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Establishing a strategy for realizing programmed self-assembly is critical in manufacturing materials with functional hybrid structures. In this work, we introduce a robust methodology for enabling multi-component self-assembly using the concept of chirality-directed self-assembly. A specific combination of heterochiral Zn(II) methylene bis(oxazoline) (BOX) complexes can be selectively generated when combinations of enantiomers of chiral BOX ligands are mixed in the presence of Zn(Oac)2. The resulting Zn(II) BOX complexes, unlike non-covalent bonds, are highly stable and stay intact at elevated temperatures, yet can be reversibly disintegrated under mild conditions using EDTA. This approach can be easily applied to multi-functionalize various solid supports enabling the one-pot generation of multi-functional hybrid structures. 

Resin-immobilized catalysts were prepared through chirality-driven self-assembly. The method allows the resin-immobilized catalyst to be regenerated under mild conditions and in situ catalyst exchange to be carried out quantitatively. The uniqueness of the methodology was demonstrated by the preparation of a catalyst for TEMPO oxidation as well as a two-step sequential TEMPO oxidation/aldol condensation sequence enabled by facile catalyst exchange. 

A strategy to build Janus dendrimers via the chirality-directed self-assembly of heteroleptic Zn( ii ) BOX complexes is reported. The method allows quantitative synthesis of Janus dendrimers in situ without the need for purifications. Each dendritic domain of the Janus dendrimers can be recycled upon disassembly at the focal point. 

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.