Single-particle cryo-EM has transformed structural biology but still faces challenges in resolving conformational heterogeneity at atomic resolution. Existing cryo-EM heterogeneity analysis methods either lack atomic details or tend to subject to overfitting due to image noise and limited information in single views. To obtain atomic detailed multiple conformations and make full use of particle images of different orientations, we present here CryoDyna, a deep learning framework to infer macromolecular dynamics directly from 2D projections by integrating cross-view attention and multi-scale deformation modeling. Combining coarse-grained MARTINI representation with atomic backmapping, CryoDyna achieves near-atomic interpretation of protein conformational landscapes. Validated on multiple simulated and experimental datasets, CryoDyna demonstrates improved modeling accuracy and robustly recovers multi-scale complex structure changes hidden in the cryo-EM particle stacks. As examples, we generated protein-RNA coordinated motions, resolved dynamics in the unseen region of RAG signal end complex, mapped translocating ribosome states in a one-shot manner, and revealed step-wise closure of a membrane-anchored protein multimer. This work bridges the gap between cryo-EM heterogeneity analysis and atomic-scale structural dynamics, offering a promising tool for exploration of complex biological mechanisms.

In clinical practice, multiple biomarkers are used for disease diagnosis, but their individual accuracies are often suboptimal, with only a few proving directly relevant. Effectively selecting and combining biomarkers can significantly improve diagnostic accuracy. Existing methods often optimize metrics like the Area Under the ROC Curve (AUC) or the Youden index. However, optimizing AUC does not yield estimates for optimal cutoff values, and the Youden index assumes equal weighting of sensitivity and specificity, which may not reflect clinical priorities where these metrics are weighted differently. This highlights the need for methods that can flexibly accommodate such requirements. In this paper, we present a novel framework for selecting and combining biomarkers to maximize a weighted version of the Youden index. We introduce a smoothed estimator based on the weighted Youden index and propose a penalized version using the SCAD penalty to enhance variable selection. To handle the non-convexity of the objective function and the non-smoothness of the penalty, we develop an efficient algorithm, also applicable to other non-convex optimization problems. Simulation studies demonstrate the performance and efficiency of our method, and we apply it to construct a diagnostic scale for dermatitis.