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Epigenetic modifications are crucial for plant development. EFD (ExineFormationDefect) encodes a SAM-dependent methyltransferase that is essential for the pollen wall pattern formation and male fertility in Arabidopsis. In this study, we find that the expression of DRM2, a de novo DNA methyltransferase in plants, complements for the defects in efd, suggesting its potential de novo DNA methyltransferase activity. Genetic analysis indicates that EFD functions through HB21, as the knockout of HB21 fully restores fertility in efd mutants. DNA methylation and histone modification analyses reveal that EFD represses the transcription of HB21 through epigenetic mechanisms. Additionally, we demonstrate that HB21 directly represses the expression of genes crucial for pollen formation and anther dehiscence, including CalS5, RPG1/SWEET8, CYP703A2 and NST2. Collectively, our findings unveil a double negative regulatory cascade mediated by epigenetic modifications that coordinates anther development, offering insights into the epigenetic regulation of this process. Epigenetic modifications are crucial for plant development. Here, the authors find an epigenetically mediated double negative cascade for pollen formation and anther dehiscence, offering new insights into epigenetic regulation of anther development. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a disabling and highly prevalent condition affecting millions worldwide. Recently discovered, disulfidptosis represents a novel form of cell death induced by the excessive accumulation of cystine. Despite its significance, a systematic exploration of disulfidptosis-related genes (DRGs) in OA is lacking. Methods: This study utilized three OA-related datasets and DRGs. Differentially expressed (DE)-DRGs were derived by intersecting the differentially expressed genes (DEGs) from GSE114007 with DRGs. Feature genes underwent screening through three machine learning algorithms. High diagnostic value genes were identified using the receiver operating characteristic curve. Hub genes were confirmed through expression validation. These hub genes were then employed to construct a nomogram and conduct enrichment, immune, and correlation analyses. An additional validation of hub genes was performed through in vitro cell experiments. Results: SLC3A2 and PDLIM1 were designated as hub genes, displaying excellent diagnostic performance. PDLIM1 exhibited low expression in early chondrocyte differentiation, rising significantly in the late stage, while SLC3A2 showed high overall expression, declining in the late differentiation stage. Cellular experiments corroborated the correlation of SLC3A2 and PDLIM1 with chondrocyte inflammation. Conclusions: Two hub genes, SLC3A2 and PDLIM1, were identified in relation to disulfidptosis, providing potential directions for diagnosing and treating OA. [ABSTRACT FROM AUTHOR]

Purpose: Over the past 24 years, significant advancements have been made in applying artificial intelligence (AI) to musculoskeletal (MSK) diseases. However, there is a lack of analytical and descriptive investigations on the trajectory, essential research directions, current research scenario, pivotal focuses, and future perspectives. This research aims to provide a thorough update on the progress in AI for MSK diseases over the last 24 years. Methods: Data from the Web of Science database, covering January 1, 2000, to March 1, 2024, was analyzed. Using advanced analytical tools, we conducted comprehensive scientometric and visual analyses. Results: The findings highlight the predominant influence of the USA, which accounts for 28.53% of the total publications and plays a key role in shaping research in this field. Notable productivity was seen at institutions such as the University of California, San Francisco, Harvard Medical School, and Seoul National University. Valentina Pedoia is identified as the most prolific contributor. Scientific Reports had the highest number of publications in this area. The five most significant diseases are joint diseases, bone fractures, bone tumors, cartilage diseases, and spondylitis. Conclusion: This comprehensive scientometric assessment benefits both experienced researchers and newcomers, providing quick access to essential information and fostering the development of innovative concepts in this field. [ABSTRACT FROM AUTHOR]

Aerosol jet printing has the potential to fabricate fine features on various substrates due to its large stand-off distance. However, the presence of overspray and instability, particularly at high printing resolutions, has limited its widespread application. In this study, we introduce an efficient approach called annular acoustic focusing for aerosol jet printing. By determining the optimal focusing frequency (5.8 MHz) for silver nanoparticles using a particle ejection model, we achieve precise and stable printing. We also propose a modified print nozzle geometry, resulting in ultrafine traces (line width < 6 μm, overspray < 0.1 μm). Compared to printing without acoustic focusing, the line width of the traces decreases to 60 ± 5% while their conductivity increases to 180 ± 5%. Additionally, several 8 h experiments demonstrate excellent printing stability. This research opens up possibilities for the fabrication of conformal electronics with high precision and improved conductivity using aerosol jet printing. Overspray and instability at high resolution has limited the widespread application of aerosol jet printing. Here, authors introduce an annular acoustic field coupled with a modified nozzle geometry, resulting in fine traces (line width <6 μm, overspray < 0.1 μm) with excellent printing stability. [ABSTRACT FROM AUTHOR]

The rise of social media has changed how people view connections. Machine Learning (ML)-based sentiment analysis and news categorization help understand emotions and access news. However, most studies focus on complex models requiring heavy resources and slowing inference times, making deployment difficult in resource-limited environments. In this paper, we process both structured and unstructured data, determining the polarity of text using the TextBlob scheme to determine the sentiment of news headlines. We propose a Stochastic Gradient Descent (SGD)-based Ridge classifier (RC) for blending SGDR with an advanced string processing technique to effectively classify news articles. Additionally, we explore existing supervised and unsupervised ML algorithms to gauge the effectiveness of our SGDR classifier. The scalability and generalization capability of SGD and L2 regularization techniques in RCs to handle overfitting and balance bias and variance provide the proposed SGDR with better classification capability. Experimental results highlight that our string processing pipeline significantly boosts the performance of all ML models. Notably, our ensemble SGDR classifier surpasses all state-of-the-art ML algorithms, achieving an impressive 98.12% accuracy. McNemar's significance tests reveal that our SGDR classifier achieves a 1% significance level improvement over K-Nearest Neighbor, Decision Tree, and AdaBoost and a 5% significance level improvement over other algorithms. These findings underscore the superior proficiency of linear models in news categorization compared to tree-based and nonlinear counterparts. This study contributes valuable insights into the efficacy of the proposed methodology, elucidating its potential for news categorization and sentiment analysis. [ABSTRACT FROM AUTHOR]