Bintao He

Bintao He, Yiran Cheng, Hongjia Li et al.

Understanding protein flexibility and its dynamic interactions with other molecules is essential for studying protein function. Although cryogenic electron microscopy(cryo-EM) provides an opportunity to observe macromolecular dynamics directly, computational analysis of datasets mixing continuous and discrete structural states remains a formidable challenge. Here we introduce GaussianEM, a Gaussian-based pseudo-atomic framework that simultaneously resolves compositional and conformational heterogeneity from cryo-EM images. GaussianEM employs a dual-encoder-single-decoder architecture to decompose images into learnable Gaussian components, with variability encoded through modulated parameters. This explicit parameterization yields a continuous, intuitive representation of conformational dynamics that inherently preserves local structural integrity. By modeling displacements in Gaussian space, we capture atomic-scale conformational landscapes, bridging density maps and all-atom models. In comprehensive experiments, GaussianEM successfully reconstructs complex compositional and conformational variability,and resolves previously unobserved details in public datasets. Quantitative evaluations further confirm its ability to capture broader conformational diversity without sacrificing structural fidelity.

Bintao He, Fa Zhang, Chenjie Feng et al.

Advances on cryo-electron imaging technologies have led to a rapidly increasing number of density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we propose a fast and accurate global and local cryo-electron microscopy density map alignment method CryoAlign, which leverages local density feature descriptors to capture spatial structure similarities. CryoAlign is the first feature-based EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences and robust estimation of alignment parameters. Extensive experimental evaluations demonstrate the superiority of CryoAlign over the existing methods in both alignment accuracy and speed.

He Liu, Changtao Miao, Xinjie Yang et al.

Large language models deployed in open-world applications require safety guardrails that are both robust to complex risks and efficient enough for low-latency runtime moderation. Existing guardrails face a practical trade-off between lightweight classification-based models, which are efficient but often struggle with concealed intent, ambiguous semantics, and borderline safety decisions, and reasoning-based guards, which improve judgment quality but introduce additional token generation and inference latency. We present DT-Guard, a content safety guardrail model based on a Reasoning-Active Training, Reasoning-Free Inference paradigm. The key idea is to use reasoning supervision during training while emitting only structured safety labels at inference time. DT-Guard formulates safety judgment as a progressive decision process, Intent - Category - Safety, and constructs an intent-driven dataset with intent labels, risk categories, safety labels, and structured reasoning trajectories. To further improve hard-case robustness, we propose Rollout-Guided Progressive Hard-Case Optimization (RG-PHO), which uses multi-rollout consistency to identify stably mastered, persistently failed, and preference-unstable samples, and applies targeted supervised and preference optimization accordingly. At inference time, DT-Guard directly generates structured labels without explicit reasoning traces, preserving deployment efficiency. Experiments on prompt-side and response-side safety benchmarks show that DT-Guard achieves average F1 scores of 0.886 and 0.870, respectively. With only a 4B backbone, it reaches a dual-side average F1 of 0.878, outperforming strong 8B guardrail baselines. These results demonstrate that reasoning supervision can be effectively internalized into low-latency safety discrimination.