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Tumor microenvironment (TME) is a complex ecosystem composed of both cellular and non-cellular components that surround tumor tissue. The extracellular matrix (ECM) is a key component of the TME, performing multiple essential functions by providing mechanical support, shaping the TME, regulating metabolism and signaling, and modulating immune responses, all of which profoundly influence cell behavior. The quantity and cross-linking status of stromal components are primary determinants of tissue stiffness. During tumor development, ECM stiffness not only serves as a barrier to hinder drug delivery but also promotes cancer progression by inducing mechanical stimulation that activates cell membrane receptors and mechanical sensors. Thus, a comprehensive understanding of how ECM stiffness regulates tumor progression is crucial for identifying potential therapeutic targets for cancer. This review examines the effects of ECM stiffness on tumor progression, encompassing proliferation, migration, metastasis, drug resistance, angiogenesis, epithelial-mesenchymal transition (EMT), immune evasion, stemness, metabolic reprogramming, and genomic stability. Finally, we explore therapeutic strategies that target ECM stiffness and their implications for tumor progression.

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.

Pseudouridine (Ψ) is an abundant post-transcriptional RNA modification in ncRNA and mRNA. However, stoichiometric measurement of individual Ψ sites in human transcriptome remains unaddressed. Here we develop ‘PRAISE’, via selective chemical labeling of Ψ by bisulfite to induce nucleotide deletion signature during reverse transcription, to realize quantitative assessment of the Ψ landscape in the human transcriptome. Unlike traditional bisulfite treatment, our approach is based on quaternary base mapping and revealed an ~10% median modification level for 2,209 confident Ψ sites in HEK293T cells. By perturbing pseudouridine synthases, we obtained differential mRNA targets of PUS1, PUS7, TRUB1 and DKC1, with TRUB1 targets showing the highest modification stoichiometry. In addition, we quantified known and new Ψ sites in mitochondrial mRNA catalyzed by PUS1. Collectively, we provide a sensitive and convenient method to measure transcriptome-wide Ψ; we envision this quantitative approach would facilitate emerging efforts to elucidate the function and mechanism of mRNA pseudouridylation. Pseudouridine (Ψ) is an important modification in RNA biology and mRNA vaccine. A method called PRAISE was developed via selective labeling of Ψ by bisulfite to induce nucleotide deletion signature during reverse transcription, thus realizing quantitative assessment of the Ψ landscape in the human transcriptome.

Studying the spatial and temporal changes of grassland soil organic carbon (SOC) is helpful in promote the management of regional ecosystem carbon sinks. Grazing is one of the main ways of rational utilization of grassland. Different grazing intensities will affect the change of SOC density. Under different grazing intensity and management measures in Zhangye grassland, this study uses the parameter localized CENTURY model to simulate the temporal and spatial variations of SOC density from 1970 to 2022. The results showed that long-term light grazing reduced the average SOC by 195.114 g·m −2 and 1.91%. Moderate and severe grazing, respectively, for a long time made the total SOC density loss of 5.21% and 17.69%. In a short period, mild and moderate grazing reduced total SOC first and then increased it. Under light grazing, total SOC density appeared higher relative changes in the southeast, and lower in the northwest and central. There was no significant difference in the relative changes of total SOC between steppe and desert grasslands under light grazing. The decrease range of steppe was gradually greater than that in desert grassland. Since different management measures were implemented in some sampling sites in 2017, we divided the study period into two periods, 1970–2016 and 2017–2022. The implementation of degraded grassland improvement, fallow grazing, and rotational grazing would increase the total SOC density and mild SOC density, rotational grazing > degraded grassland improvement > rest grazing. Rotational grazing and the improvement of degraded grassland increased the density of active and inert SOC, while resting grazing decreased the density of SOC.

The apple industry is an essential industry to assist in rural revitalization. However, in recent years, the urbanization, industrialization, globalization and climate change have brought various challenges to the apple industry in China's main apple-producing provinces. Given this, effectively identifying, enhancing on apple production comparative advantage (APCA) is imperative to safeguard the long-term sustainable development of China's apple industry. This study aims to explore the evolutionary trends and influencing factors of APCA, and to provide quantitative support for the formulation of scientific and effective apple production policies. In this paper, the APCA of China's eight main apple-producing provinces from 2013 to 2022 was measured by using a aggregate comparative advantage index. The spatio-temporal dynamic evolution characteristics of APCA were revealed by adopted Arc GIS and kernel density estimation method. Second, the transfer probabilities of different types of APCA were predicted by empolyed traditional and spatial Markov chains. Finally, the driving mechanism of APCA is explored with the panel quantile model. 1) The average value of APCA of the main producing provinces increased from 1.330 in 2013 to 1.419 in 2022. 2) The probabilities of provinces with low, primary and middle level of advantage jumping to the next level are 31.58%, 16.67% and 11.76%, respectively. When the spatial lag type is high-level advantage, the probability of stabilization of the low-level advantage decreases from 68.42% to 0.00%. 3) Nonfarm payrolls have the largest dampening effect at the 40% quantile. 1) Temporally, APCA shows a trend of slow growth, ups and downs. Spatially, APCA shows a distribution pattern of \"west high, east low\". 2) APCA mainly shifted sequentially between neighbouring ranks. Besides, the change of APCA had significant spatial spillover effect, and highly advantage provinces featured more prominent proactive spillovers. 3) There is significant heterogeneity among the influencing factors.

Abstract Microbiomes are important for crop performance. However, a deeper knowledge of crop-associated microbial communities is needed to harness beneficial host-microbe interactions. Here, by assessing the assembly and functions of maize microbiomes across soil types, climate zones, and genotypes, we found that the stem xylem selectively recruits highly conserved microbes dominated by Gammaproteobacteria. We showed that the proportion of bacterial taxa carrying the nitrogenase gene ( nifH ) was larger in stem xylem than in other organs such as root and leaf endosphere. Of the 25 core bacterial taxa identified in xylem sap, several isolated strains were confirmed to be active nitrogen-fixers or to assist with biological nitrogen fixation. On this basis, we established synthetic communities (SynComs) consisting of two core diazotrophs and two helpers. GFP-tagged strains and 15 N isotopic dilution method demonstrated that these SynComs do thrive and contribute, through biological nitrogen fixation, 11.8% of the total N accumulated in maize stems. These core taxa in xylem sap represent an untapped resource that can be exploited to increase crop productivity.

Plant residue decomposition is critical for carbon cycling in terrestrial ecosystems. Nitrogen (N) availability alters this process through orchestrating the microbial community, yet the mechanisms remain elusive. By investigating the wheat residue decomposition process and the microbial succession under different N input levels in agricultural fields, we find that higher N availability accelerates residue breakdown mainly at the early stage by promoting the rapid colonization of a soil-derived microbial consortium with key interactions. Metabolic potential evaluations show that the Bacillus decomposers harbor diverse carbohydrate-active enzymes that degrade cellulose and hemicellulose, whereas the non-decomposer Staphylococcus sciuri efficiently transports and consumes downstream sugar products. Synthetic communities combined with omics techniques confirm that the N-enriched non-decomposer S. sciuri restricts the growth of weak decomposers through sugar depletion, thereby restructuring the community dominated by strong decomposers. This shift increases the residue decomposition rate by 16.77% under N fertilization. Our results highlight the important role of usually overlooked fast-growing non-decomposers in agricultural soil carbon cycling. The mechanisms by which N inputs alter residue decay remain elusive. Here, the authors find that the usually overlooked fast-growing non-decomposers reshape a community dominated by strong decomposers through sugar depletion, facilitating residue decay.

Traditional villages, an important rural cultural heritage, have gradually developed tourism value and become attractive tourism resources with the support of increasingly convenient transportation infrastructure, and there is inevitably a correlation between the spatial pattern distribution and tourism attractiveness as well as transportation accessibility. Therefore, the study adopts ArcGIS, a modified gravity model combined with the research method of transportation accessibility, to investigate the interaction mechanism and influence relationship between 724 traditional villages in 88 districts and counties of Guizhou Province as the research object. The results found that: (1) Traditional villages in Guizhou Province are mainly clustered in Qiandongnan, with an overall clustering distribution. (2) Guizhou has formed an attraction structure centered on the central Guizhou economic circle. The number of traditional villages represents the richness of their cultural tourism resources, which affects the tourism economic links between regions. (3) Traditional villages are mainly centered on Qiandongnan, forming six isochronous circles. The regional superposition effect generated by transportation accessibility will accelerate the formation of the core advantages of tourist destinations. (4) The degree of agglomeration of traditional villages will affect the advantages and disadvantages of rural cultural tourism resource endowment. Transportation and tourism are mutually beneficial and coexistent, and when the roads are clear and not blocked, the industry will prosper, and when the scenery is beautiful, the people will be rich. The study provides ideas for deepening the integration of road and tourism in traditional villages, and is also activating the tourism value of traditional villages and promoting the layout of reverse and niche tourism products in rural areas.

N 6 ,2′- O -dimethyladenosine (m 6 Am), a terminal modification adjacent to the mRNA cap, is a newly discovered reversible RNA modification. Yet, a specific and sensitive tool to directly map transcriptome-wide m 6 Am is lacking. Here, we report m 6 Am-seq, based on selective in vitro demethylation and RNA immunoprecipitation. m 6 Am-seq directly distinguishes m 6 Am and 5′-UTR N 6 -methyladenosine (m 6 A) and enables the identification of m 6 Am at single-base resolution and 5′-UTR m 6 A in the human transcriptome. Using m 6 Am-seq, we also find that m 6 Am and 5′-UTR m 6 A respond dynamically to stimuli, and identify key functional methylation sites that may facilitate cellular stress response. Collectively, m 6 Am-seq reveals the high-confidence m 6 Am and 5′-UTR m 6 A methylome and provides a robust tool for functional studies of the two epitranscriptomic marks. m 6 Am is a dynamic and reversible RNA modification found on the mRNA cap. Here the authors report m 6 Am-seq to directly distinguish m 6 Am from m 6 A and identify functional methylation sites.

Background Herbal nanoparticles are made from natural herbs/medicinal plants, their extracts, or a combination with other nanoparticle carriers. Compared to traditional herbs, herbal nanoparticles lead to improved bioavailability, enhanced stability, and reduced toxicity. Previous research indicates that herbal medicine nanomaterials are rapidly advancing and making significant progress; however, bibliometric analysis and knowledge mapping for herbal nanoparticles are currently lacking. We performed a bibliometric analysis by retrieving publications related to herbal nanoparticles from the Web of Science Core Collection (WoSCC) database spanning from 2004 to 2023. Data processing was performed using the R package Bibliometrix, VOSviewers, and CiteSpace. Results In total, 1876 articles related to herbal nanoparticles were identified, originating from various countries, with China being the primary contributing country. The number of publications in this field increases annually. Beijing University of Chinese Medicine, Shanghai University of Traditional Chinese Medicine, and Saveetha University in India are prominent research institutions in this domain. The Journal “International Journal of Nanomedicine” has the highest number of publications. The number of authors of these publications reached 8234, with Yan Zhao, Yue Zhang, and Huihua Qu being the most prolific authors and Yan Zhao being the most frequently cited author. “Traditional Chinese medicine,” “drug delivery,” and “green synthesis” are the main research focal points. Themes such as “green synthesis,” “curcumin,” “wound healing,” “drug delivery,” and “carbon dots” may represent emerging research areas. Conclusions Our study findings assist in identifying the latest research frontiers and hot topics, providing valuable references for scholars investigating the role of nanotechnology in herbal medicine. Graphical Abstract