73.36%) and Ascomycota (24.64%) being the dominant fungal phyla across the three carbon storage levels. Soil fungi were more responsive to carbon storage than bacteria in P. massoniana. C/N, NH4+-N, NO3--N, and SOC were the main drivers (p

Intelligent equipment is necessary to ensure stable, high-quality, and efficient production of facility agriculture. Among them, intelligent harvesting equipment needs to be designed and developed according to the characteristics of fruits and vegetables, so there is little large-scale mechanization. The intelligent harvesting equipment in Japan has nearly 40 years of research and development history since the 1980s, and the review of its research and development products has specific inspiration and reference significance. First, the preferential policies that can be used for harvesting robots in the support policies of the government and banks to promote the development of facility agriculture were introduced. Then, the development of agricultural robots in Japan was reviewed. The top ten fruits and vegetables in the greenhouse were selected, and the harvesting research of tomato, eggplant, green pepper, cucumber, melon, asparagus, and strawberry harvesting robots based on the combination of agricultural machinery and agronomy was analyzed. Next, the commercialized solutions for tomato, green pepper, and strawberry harvesting system were detailed and reviewed. Among them, taking the green pepper harvesting robot developed by the start-up company AGRIST Ltd. in recent years as an example, the harvesting robot developed by the company based on the Internet of Things technology and artificial intelligence algorithms was explained . This harvesting robot can work 24 h a day and can control the robot's operation through the network. Then, the typical strawberry harvesting robot that had undergone four generations of prototype development were reviewed. The fourth-generation system was a systematic solution developed by the company and researchers. It consisted of high-density movable seedbeds and a harvesting robot with the advantages of high space utilization, all-day work, and intelligent quality grading. The strengths, weaknesses, challenges, and future trends of prototype and industrialized solutions developed by universities were also summarized. Finally, suggestions for accelerating the development of intelligent, smart, and industrialized harvesting robots in China's facility agriculture were provided.

Terpenoids, abundant and structurally diverse secondary metabolites in plants, especially in conifer species, play crucial roles in the plant defense mechanism and plant growth and development. In Pinus massoniana, terpenoids' biosynthesis relies on both the mevalonate (MVA) pathway and the 2-methyl-D-erythritol-4-phosphate (MEP) pathway, with 1-hydroxy-2-methyl-2-(E)-butenyl-4-diphosphate synthase (HDS) catalyzing the sixth step of the MEP pathway. In this study, we cloned and conducted bioinformatics analysis of the PmHDS gene from P. massoniana. The results showed that PmHDS shares homology with HDS proteins from other species. Analysis of tissue expression patterns indicated that PmHDS exhibits the highest expression level in xylem tissue, followed by stems, with significantly lowest expression in the apical meristem. Treatment with NaCl, abscisic acid (ABA), ethylene (ETH), methyl jasmonate (MeJA), and salicylic acid (SA) upregulated the expression of PmHDS. Furthermore, we successfully cloned the PmHDS promoter (about 2220 bp) and integrated it into a GUS reporter vector, which resulted in GUS activity being observed in various tissues of Arabidopsis thaliana. Overexpression of the PmHDS gene in A. thaliana significantly increased the content of carotenoids, chlorophylls a and b, and related enzyme activities, as well as the levels of terpenoid derivatives such as cytokinin (CTK), gibberellic acid (GA), and ABA, thereby enhancing the resistance to those abiotic stresses. These findings suggest that PmHDS plays an important role in the terpenoid synthesis pathway. This study provides a theoretical basis for understanding the biosynthesis of terpenoids and lays a foundation for future research on the regulation of terpene synthesis and resistance in molecular breeding. [ABSTRACT FROM AUTHOR]

A series of novel amphiphilic alternating CPEG copolymers were synthesized through an amine–epoxy click reaction comprising aliphatic amine and polyethylene glycol diglycidyl ether (PEGDE). These polymers were characterized in detail via nuclear magnetic resonance (NMR), gel permeation chromatography (GPC), Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA) to confirm the successful synthesis. Due to their amphiphilic structure, these polymers display thermoresponsiveness, with tunable cloud points (Tcps) that are adjustable from 20.8 °C to 46.8 °C by altering the side-chain length of the aliphatic amine, varying the mixing ratios of copolymers, the solution's pH, and salt additions. This tunable thermoresponsive behavior positions CPEG copolymers as promising candidates for a range of functional material applications. [ABSTRACT FROM AUTHOR]

Learning analytics has received increasing attention in the field of education. However, few studies have investigated the overall impact of learning analytics interventions on learning achievements. This study aims to close this research gap and examine the sizes of the overall effects of learning analytics interventions on learning achievements according to research conducted from 2012 to 2021. In total, 33 empirical studies including 3098 participants were synthesized in the present meta-analysis. The findings revealed that learning analytics interventions had a large effect size on learning achievements. Furthermore, the impacts of 13 moderator variables were analyzed in depth. The results indicated that sample levels, learning domains, learning approaches, learning analytics technologies, and learning analytics metrics significantly moderated the effectiveness of learning analytics interventions. These findings and their implications for the use of learning analytics interventions were discussed in depth. [ABSTRACT FROM AUTHOR]

Transcription factors refer to types of proteins that perform significant functions in the process of gene expression regulation. The ethylene insensitive 3/ethylene insensitive 3-like (EIN3/EIL) family, functioning as significant transcription factors regulating ethylene, plays a critical role in the growth and development of plants and participates in the plant's response to diverse environmental stresses. Pinus massoniana is an excellent native tree with high economic and ecological value. However, the study of EIN3/EIL genes in gymnosperms, for instance, P. massoniana, is still relatively limited. In this research, four putative EIN3/EIL genes were identified in the transcriptome of P. massoniana. Bioinformatics analysis showed that PmEIL genes contain a highly conserved EIN3 domain and other structural features of acidic, proline-rich and glutamine-rich sites. The molecular evolution tree analysis demonstrated that the EIN3/EIL family was partitioned into three categories (A, B, and C), and the number, type, and distribution of conserved motifs grouped in one category were similar. The results of qRT-PCR indicated that the expression levels of PmEIL genes were markedly elevated in needles compared to other tissues. Through the analysis of expression patterns of the PmEIL genes under various stress treatments, it was found that the PmEIL genes could participate in plant hormone stimulation induction, osmosis, drought and other response processes. In addition, PmEIL is a nuclear localization protein. PmEIL1, PmEIL3, and PmEIL4 are transcriptional activators, while PmEIL2 is a transcriptional suppressor. This research provides a basis for further elucidating the function of EIN3/EIL transcription factors in growth, development and stress response of P. massoniana. [ABSTRACT FROM AUTHOR]

The document discusses the integration of intelligent agricultural machinery and robots in modern agriculture to address global food demands, labor shortages, and sustainability concerns. It highlights advancements in path-planning, sensor-based monitoring, precision agriculture, and robotic systems for various agricultural functions. The studies emphasize the role of robotics, AI, and sensor technology in enhancing agricultural efficiency, precision, and sustainability, ultimately contributing to a resilient global food system. The authors underscore the importance of embracing these innovations for a sustainable and high-efficiency agricultural future. [Extracted from the article]

In Pinus massoniana, the methyl-D-erythritol-4-phosphate (MEP) pathway plays a crucial role in the biosynthesis of terpenoids. The fourth step of this pathway is specifically regulated by 4-(cytidine 5′-diphospho)-2-C-methyl-D-erythritol kinase (CMK). In this study, PmCMK (MW892445.1) was isolated. As a member of the GHMP kinase family, PmCMK exhibits homology with CMK genes across diverse species. The examination of relative expression patterns revealed that PmCMK exhibited higher expression levels in tissues of P. massoniana that are rich in resin. We successfully cloned the PmCMK promoter (1654 bp) and integrated it into a GUS reporter vector. This construct was then transformed into the leaves of tobacco (Nicotiana × sanderae) to assess transient expression patterns. The results demonstrated that the promoter was active not only in the roots, leaves, and stems of the tobacco plants but also exhibited varying expression levels in response to treatments with IAA, SA, MeJA, and PEG6000. This suggested that PmCMK expression was modulated by a variety of signals. It revealed that the expression of PmCMK was affected by different treatments. Further allogeneic expression studies showed that tobacco overexpressing PmCMK exhibited increased levels of chlorophyll and carotene compared to the wild type. This enhancement in content indicates that PmCMK has a significant role in isoprene biosynthesis. These findings provide valuable insights for future research aimed at elucidating the biosynthetic pathways of terpenoids and developing breeding strategies to enhance resin production in P. massoniana. [ABSTRACT FROM AUTHOR]

Background: Online collaborative learning has been widely adopted in the field of education. However, learners often find it difficult to engage in collaboratively building knowledge and jointly regulating online collaborative learning. Objectives: The study compared the impacts of the three learning approaches on collaborative knowledge building, group performance, socially shared regulation, behavioural engagement, and cognitive load in an online collaborative learning context. The first is the automatic construction of knowledge graphs (CKG) approach, the second is the automatic analysis of topic distribution (ATD) approach, and the third one is the traditional online collaborative learning (OCL) approach without any analytic feedback. Methods: A total of 144 college students participated in a quasi‐experimental study, where 48 students learned with the CKG approach, 48 students used the ATD approach, and the remaining 48 students adopted the OCL approach. Results and Conclusions: The findings revealed that the CKG approach could encourage collaborative knowledge building, socially shared regulation, and behavioural engagement in building knowledge better than the ATD and OCL approaches. Both the CKG and ATD approaches could better improve group performance than the OCL approach. Furthermore, the CKG approach did not increase learners' cognitive load, but the ATD approach did. Implications: This study has theoretical and practical implications for utilising learning analytics in online collaborative learning. Furthermore, deep neural network models are powerful for constructing knowledge graphs and analysing topic distribution. Lay Description: What is currently known about the subject matter: Online collaborative learning has been widely adopted in the field of education.Learners often find it difficult to engage in collaboratively building knowledge and jointly regulating collaborative learning. What the paper adds to this: This study compared the impacts of three learning approaches, namely, the automatic construction of knowledge graphs (CKG), the automatic analysis of topic distribution (ATD), and the traditional online collaborative learning (OCL) approach.The findings revealed that the CKG approach could encourage collaborative knowledge building, socially shared regulation, and behavioral engagement in building knowledge better than the ATD and OCL approaches. Both the CKG and ATD approaches could better improve group performance than the OCL approach. Furthermore, the CKG approach did not increase learners' cognitive load, but the ATD approach did. Implications of study findings for practitioners: It is strongly recommended that the CKG approach can be employed to enhance collaborative knowledge building, socially shared regulation, and behavioral engagement in building knowledge in an online collaborative learning context. Both the CKG and ATD approaches contribute to improving group performance.Deep neural network models are powerful for constructing knowledge graphs and analysing topic distribution. [ABSTRACT FROM AUTHOR]