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Deep learning models facilitate the discovery, engineering, and design of novel genome editors.Protein structure prediction and homolog search are effective methods for mining genome editors that have long evaded detection by traditional sequence-based algorithms.AI-driven approaches turn protein engineering from a needle-in-a-haystack challenge into a tractable process, enabling the optimization of properties of genome editors through efficient in silico engineering (i.e., finding the most stabilizing mutations).Computational protein design methods now bypass evolutionary constraints, creating next-generation genome editors with functions unprecedented in nature. Artificial intelligence (AI) has revolutionized life sciences, driving transformative advances in engineering clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas)-based genome editors for therapeutic and agricultural applications. Recent breakthroughs demonstrate how deep learning accelerates the discovery, engineering, and design of next-generation genome editing tools. In this review, we explore how AI-driven approaches are supercharging genome editing in three aspects: (i) structure-based methods for discovering novel genome editors neglected by conventional methods, (ii) engineering genome editors with enhanced properties, and (iii) the de novo design of entirely new genome editors endowed with bespoke functions. Finally, we discuss the current challenges and envision the future potential of data-driven AI to unlock new possibilities in genome editing, catalyzing innovations across biology and biotechnology. Artificial intelligence (AI) has revolutionized life sciences, driving transformative advances in engineering clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas)-based genome editors for therapeutic and agricultural applications. Recent breakthroughs demonstrate how deep learning accelerates the discovery, engineering, and design of next-generation genome editing tools. In this review, we explore how AI-driven approaches are supercharging genome editing in three aspects: (i) structure-based methods for discovering novel genome editors neglected by conventional methods, (ii) engineering genome editors with enhanced properties, and (iii) the de novo design of entirely new genome editors endowed with bespoke functions. Finally, we discuss the current challenges and envision the future potential of data-driven AI to unlock new possibilities in genome editing, catalyzing innovations across biology and biotechnology.

Allogeneic cultured epidermis (allo-CE) is a cultured keratinocyte sheet manufactured from donor cells and promotes wound healing when used in deep dermal burns, donor sites, and chronic ulcers and serves as a wound dressing. Allo-CE is usually cryopreserved to be ready to use. However, the cryopreservation procedure will damage the cell viability, and the influence of Allo-CE, according to its viability or wound healing process, has not been evaluated sufficiently. In this study, we aimed to prove the influence of keratinocyte viability contained in allo-CEs on wound healing. We prepared CEs with Green's method using keratinocytes obtained from a polydactyly patient and then prepared four kinds of CEs with different cell viabilities [fresh, cryopreserved, frozen, and FT (freeze and thaw)]. The cell viabilities of fresh, cryopreserved, frozen, and FT CEs were 95.7%, 59.9%, 16.7%, and 0.0%, respectively. The four CEs had homogeneous characteristics, except for small gaps found in the FT sheet by transmission electron microscopy observation. The four CEs were applied on the full-thickness skin defect of diabetic mice (BKS.Cg-Dock 7.sup.m +/+ Lepr.sup.db /Jcl), and the wound area and neoepithelium length were evaluated on days 4, 7, and 14. As a result, FT CEs without viable cells similarly promoted epithelialization on days 4 and 7 (p<0.05) and accelerated wound closure on day 7 (p<0.01) as fresh CEs compared with the control group. In conclusion, the promoting effect of allo-CE on wound healing does not depend on cell viability. Lyophilized CEs may be a suitable wound dressing with a long storage period at room temperature.

时空索引是时空数据存储和管理的关键技术之一，基于空间填充曲线（space filling curve，SFC）的索引方法近年来受到了广泛关注。然而对于矢量数据，现有索引方法多侧重于空间索引的实现，难以同时顾及时间查询和空间查询的效率，且对于非点要素（线要素与面要素），确定最优的索引级别一直是难点所在。为此，本文面向对等网络环境，提出一种自适应层级的时空索引构建方法。首先提出了基于分区键和分区内排序键组合策略的时空信息联合编码，然后据此设计了点要素、非点要素的时空表达结构，最后设计了多层级树结构以构建时空索引MLS3（multi-level sphere 3），并基于地理实体时间粒度及空间密度等特征自适应确定其最优索引层级。利用轨迹（点要素）、公路（线要素）和建筑物（面要素）实际数据进行了试验。试验结果表明，相比GeoMesa提出的XZ3时空索引，本文索引方法可有效解决非点要素的时空表达及层级划分问题，在避免存储热点的同时实现更为高效的时空检索。