Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure-property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use.self-assembly | dipeptide hydrogels | machine learning H ydrogels that are cross-linked by three-dimensional networks of modified molecules can maintain a large amount of water without dissolving its own chemical structure, which is very similar to natural tissue. As a result of favorable biocompatibility, hydrogels have great potential in biomedical applications such as drug delivery, tissue engineering, sensing, and cell encapsulation (1-7). In the past few years, considerable attention has been directed toward the design of peptide-based hydrogels in particular, not only because of their favorable features such as easy synthesis, decoration, biodegradability, and high compatibility, but also due to their wide applications in the biological and medical fields (8)(9)(10)(11)(12)(13)(14). However, to the best of our knowledge, the prediction and design of peptide-based hydrogels is still challenging, which limits our research choices on peptide-based hydrogels (15,16). Therefore, the design strategy for hydrogels based on peptides is of great significance. Our aim is to reveal the relationship between molecular structure and hydrogel behavior, which can help us to predict and design peptide hydrogels with new chemical structures.There are approaches using molecular dynamics simulation to model the self-assembly behavior of peptides into different types of nanostructures, including nanofibers, which can subsequently form hydrogels (17)(18)(19). However, it is difficult to evaluate the actual prediction accuracy of the molecular dynamics simulation methods because only a few positive peptides were selected and synthesized to test whether they could form a hydrogel. Additionally, the current reported synthetic method on 9fluorenylmethyloxycarbonyl (Fmoc)-peptide is limited to the traditional peptide synthesis method, involving step-by-step protection and deprotection. Since a high-throughput peptide generation method is not available, our first ...

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Correlative microscopy is a methodology combining the functionality of light microscopy with the high resolution of electron microscopy and other microscopy technologies for the same biological specimen. In this paper, we propose an image registration method for correlative microscopy, which is challenging due to the distinct appearance of biological structures when imaged with different modalities. Our method is based on image analogies and allows to transform images of a given modality into the appearance-space of another modality. Hence, the registration between two different types of microscopy images can be transformed to a mono-modality image registration. We use a sparse representation model to obtain image analogies. The method makes use of corresponding image training patches of two different imaging modalities to learn a dictionary capturing appearance relations. We test our approach on backscattered electron (BSE) scanning electron microscopy (SEM)/confocal and transmission electron microscopy (TEM)/confocal images. We perform rigid, affine, and deformable registration via B-splines and show improvements over direct registration using both mutual information and sum of squared differences similarity measures to account for differences in image appearance.

We propose a coupled dictionary learning method to predict deformation fields based on image appearance. Rather than estimating deformations by standard image registration methods, we investigate how to obtain a basis of the space of deformations. In particular, we explore how image appearance differences with respect to a common atlas image can be used to predict deformations represented by such a basis. We use a coupled dictionary learning method to jointly learn a basis for image appearance differences and their related deformations. Our proposed method is based on local image patches. We evaluate our method on synthetically generated datasets as well as on a structural magnetic resonance brain imaging (MRI) dataset. Our method results in an improved prediction accuracy while reducing the search space compared to nearest neighbor search and demonstrates that learning a deformation basis is feasible.