The RhWRKY33a-RhPLATZ9 regulatory module delays petal senescence by suppressing rapid reactive oxygen species accumulation in rose flowers.

Redox homeostasis in plant cells is critical for maintaining normal growth and development because reactive oxygen species (ROS) can function as signaling molecules or toxic compounds. However, how plants fine-tune redox homeostasis during natural or stress-induced senescence remains unclear. Cut roses (Rosa hybrida), an economically important ornamental product worldwide, often undergo stress-induced precocious senescence at the post-harvest bud stage. Here, we identified RhPLATZ9, an age- and dehydration-induced PLATZ (plant AT-rich sequence and zinc-binding) protein, and determined that it functions as a transcriptional repressor in rose flowers during senescence. We also showed that RhWRKY33a regulates RhPLATZ9 expression during flower senescence. RhPLATZ9-silenced flowers and RhWRKY33a-silenced flowers showed accelerated senescence, with higher ROS contents than the control. By contrast, overexpression of RhWRKY33a or RhPLATZ9 delayed flower senescence, and overexpression in rose calli showed lower ROS accumulation than the control. RNA-sequencing analysis revealed that apoplastic NADPH oxidase genes (RhRbohs) were enriched among the upregulated differentially expressed genes in RhPLATZ9-silenced flowers compared to wild-type flowers. Yeast one-hybrid assays, electrophoretic mobility shift assays, dual luciferase assays and chromatin immunoprecipitation quantitative PCR confirmed that the RhRbohD gene is a direct target of RhPLATZ9. These findings suggest that the RhWRKY33a-RhPLATZ9-RhRbohD regulatory module acts as a brake to help maintain ROS homeostasis in petals and thus antagonize age- and stress-induced precocious senescence in rose flowers.

Controllable Damping Magnetorheological Elastomer Meniscus.

The human healthy meniscus fulfills key biomechanical functions in the tibiofemoral (knee) joint. Meniscal injury leads to an increased risk for symptomatic osteoarthritis. In order to prevent osteoarthritis, many researchers have put efforts into developing new-type meniscal substitute materials. In this study, MRI data of the human knee joint is obtained by CT scanning, and a three-dimensional finite element model of the meniscus is established. Compressive forces of 400 N, 600 N, 800 N, and 1000 N are selected to complete the meniscus modeling and finite element simulation analysis of the meniscus by ANSYS; at the same time, the compressive force and compressive displacement of the magnetorheological elastomer are controlled by changing the current size. The results show that the compressive force and compressive displacement of the magnetorheological elastomer can be controlled by an electric current, so as to adapt to the required mechanical properties of the meniscus under external complex loads and provide a theoretical and experimental basis for clinical meniscus replacement.

Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis.

Cancer has become a major threat to global health care. With the development of computer science, artificial intelligence (AI) has been widely applied in histopathological images (HI) analysis. This study analyzed the publications of AI in HI from 2001 to 2021 by bibliometrics, exploring the research status and the potential popular directions in the future. A total of 2844 publications from the Web of Science Core Collection were included in the bibliometric analysis. The country/region, institution, author, journal, keyword, and references were analyzed by using VOSviewer and CiteSpace. The results showed that the number of publications has grown rapidly in the last five years. The USA is the most productive and influential country with 937 publications and 23,010 citations, and most of the authors and institutions with higher numbers of publications and citations are from the USA. Keyword analysis showed that breast cancer, prostate cancer, colorectal cancer, and lung cancer are the tumor types of greatest concern. Co-citation analysis showed that classification and nucleus segmentation are the main research directions of AI-based HI studies. Transfer learning and self-supervised learning in HI is on the rise. This study performed the first bibliometric analysis of AI in HI from multiple indicators, providing insights for researchers to identify key cancer types and understand the research trends of AI application in HI.