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As a groundbreaking innovation in the field of soft robotics, fabric-based pneumatic soft actuators exhibit substantial advantages over traditional rigid mechanical systems in terms of adaptability, safety, and multifunctionality. This paper presents a thorough review of the design principles, classifications, and application advancements of these actuators. By leveraging the intrinsic flexibility and programmability of fabric materials, these actuators achieve complex and precise motion control through the modulation of internal air pressure. This review investigates the state-of-the-art research progress in overcoming critical challenges, such as enhancing multidirectional expansion capabilities, optimizing the trade-off between flexibility and driving force, and improving control accuracy and response speed. Furthermore, the integration of fabric-based actuators with flexible sensors is highlighted as a highly promising research direction, offering the potential to enhance device intelligence via real-time feedback and adaptive control functionalities. In conclusion, with ongoing advancements in material science, structural design, and control strategies, fabric-based pneumatic soft actuators are expected to unlock broader application potentials in domains such as healthcare, wearable technology, and human–-computer interaction.

Background The Chinese government initiated the “Top-notch Students Training Program 2.0 for Basic Disciplines” to cultivate talent in foundational fields, including basic medicine. However, existing programs lack a systematic framework tailored to the specific needs of basic medicine students. This study proposes a novel cultivation model specifically designed for basic medicine students, integrating interdisciplinary research and addressing the unique challenges inherent in foundational medical education. Compared to existing international models such as MSTP in the U.S., which focuses predominantly on streamlined clinical-research integration, and WISE in Japan, emphasizing productivity through targeted doctoral training, this model uniquely incorporates comprehensive interdisciplinary teaching, structured research training mechanisms, and explicit quality assurance procedures. These enhancements specifically address previously identified gaps in holistic, systematic cultivation frameworks for top-notch basic medicine students, aligning closely with national educational strategies and global healthcare challenges. Methods This study employed a modified Delphi method conducted over three rounds. An initial indicator framework was developed by analyzing documents from 12 universities, primarily those participating in the “Top-notch Students Training Program 2.0 for Basic Disciplines.”Experts specializing in medical education and academic management were invited to evaluate the relevance and importance of the proposed indicators. During each Delphi round, experts provided ratings and qualitative feedback, which informed iterative refinements of the framework. Adjustments were made based on statistical thresholds (e.g., arithmetic mean < 4, full score ratio < 0.5, or variation coefficient > 0.25) and expert consensus. The process concluded with a finalized cultivation model, incorporating 5 primary indicators, 17 secondary indicators, and 63 tertiary indicators. To validate the model, statistical analysis was conducted to ensure its reliability, practicality, and alignment with the goals of top-notch student cultivation in basic medicine. The finalized framework emphasizes interdisciplinary integration, innovation, and research-based learning, reflecting the priorities identified in the Delphi process. Results The effective response rates for the three rounds were 84%, 100%, and 95%, respectively, with an expert authority coefficient of 0.89. Based on these consultations, the final model includes 5 primary indicators, 17 secondary indicators, and 63 tertiary indicators. The model emphasizes five core dimensions: cultivation philosophy, cultivation standards and objectives, curriculum and teaching, scientific research training, and quality assurance and evaluation. Conclusions The proposed model emphasizes a systematic approach, adaptability to environmental changes, and practical operability in the cultivation process. The five core dimensions—cultivation philosophy, cultivation standards and objectives, curriculum and teaching, scientific research training, and quality assurance and evaluation—are designed to work in harmony. This ensures seamless alignment throughout the cultivation process, promoting synergy between elements to optimize pathways for top-notch students in basic medicine.

Climate change is reshaping plant distribution and ecological adaptation worldwide. Ficus tikoua Bur., a perennial resource plant native to Southwest and South China, has not been systematically assessed for its future cultivation potential. In this study, we used the Biomod2 ensemble modeling framework, integrating 12 algorithms with 469 occurrence records and 16 environmental variables, to predict the potential distribution and niche dynamics of F. tikoua under current and future climate scenarios (SSP126, SSP370, and SSP585). The ensemble model achieved high predictive accuracy based on multiple algorithms and cross-validation. The minimum temperature of the coldest month (bio6, 43.5%), maximum temperature of the warmest month (bio5, 25.0%), and annual precipitation (bio12, 10.3%) were identified as the dominant factors shaping its distribution. Model projections suggest that suitable habitats will generally expand northwestward, while contracting in the southeast. Core areas, such as the Yunnan–Guizhou Plateau and the Sichuan Basin, are predicted to remain highly stable. In contrast, southeastern marginal regions are likely to experience a decline in suitability due to intensified heat stress. Niche analyses further revealed strong niche conservatism (overlap D = 0.83–0.94), suggesting that the species maintains stable climatic tolerance and adapts primarily through range shifts rather than evolutionary change. This finding suggests limited adaptive flexibility in response to rapid warming. Overall, climate warming may enhance cultivation opportunities for F. tikoua at higher latitudes and elevations, while emphasizing the importance of protecting stable core habitats, planning climate adaptation corridors, and integrating this species into climate-resilient agroforestry strategies. These findings provide practical guidance for biodiversity conservation and land-use planning, offering a scientific basis for regional policy formulation under future climate change.

Green technology innovation is one of the important driving forces to promote green development of China’s economy. Based on the panel data of 29 provinces, municipalities and autonomous regions in China and the mechanism of Outward Foreign Direct Investment (OFDI) on green technology innovation, this paper introduced different spatial weights and established a Spatial Durbin Model (SDM) to study the impact of OFDI on China’s green technology innovation according to the hypothesis proposed in this paper. It is found that OFDI can significantly promote China’s green technology innovation, which is under the influence of different spatial weights, and presents a significant spatial spillover effect. However, there are obvious regional differences in the spatial impact of OFID on green technology innovation, and the spatial spillover effect in some regions is not significant enough. Then the change of international investment environment will affect the spatial spillover effect of OFDI on China’s green technology innovation. Moreover, OFDI has significant nonlinear characteristics in its impact on green technology innovation. After crossing the threshold, OFDI has significantly increased its impact on green technology innovation, which further confirms that OFDI can effectively promote China’s green technology innovation and it can promote China’s green economic development.

The importance of the humanities in promoting economic and social development is becoming increasingly clear. Combining humanities with higher medical education in order to meet the needs of medical talent training in the new situation has become a key component of higher medical education reform and development. Adult higher medical education is an integral part of higher medical education, but it has different training objectives and training objects than regular higher medical education. These technological advancements are certain to hasten the continued emergence of education cloud or industry cloud, create a good information-based environment for education informatization improvement, and pose technical challenges to resource allocation in intelligent computing environments. Humanistic quality higher medical education based on the Internet of Things and intelligent computing makes the efficient intelligent information system more open, interactive, and coordinated, allowing students and teachers to perceive a variety of teaching resources more comprehensively.

Protoberberine alkaloids are a characteristic group of natural products in Coptis plants known for their notable pharmacological activities. However, the structural similarity and the substrate promiscuity of their biosynthetic enzymes have left the precise synthetic pathways remain unclarified, posing challenges to regulate product formation. In this study, we identified CcOMT8, a key enzyme responsible for C2‐methoxylation in the biosynthesis of epiberberine in C. chinensis, through methyl jasmonate elicitation analysis and comparative genomics‐based microsynteny analysis. Functional characterisation demonstrated that CcOMT8 specifically catalyses 2‐O‐methylation of (S)‐scoulerine, as verified by heterologous expression in both microbial and plant systems. Its lack of activity toward (S)‐cheilanthifoline further confirmed the specific route for epiberberine biosynthesis. Structural investigations of CcOMT8 and its complexes revealed key aspects of substrate recognition and a catalytic mechanism mediated by the His253‐Asp254‐Glu312 triad. Comparative structural analysis with 9‐O‐methyltransferases indicated that hydrophilic residues and reduced steric hindrance in the substrate binding pocket govern the regioselectivity of CcOMT8. Using focused rational iterative site‐specific mutagenesis (FRISM), we developed an optimised mutant, S109L/C250A/L300A, with 4.88‐fold enhanced catalytic efficiency. This study elucidates the biosynthetic pathway of epiberberine in Coptis, clarifies the molecular basis of enzyme‐directed metabolic flux, and provides efficient biocatalysts for the synthetic biosynthesis of protoberberine alkaloids.

Alfalfa is a perennial herbaceous forage legume that is remarkably and negatively affected by monocropping. However, the contribution of the changes in bacterial communities to soil sickness in alfalfa have not been elucidated. Therefore, we investigated bacterial community structures in response to monocropped alfalfa along the chronosequence. Continuous cropping remarkably reduced bacterial alpha diversity and altered community structures, and soil pH, total P and available P were strongly associated with the changes of bacterial diversity and community structures. Intriguingly, 10 years of monocropped alfalfa might be a demarcation point separating soil bacterial community structures into two obvious groups that containing soil samples collected in less and more than 10 years. The relative abundances of copiotrophic bacteria of Actinobacteria and Gammaproteobacteria significantly increased with the extension of continuous cropping years, while the oligotrophic bacteria of Armatimonadetes, Chloroflexi, Firmicutes and Gemmatimonadetes showed the opposite changing patterns. Among those altered phyla, Actinobacteria, Chloroflexi, Alphaproteobacteria and Acidobacteria were the most important bacteria which contributed 50.86% of the community variations. Additionally, the relative abundances of nitrogen fixation bacteria of Bradyrhizobium and Mesorhizobium obviously increased with continuous cropping years, while the abundances of Arthrobacter, Bacillus, Burkholderiaceae and Microbacterium with potential functions of solubilizing phosphorus and potassium remarkably decreased after long-term continuous cropping. Furthermore, bacterial cooccurrence patterns were significantly influenced by continuous cropping years, with long-term monocropped alfalfa simplifying the complexity of the cooccurrence networks. These findings enhanced our understandings and provided references for forecasting how soil bacterial communities responds to monocropped alfalfa.

Ray tracing is a technique for generating an image by tracing the path of light through pixels in an image plane and simulating the effects of high-quality global illumination at a heavy computational cost. Because of the high computation complexity, it can't reach the requirement of real-time rendering. The emergence of many-core architectures, makes it possible to reduce significantly the running time of ray tracing algorithm by employing the powerful ability of floating point computation. In this paper, a new GPU implementation and optimization of the ray tracing to accelerate the rendering process is presented.

Permafrost, characterized by its frozen soil, serves as a unique habitat for diverse microorganisms. Understanding these microbial communities is crucial for predicting the response of permafrost ecosystems to climate change. However, large-scale evidence regarding stratigraphic variations in microbial profiles remains limited. Here, we analyze microbial community structure and functional potential based on 16S rRNA gene amplicon sequencing and metagenomic data obtained from an ∼1000 km permafrost transect on the Tibetan Plateau. We find that microbial alpha diversity declines but beta diversity increases down the soil profile. Microbial assemblages are primarily governed by dispersal limitation and drift, with the importance of drift decreasing but that of dispersal limitation increasing with soil depth. Moreover, genes related to reduction reactions (e.g., ferric iron reduction, dissimilatory nitrate reduction, and denitrification) are enriched in the subsurface and permafrost layers. In addition, microbial groups involved in alternative electron accepting processes are more diverse and contribute highly to community-level metabolic profiles in the subsurface and permafrost layers, likely reflecting the lower redox potential and more complicated trophic strategies for microorganisms in deeper soils. Overall, these findings provide comprehensive insights into large-scale stratigraphic profiles of microbial community structure and functional potentials in permafrost regions. Research on permafrost microbial communities is crucial for predicting the response of permafrost ecosystems to climate change. Here, Kang et al. provide insights into the structure and functional potential of permafrost microbial communities by analyzing 16S rRNA gene sequence data and metagenomic data obtained from an ∼1000 km transect on the Tibetan Plateau.

Maize leaf disease detection is an essential project in the maize planting stage. This paper proposes the convolutional neural network optimized by a Multi-Activation Function (MAF) module to detect maize leaf disease, aiming to increase the accuracy of traditional artificial intelligence methods. Since the disease dataset was insufficient, this paper adopts image pre-processing methods to extend and augment the disease samples. This paper uses transfer learning and warm-up method to accelerate the training. As a result, three kinds of maize diseases, including maculopathy, rust, and blight, could be detected efficiently and accurately. The accuracy of the proposed method in the validation set reached 97.41%. This paper carried out a baseline test to verify the effectiveness of the proposed method. First, three groups of CNNs with the best performance were selected. Then, ablation experiments were conducted on five CNNs. The results indicated that the performances of CNNs have been improved by adding the MAF module. In addition, the combination of Sigmoid, ReLU, and Mish showed the best performance on ResNet50. The accuracy can be improved by 2.33%, proving that the model proposed in this paper can be well applied to agricultural production.