5 results match your criteria: "Academy for Advanced Interdisciplinary Studies Peking University Beijing China.[Affiliation]"
Cogn Psychol
January 2025
School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health Peking University Beijing China; Peking-Tsinghua Center for Life Sciences Peking University Beijing China; PKU-IDG/McGovern Institute for Brain Research Peking University Beijing China; State Key Laboratory of General Artificial Intelligence Peking University, Beijing, China; Chinese Institute for Brain Research Beijing China. Electronic address:
Some seemingly irrational decision behaviors (anomalies), once seen as flaws in human cognition, have recently received explanations from a rational perspective. The basic idea is that the brain has limited cognitive resources to process the quantities (e.g.
View Article and Find Full Text PDFImeta
December 2024
Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China.
The Conference 2024 provides a platform to promote the development of an innovative scientific research ecosystem for microbiome and One Health. The four key components - Technology, Research (Biology), Academic journals, and Social media - form a synergistic ecosystem. Advanced technologies drive biological research, which generates novel insights that are disseminated through academic journals.
View Article and Find Full Text PDFIn this work, we introduced a siderophore information database (SIDERTE), a digitized siderophore information database containing 649 unique structures. Leveraging this digitalized data set, we gained a systematic overview of siderophores by their clustering patterns in the chemical space. Building upon this, we developed a functional group-based method for predicting new iron-binding molecules with experimental validation.
View Article and Find Full Text PDFRecognizing and classifying multiple morphological features, such as patterns, sizes, and textures, can provide a comprehensive understanding of their variability and phenotypic evolution. Yet, quantitatively measuring complex morphological characters remains challenging.We provide a machine learning-based pipeline (SVMorph) to consider and classify complex morphological characters in multiple organisms that have either small or large datasets.
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