CRISPR-Cas9 technology has been widely used for genome engineering. Its RNA-guided endonuclease Cas9 binds specifically to target DNA and then cleaves the two DNA strands with HNH and RuvC nuclease domains. However, structural information regarding the DNA cleavage-activating state of two nuclease domains remains sparse. Here, we report a 5.2 Å cryo-EM structure of Cas9 in complex with sgRNA and target DNA. This structure reveals a conformational state of Cas9 in which the HNH domain is closest to the DNA cleavage site. Compared with two known HNH states, our structure shows that the HNH active site moves toward the cleavage site by about 25 and 13 Å, respectively. In combination with EM-based molecular dynamics simulations, we show that residues of the nuclease domains in our structure could form cleavage-compatible conformations with the target DNA. Together, these results strongly suggest that our cryo-EM structure resembles a DNA cleavage-activating architecture of Cas9.

In 3D (bio)printing, it is critical to optimize the printing conditions to obtain scaffolds with designed structures and good uniformities. Traditional approaches for optimizing the parameters oftentimes rely on the prior knowledge of the operators and tedious optimization experiments, which can be both time‐consuming and labor‐intensive. Moreover, with the rapid increase in the types of biomaterial inks and the geometrical complexities of the scaffolds to be fabricated, such a traditional strategy may prove less effective. To address the challenge, an artificial intelligence‐assisted high‐throughput printing‐condition‐screening system (AI‐HTPCSS) is proposed, which is composed of a programmable pneumatic extrusion (bio)printer and an AI‐assisted image‐analysis algorithm. Based on the AI‐HTPCSS, the printing conditions for obtaining uniformly structured hydrogel architectures are screened in a high‐throughput manner. The results show that the scaffolds printed under the optimized conditions demonstrate satisfying mechanical properties, in vitro biological performances, and efficacy in accelerating the diabetic wound healing in vivo. The unique AI‐HTPCSS is expected to offer an enabling platform technology on streamlining the manufacturing of tissue‐engineering scaffolds through 3D (bio)printing techniques in the future.

Motivation Single-particle cryo-electron microscopy (cryo-EM) has become a powerful technique for determining 3D structures of biological macromolecules at near-atomic resolution. However, this approach requires picking huge numbers of macromolecular particle images from thousands of low-contrast, high-noisy electron micrographs. Although machine-learning methods were developed to get rid of this bottleneck, it still lacks universal methods that could automatically picking the noisy cryo-EM particles of various macromolecules. Results Here, we present a deep-learning segmentation model that employs fully convolutional networks trained with synthetic data of known 3D structures, called PARSED (PARticle SEgmentation Detector). Without using any experimental information, PARSED could automatically segment the cryo-EM particles in a whole micrograph at a time, enabling faster particle picking than previous template/feature-matching and particle-classification methods. Applications to six large public cryo-EM datasets clearly validated its universal ability to pick macromolecular particles of various sizes. Thus, our deep-learning method could break the particle-picking bottleneck in the single-particle analysis, and thereby accelerates the high-resolution structure determination by cryo-EM. Availability and implementation The PARSED package and user manual for noncommercial use are available as Supplementary Material (in the compressed file: parsed_v1.zip). Supplementary information Supplementary data are available at Bioinformatics online.

Compared with the indices in frequency domain, the Poincaré indices were more sensitive and accurate in UST measurement of ANS during exercise. The results demonstrated that the UST method could characterize the dynamic changing tendency of ANS during and after exercise and quantify the differences of changes in ANS induced by exercise with different intensities. In particular, the vagal branch functioned dominantly in controlling HR in S0 but the effect of the sympathetic branch on HR enhanced with the increase of exercise intensity. In addition, the transient changes of ANS related with the sudden onset of exercise could also be reflected, despite perhaps being limited by the computation window width to some extent. Thus, the consecutive UST Poincaré indices could provide a feasible and simple method to measure quantitatively the exercise-induced dynamic changes in ANS.