Publications by authors named "M Buehler"
A key challenge in artificial intelligence (AI) is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. In this work, SciAgents, an approach that leverages three core concepts is presented: (1) large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning capabilities. Applied to biologically inspired materials, SciAgents reveals hidden interdisciplinary relationships that were previously considered unrelated, achieving a scale, precision, and exploratory power that surpasses human research methods.
View Article and Find Full Text PDFSnake venoms are complex mixtures of toxic proteins that hold significant medical, pharmacological and evolutionary interest. To better understand the genetic diversity underlying snake venoms, we developed VenomCap, a novel exon-capture probe set targeting toxin-coding genes from a wide range of elapid snakes, with a particular focus on the ecologically diverse and medically important subfamily Hydrophiinae. We tested the capture success of VenomCap across 24 species, representing all major elapid lineages.
View Article and Find Full Text PDFArticle Synopsis
- CAR T-cell therapies targeting specific antigens have been approved for treating B- and plasma-cell cancers, but their efficacy is limited by low antigen expression and safety issues due to the lack of control over their activity.
- A new approach, called adaptor-CAR (AdFITC-CAR) T-cells, was developed to target a broader range of AML antigens and allow for modulation of T-cell activity, potentially avoiding damage to healthy cells.
- Experiments showed that AdFITC-CAR T-cells, especially when combined with multiple adaptor proteins, significantly improved the killing of AML cells and demonstrated effective therapy in mouse models, suggesting a promising advance in treating AML.
Article Synopsis
- Understanding how cells fold into structures during development, like embryogenesis, is a key question in biology, but predicting cell behavior remains difficult.
- A new geometric deep-learning model captures complex cell interactions and represents multicellular data using a unified graph structure.
- This model allows for 4-D morphological sequence alignment and predicts cell rearrangements with high precision, suggesting that cell shapes and junctions play crucial roles in development.