Psittacosaurus was a genus of herbivorous dinosaurs that lived in the Early Cretaceous. Since the first report of Psittacosaurus discoveries in Mongolia a century ago, at least 12 valid species have been recognized to date. The unearthed fossils cover a complete developmental sequence from newly hatched juveniles to adult individuals. Recently, a joint research team from the Dinosaur Evolution Research Center of Jilin University and the University of Toronto Mississauga analyzed 13 juvenile Psittacosaurus fossils from the same nest, all containing gastroliths. The findings have significantly reshaped the traditional understanding of their feeding behavior—even individuals under one year of age ingested gastroliths to aid in digesting tough, fibrous plants. Lithological analysis of these gastroliths indicates that the juveniles likely obtained the stones locally, with their activity confined to the western Liaoning region and showing no evidence of long-distance migration. This study highlights a relatively stable feeding strategy throughout the life cycle of  Psittacosaurus, providing key insights into reconstructing the paleoecological evolution of early ceratopsians. The research has been published in SCIENCE CHINA Earth Sciences.

In new research from the University of Chicago Pritzker School of Molecular Engineering, Argonne National Laboratory and Purdue University, the AI-driven robotic lab Polybot designed and built new polymers that hit precise, targeted shades of orange and green – a task that takes researchers decades of experience to learn – in just 72 hours.

Neuroimaging analysis in brain disorders faces a persistent challenge: brain signals are complex and high-dimensional, while high-quality labeled datasets remain limited. This review article systematically examines how self-supervised learning can help address that gap by learning meaningful representations directly from unlabeled neuroimaging data. It covers major methodological families, including contrastive, generative, and hybrid generative-contrastive approaches, and discusses their applications in functional MRI, EEG, and multimodal brain network analysis.

The review argues that self-supervised learning offers more than annotation efficiency. It may enable more transferable and clinically useful representations for disease screening, diagnosis, and prognosis across heterogeneous datasets and disorders. At the same time, interpretability, data heterogeneity, missing modalities, and clinical validation remain major barriers. Future work will likely focus on stronger multimodal fusion, better cross-site generalization, and more clinically adaptable model design.