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Extracellular forces transmitted through the cytoskeleton can deform the cell nucleus. Large nuclear deformations increase the risk of disrupting the integrity of the nuclear envelope and causing DNA damage. The mechanical stability of the nucleus defines its capability to maintain nuclear shape by minimizing nuclear deformation and allowing strain to be minimized when deformed. Understanding the deformation and recovery behavior of the nucleus requires characterization of nuclear viscoelastic properties. Here, we quantified the decoupled viscoelastic parameters of the cell membrane, cytoskeleton, and the nucleus. The results indicate that the cytoskeleton enhances nuclear mechanical stability by lowering the effective deformability of the nucleus while maintaining nuclear sensitivity to mechanical stimuli. Additionally, the cytoskeleton decreases the strain energy release rate of the nucleus and might thus prevent shape change-induced structural damage to chromatin.

In transfusion medicine, the deformability of stored red blood cells (RBCs) changes during storage in blood banks. Compromised RBC deformability can reduce the transfusion efficiency or intensify transfusion complications, such as sepsis. This paper reports the microfluidic mechanical measurement of stored RBCs under the physiological deformation mode (that is, folding). Instead of using phenomenological metrics of deformation or elongation indices (DI or EI), the effective stiffness of RBCs, a flow velocityindependent parameter, is defined and used for the first time to evaluate the mechanical degradation of RBCs during storage. Fresh RBCs and RBCs stored up to 6 weeks (42 days) in the blood bank were measured, revealing that the effective stiffness of RBCs increases over the storage process. RBCs stored for 1 week started to show significantly higher stiffness than fresh RBCs, and stored RBC stiffness degraded faster during the last 3 weeks than during the first 3 weeks. Furthermore, the results indicate that the time points of the effective stiffness increase coincide well with the degradation patterns of S-nitrosothiols (SNO) and adenosine triphosphate (ATP) in RBC storage lesions.

With the rapid growth of social tagging systems, many efforts have been put on tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping both the tag-based user and item profiles to an abstract deep feature space, where the deepsemantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized). Due to huge numbers of online items, the training of this model is usually computationally expensive in the real-world context. Therefore, we introduce negative sampling, which significantly increases the model's training efficiency (109.6 times quicker) and ensures the scalability in practice. Experimental results show that our model can significantly outperform the state-of-the-art baselines in tag-aware personalized recommendation: e.g., its mean reciprocal rank is between 5.7 and 16.5 times better than the baselines.