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Evolutionary design is a widely accepted practice for defining microservice boundaries. It is performed through a sequence of incremental refactoring tasks (we call it “microservice refactoring”), each restructuring only part of a microservice system (a.k.a., refactoring part) into well-defined services for improving the architecture in a controlled manner. Despite its popularity in practice, microservice refactoring suffers from insufficient methodological support. While there are numerous studies addressing similar software design tasks, i.e., software remodularization and microservitization, their approaches prove inadequate when applied to microservice refactoring. Our analysis reveals that their approaches may even degrade the entire architecture in microservice refactoring, as they only optimize the refactoring part in such applications, but neglect the relationships between the refactoring part and the remaining system. As the first response to the need, Micro2Micro is proposed to re-partition the refactoring part while optimizing three quality objectives including the interdependence between the refactoring and non-refactoring parts. In addition, it allows architects to intervene in the decision-making process by interactively incorporating their knowledge into the iterative search for optimal refactoring solutions. An empirical study on 13 open-source projects of different sizes shows that the solutions from Micro2Micro perform well and exhibit quality improvement with an average up to 45% to the original architecture. Users of Micro2Micro found the suggested solutions highly satisfactory. They acknowledge the advantages in terms of infusing human intelligence into decisions, providing immediate quality feedback, and quick exploration capability. 

Synthetic matrices with dynamic presentation of cell guidance cues are needed for the development of physiologically relevant in vitro tumor models. Towards the goal of mimicking prostate cancer progression and metastasis, we engineered a tunable hyaluronic acid-based hydrogel platform with protease degradable and cell adhesive properties employing bioorthogonal tetrazine ligation with strained alkenes. The synthetic matrix was first fabricated via a slow tetrazine-norbornene reaction, then temporally modified via a diffusion-controlled method using trans-cyclooctene, a fierce dienophile that reacts with tetrazine with an unusually fast rate. The encapsulated DU145 prostate cancer single cells spontaneously formed multicellular tumoroids after 7 days of culture. In situ modification of the synthetic matrix via covalent tagging of cell adhesive RGD peptide induced tumoroid decompaction and the development of cellular protrusions. RGD tagging did not compromise the overall cell viability, nor did it induce cell apoptosis. In response to increased matrix adhesiveness, DU145 cells dynamically loosen cell-cell adhesion and strengthen cell-matrix interactions to promote an invasive phenotype. Characterization of the 3D cultures by immunocytochemistry and gene expression analyses demonstrated that cells invaded into the matrix via a mesenchymal like migration, with upregulation of major mesenchymal markers, and down regulation of epithelial markers. The tumoroids formed cortactin positive invadopodia like structures, indicating active matrix remodeling. Overall, the engineered tumor model can be utilized to identify potential molecular targets and test pharmacological inhibitors, thereby accelerating the design of innovative strategies for cancer therapeutics. 

Abstract Many RNAs function through RNA–RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA–RNA interaction is useful, however, existing tools are either too simplistic or too slow. To address this issue, we present LinearCoFold, which approximates the complete minimum free energy structure of two strands in linear time, and LinearCoPartition, which approximates the cofolding partition function and base pairing probabilities in linear time. LinearCoFold and LinearCoPartition are orders of magnitude faster than RNAcofold. For example, on a sequence pair with combined length of 26,190 nt, LinearCoFold is 86.8× faster than RNAcofold MFE mode, and LinearCoPartition is 642.3× faster than RNAcofold partition function mode. Surprisingly, LinearCoFold and LinearCoPartition’s predictions have higher PPV and sensitivity of intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the RNA–RNA interaction between SARS-CoV-2 genomic RNA (gRNA) and human U4 small nuclear RNA (snRNA), which has been experimentally studied, and observe that LinearCoFold’s prediction correlates better with the wet lab results than RNAcofold’s.