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Adeno-associated virus (AAV) is a relatively safe and efficient vector for gene therapy. However, due to its 4.7-kb limit of cargo, SpCas9-mediated base editors cannot be packaged into a single AAV vector, which hinders their clinical application. The development of efficient miniature base editors becomes an urgent need. Un1Cas12f1 is a class II V-F-type CRISPR-Cas protein with only 529 amino acids. Although Un1Cas12f1 has been engineered to be a base editor in mammalian cells, the base-editing efficiency is less than 10%, which limits its therapeutic applications. Here, we developed hypercompact and high-efficiency base editors by engineering Un1Cas12f1, fusing non-specific DNA binding protein Sso7d, and truncating single guide RNA (sgRNA), termed STUminiBEs. We demonstrated robust A-to-G conversion (54% on average) by STUminiABEs or C-to-T conversion (45% on average) by STUminiCBEs. We packaged STUminiCBEs into AAVs and successfully introduced a premature stop codon on the PCSK9 gene in mammalian cells. In sum, STUminiBEs are efficient miniature base editors and could readily be packaged into AAVs for biological research or biomedical applications.

In this study, different modeling approaches used in panel data for performance forecast of transportation infrastructure are firstly reviewed, and the panel data models (PDMs) are highlighted for longitudinal data sets. The state-space specification of PDMs are proposed as a framework to formulate dynamic performance models for transportation facilities and panel data sets are used for estimation. The models could simultaneously capture the heterogeneity and update forecast through inspections. PDMs are applied to tackle the cross-section heterogeneity of longitudinal data, and PDMs in state-space forms are used to achieve the goal of updating performance forecast with new coming data. To illustrate the methodology, three classes of dynamic PDMs are presented in four examples to compare with two classes of static PDMs for a group of composite pavement sections in an airport in east China. Estimation results obtained by ordinary least square (OLS) estimator and system generalized method of moments (SGMM) are compared for two dynamic instances. The results show that the average root mean square errors of dynamic specifications are all significantly lower than those of static counterparts as prediction continues over time. There is no significant difference of prediction accuracy between state-space model and curve shifting model over a short time. In addition, SGMM does not obtain higher prediction accuracy than OLS in this case. Finally, it is recommended to specify the inspection intervals as several constants with integer multiples.