In recent years, magnetically controlled microrobots have garnered significant attention. This paper presents the H-robot, a self-designed microrobot featuring an innovative structure. The H-robot features a honeycomb porous spherical design specifically engineered to enhance cargo capacity. A new dynamic model for this structure has been developed for low Reynolds number fluid environments, along with a robust backstepping sliding mode control (RBSMC) strategy. Experiments were conducted in a calibrated magnetic field generated by a magnetic field generator to achieve precise motion control. The results demonstrate that the H-robot accurately tracks standard trajectories, with root mean square errors (RMSE) of$9.09 \times 10^{-4} \mathbf{~ m}$for the Number-8 path and$8.29 \times 10^{-4} \mathbf{~ m}$for the S-shaped path. Additionally, the proposed resistance model enhances tracking accuracy by 73.61% compared to traditional models, effectively adjusting the dynamic behavior of the H-robot in low Reynolds number fluids and significantly improving its motion performance. Finally, path planning experiments in a maze demonstrate the H-robot's ability to navigate and avoid obstacles.

Designing regulatory DNA sequences that achieve precise cell-type-specific gene expression is crucial for advancements in synthetic biology, gene therapy and precision medicine. Although transformer-based language models (LMs) can effectively capture patterns in regulatory DNA, their generative approaches often struggle to produce novel sequences with reliable cell-specific activity. Here, we introduce Ctrl-DNA, a novel constrained reinforcement learning (RL) framework tailored for designing regulatory DNA sequences with controllable cell-type specificity. By formulating regulatory sequence design as a biologically informed constrained optimization problem, we apply RL to autoregressive genomic LMs, enabling the models to iteratively refine sequences that maximize regulatory activity in targeted cell types while constraining off-target effects. Our evaluation on human promoters and enhancers demonstrates that Ctrl-DNA consistently outperforms existing generative and RL-based approaches, generating high-fitness regulatory sequences and achieving state-of-the-art cell-type specificity. Moreover, Ctrl-DNA-generated sequences capture key cell-type-specific transcription factor binding sites (TFBS), short DNA motifs recognized by regulatory proteins that control gene expression, demonstrating the biological plausibility of the generated sequences.

Unlocking deep and interpretable biological reasoning from complex genomic data remains a major AI challenge limiting scientific progress. While current DNA foundation models excel at representing sequences, they struggle with multi-step reasoning and lack transparent, biologically meaningful explanations. BioReason addresses this by tightly integrating a DNA foundation model with a large language model (LLM), enabling the LLM to directly interpret and reason over genomic information. Through supervised fine-tuning and reinforcement learning, BioReason learns to produce logical, biologically coherent deductions. It achieves major performance gains, boosting KEGG-based disease pathway prediction accuracy from 86% to 98% and improving variant effect prediction by an average of 15% over strong baselines. BioReason can reason over unseen biological entities and explain its decisions step by step, offering a transformative framework for interpretable, mechanistic AI in biology. All data, code, and checkpoints are available at [https://github.com/bowang-lab/BioReason](https://github.com/bowang-lab/BioReason).

This paper presents a novel snake robot with the docking function, which can help the snake robots to connect with each other to achieve a stronger one with double length and double degrees of freedom. First, the mechanical design of the snake robot with docking function is introduced, including the body link and the head-tail passive docking mechanical structure. Second, the control system is built, and the control strategies of locomotion and docking are separately proposed. Then, the visual perception function is implemented for the target recognition during the docking process. Finally, the prototype is developed. The mobility and the docking function are fully verified and analyzed through the physical experiments.