A research team has developed a novel approach combining refined spatial competition mapping with powerful ensemble learning to predict maximum crown width height (HMCW) in Chinese fir with unprecedented accuracy.

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.

In order to improve the diagnostic accuracy of deep-learning AI algorithms, models require larger amounts of high-quality training data, which presents a significant burden for pathologists or radiologists that diagnose disease based on images composed of billions of pixels. Researchers have developed a method to imitate the expertise of pathologists by tracking their eye movements while diagnosing whole slide images. This data helps scientists train AI models to more accurately identify regions of interest and better classify tissue samples based on the behavior of highly experienced and trained professionals with little to no additional burden placed on these providers.

An artificial intelligence (AI)-assisted model that combines a patient’s MRI, biochemical, and clinical information shows preliminary promise in improving predictions of whether their knee osteoarthritis may soon worsen. Ting Wang of Chongqing Medical University, China, and colleagues present this model August 21^st in the open-access journal PLOS Medicine.

NASA’s Artemis II mission, set to send four astronauts on a nearly 10-day mission around the Moon and back, will advance the agency’s goal to land astronauts at the Moon’s south polar region and will help set the stage for future crewed Mars missions. While the Artemis II crew will be the first humans to test NASA’s Orion spacecraft in space, they will also conduct science investigations that will inform future deep space missions, including a lunar science investigation as Orion flies about 4,000 to 6,000 miles from the Moon’s surface. “Artemis II is a chance for astronauts to implement the lunar science skills they've developed in training,” said Kelsey Young, Artemis II lunar science lead at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “It’s also an opportunity for scientists and the engineers in mission control to collaborate during real-time operations, building on the years of testing and simulations that our teams have done together.”