BUFFALO, NY- August 22, 2025 – A new research paper was published in Volume 16 of Genes & Cancer on August 6, 2025, titled “Epstein-Barr virus nuclear antigen 1 upregulates Derlin1 and PSMD10 expression in HeLa cells.”

Fat metabolism in Caenorhabditis elegans is commonly studied, but linking fat molecules to specific body structures has remained difficult. Now, researchers have developed a microfluidics-based method that preserves internal anatomy while preparing worm sections for imaging. Using mass spectrometry imaging and fat-specific staining, they visualized the spatial distribution of lipids across organs like the intestine and embryos. This technique also enables 3D reconstruction, revealing how fat molecules are organized within individual worms in remarkable detail.

Launched in 2022, the SynBio Challenges is an annual initiative that empowers young innovators to tackle global challenges through synthetic biology. By promoting interdisciplinary collaboration, it drives creative and sustainable solutions for a better future.

Plants produce all amino acids essential for human life. This commonly occurs in specialised cell organelles, so-called plastids. A research team headed by Heinrich Heine University Düsseldorf (HHU) has now decoded the mechanism by which the plants distribute these amino acids within their organisms. In the scientific journal Nature Plants, the researchers describe the mechanism and the class of transport proteins used for this process. The findings could potentially contribute to breeding crop plants with a higher content of essential amino acids and thus improved nutritional quality.

Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data. To date, most text mining methods are restricted to specific literature information, resulting in incomplete knowledge graphs. Here, we report a method that combines citation analysis with topic modeling to describe the hidden development patterns in the history of science. Leveraging this method, we construct a knowledge graph in the field of Raman spectroscopy. The traditional Latent DirichletAllocation model is chosen as the baseline model for comparison to validate the performance of our model. Our method improves the topic coherence with a minimum growth rate of 100% compared to the traditional text mining method. It outperforms the traditional text mining method on the diversity, and its growth rate ranges from 0 to 126%. The results show the effectiveness of rule-based tokenizer we designed in solving the word tokenizer problem caused by entity naming rules in the field of chemistry. It is versatile in revealing the distribution of topics, establishing the similarity and inheritance relationships, and identifying the important moments in the history of Raman spectroscopy. Our work provides a comprehensive tool for the science of science research and promises to offer new insights into the historical survey and development forecast of a research field.