Cell-free gene expression (CFE) systems enable transcription and translation using crude cellular extracts, offering a versatile platform for synthetic biology by eliminating the need to maintain living cells. However, Such systems are constrained by cumbersome composition, high costs, and limited yields due to numerous additional components required to maintain biocatalytic efficiency. Here, we introduce DropAI, a droplet-based, AI-driven screening strategy designed to optimize CFE systems with high throughput and economic efficiency. DropAI employs microfluidics to generate picoliter reactors and utilizes a fluorescent color-coding system to address and screen massive chemical combinations. The in-droplet screening is complemented by in silico optimization, where experimental results train a machine-learning model to estimate the contribution of the components and predict high-yield combinations. By applying DropAI, we significantly simplified the composition of an Escherichia coli-based CFE system, achieving a fourfold reduction in the unit cost of expressed superfolder green fluorescent protein (sfGFP). This optimized formulation was further validated across 12 different proteins. Notably, the established E. coli model is successfully adapted to a Bacillus subtilis-based system through transfer learning, leading to doubled yield through prediction. Beyond CFE, DropAI offers a high-throughput and scalable solution for combinatorial screening and optimization of biochemical systems.

3D disordered fibrous network structures (3D‐DFNS), such as cytoskeletons, collagen matrices, and spider webs, exhibit remarkable material efficiency, lightweight properties, and mechanical adaptability. Despite their widespread in nature, the integration into engineered materials is limited by the lack of study on their complex architectures. This study addresses the challenge by investigating the structure‐property relationships and stability of biomimetic 3D‐DFNS using large datasets generated through procedural modeling, coarse‐grained molecular dynamics simulations, and machine learning. Based on these datasets, a network deep reinforcement learning (N‐DRL) framework is developed to optimize its stability, effectively balancing weight reduction with the maintenance of structural integrity. The results reveal a pronounced correlation between the total fiber length in 3D‐DFNS and its mechanical properties, where longer fibers enhance stress distribution and stability. Additionally, fiber orientation is also considered as a potential factor influencing stress growth values. Furthermore, the N‐DRL model demonstrates superior performance compared to traditional approaches in optimizing network stability while minimizing mass and computational cost. Structural integrity is significantly improved through the addition of triple junctions and the reduction of higher‐order nodes. In summary, this study leverages machine learning to optimize biomimetic 3D‐DFNS, providing novel insights into the design of lightweight, high‐strength materials.

发光纤维材料因其在柔性显示、智能纺织品等领域展现出的巨大潜力而受到广泛关注。在发光纤维应用的众多影响因素中，力学性能的优化与发光色彩的多样化始终是研究的重点。本研究采用了将芯层单纤维加捻与皮层封装处理相结合的策略，显著增强了纤维的力学性能，使其断裂强度提升至原来的2倍，韧性提高了2.3倍。制备的发光纤维材料展示了出色的性能稳定性，能够承受打结、编织等处理，在水下弯折或经过酒精清洗后，仍能保持正常的发光性能。此外，本研究通过利用发光层与封装层的发光效果叠加原理，实现了纤维的发光色彩的丰富化，其在1931 CIE色域图中的色域覆盖超过30%，体现出灵活的发光颜色可调性。同时，采用并联点亮策略和加捻结构，实现了在单根纱线上大跨度颜色的的独立或共同展示，满足了在单一纱线上灵活切换多种色彩风格的需求，所制备的发光纱线鞋面能够实现8种色彩风格的任意切换。本研究中所制备的发光纤维纱线，凭借其良好的力学性能、优异的稳定性及灵活的发光可编辑性，在军事伪装、柔性显示设备、可穿戴器件、时尚装饰等领域展现出了良好的应用前景。

Reconstructing transcription–translation-coupled DNA replication (TTcDR) in artificial systems is crucial for creating synthetic life; however, existing approaches face limitations mainly due to their reliance on purified biological components. Here, we introduce LoopReX, a cell-free system that reconstitutes TTcDR using crude Escherichia coli extracts, offering a more complex native biological environment. LoopReX leverages a minimal machinery composed of phi29 DNA polymerase and T7 RNA polymerase, with the latter facilitating DNA replication initiation through the generation of primer RNAs. Using machine learning, we optimize LoopReX to enhance the efficiency of both DNA replication and protein expression, achieving scalable, sustainable genetic flow and high-yield protein production with robust iterative performance. Furthermore, artificial nucleoids, autonomously formed through CipB-based compartmentalization, improve DNA spatial organization and support multiple biological functions. This work advances the construction of artificial life by reconstituting TTcDR within a single, scalable, and functionalized system, opening exciting possibilities for synthetic biology, biotechnology, and bio-hybrid applications.

This study presents a comprehensive analysis of the vascular bundle morphology, tangential and radial diameters, and distribution frequency of different parts of Oligostachyum sulcatum, elucidating their structural and functional significance. Electron microscopy images revealed distinct vascular bundle characteristics in the different parts, including the vascular bundles in both parts of the rhizomes, the middle parts of the internodes, and the middle and inner parts of the branches, which were semi-open. The vascular bundles in the outer parts of both internodes and branches were semi-differentiated and undifferentiated. The vascular bundles in the inner parts of internodes were open. Statistical analysis showed significant variations in tangential and radial diameters among these parts, reflecting their diverse mechanical and physiological functions. The internodes exhibited the largest tangential and radial diameters, suggesting a critical role in mechanical support. In contrast, the branches had the smallest diameter, indicating that vascular bundle differentiation is influenced by growth conditions. The vascular bundle frequency was the highest in branches and the lowest in inside-sticks. This study provides theoretical references for the adaptive strategies and growth regulation mechanisms of O. sulcatum.