Potential Therapeutic Applications of Exosomes in Bone Regenerative Medicine

The ability of bone regeneration is relatively robust, which is crucial for fracture healing, but delayed healing and nonunion are still common problems in clinical practice. Fortunately, exciting results have been achieved for regenerative medicine in recent years, especially in the area of stem cell-based treatment, but all these cell-based approaches face challenging problems, including immune rejection. For this reason, exosomes, stem cell-derived small vesicles of endocytic origin, have attracted the attention of many investigators in the field of bone regeneration. One of the attractive features of exosomes is that they are small and can travel between cells and deliver bioactive products, including miRNA, mRNA, proteins, and various other factors, to promote bone regeneration, with undetectable immune rejection. In this chapter, we intend to briefly update the recent progressions, and discuss the potential challenges in the target areas. Hopefully, our discussion would be helpful not only for the clinicians and the researchers in the specific disciplines but also for the general audiences as well

Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior

Tensor regression methods have been widely used to predict a scalar response from covariates in the form of a multiway array. In many applications, the regions of tensor covariates used for prediction are often spatially connected with unknown shapes and discontinuous jumps on the boundaries. Moreover, the relationship between the response and the tensor covariates can be nonlinear. In this article, we develop a nonlinear Bayesian tensor additive regression model to accommodate such spatial structure. A functional fused elastic net prior is proposed over the additive component functions to comprehensively model the nonlinearity and spatial smoothness, detect the discontinuous jumps, and simultaneously identify the active regions. The great flexibility and interpretability of the proposed method against the alternatives are demonstrated by a simulation study and an analysis on facial feature data

Increased expression of the pluripotency markers sex-determining region Y-box 2 and Nanog homeobox in ovarian endometriosis

BACKGROUND: The precise etiology of endometriosis is not fully understood; the involvement of stem cells theory is a new hypothesis. Related studies mainly focus on stemness-related genes, and pluripotency markers may play a role in the etiology of endometriosis. We aimed to analyze the transcription pluripotency factors sex-determining region Y-box 2 (SOX2), Nanog homeobox (NANOG), and octamer-binding protein 4 (OCT4) in the endometrium of reproductive-age women with and without ovarian endometriosis. METHODS: We recruited 26 women with laparoscopy-diagnosed ovarian endometriosis (endometriosis group) and 16 disease-free women (control group) to the study. Endometrial and endometriotic samples were collected. SOX2, NANOG, and OCT4 expression were analyzed with quantitative real-time polymerase chain reaction, western blotting, and immunohistochemistry. RESULTS: Compared to the control group, SOX2 mRNA and protein expression was significantly higher in the eutopic endometrium of participants in the endometriosis group. In the endometriosis group, SOX2 and NANOG mRNA and protein expression were significantly increased in ectopic endometrium compared with eutopic endometrium; there was a trend towards lower OCT4 mRNA expression and higher OCT4 protein expression in ectopic endometrium. CONCLUSIONS: The transcription pluripotency factors SOX2 and NANOG were overexpression in ovarian endometriosis, their role in pathogenesis of endometriosis should be further studied

TempCLR: Temporal Alignment Representation with Contrastive Learning

Video representation learning has been successful in video-text pre-training for zero-shot transfer, where each sentence is trained to be close to the paired video clips in a common feature space. For long videos, given a paragraph of description where the sentences describe different segments of the video, by matching all sentence-clip pairs, the paragraph and the full video are aligned implicitly. However, such unit-level similarity measure may ignore the global temporal context over a long time span, which inevitably limits the generalization ability. In this paper, we propose a contrastive learning framework TempCLR to compare the full video and the paragraph explicitly. As the video/paragraph is formulated as a sequence of clips/sentences, under the constraint of their temporal order, we use dynamic time warping to compute the minimum cumulative cost over sentence-clip pairs as the sequence-level distance. To explore the temporal dynamics, we break the consistency of temporal order by shuffling the video clips or sentences according to the temporal granularity. In this way, we obtain the representations for clips/sentences, which perceive the temporal information and thus facilitate the sequence alignment. In addition to pre-training on the video and paragraph, our approach can also generalize on the matching between different video instances. We evaluate our approach on video retrieval, action step localization, and few-shot action recognition, and achieve consistent performance gain over all three tasks. Detailed ablation studies are provided to justify the approach design

Inherent SM Voltage Balance for Multilevel Circulant Modulation in Modular Multilevel DC--DC Converters

The modularity of a modular multilevel dc converter (MMDC) makes it attractive for medium-voltage distribution systems. Inherent balance of submodule (SM) capacitor voltages is considered as an ideal property, which avoids a complex sorting process based on many measurements thereby reducing costs and enhancing reliability. This article extends the inherent balance concept previously shown for square-wave modulation to a multilevel version for MMDCs. A switching duty matrix dU is introduced: it is a circulant matrix of preset multilevel switching patterns with multiple stages and multiple durations. Inherent voltage balance is ensured with a full-rank dU . Circulant matrix theory shows that this is equivalent to a simplified common factor criterion. A nonfull rank dU causes clusters of SM voltage rather than a single common value, with the clusters indicated by the kernel of the matrix. A generalized coprime criterion is developed into several deductions that serve as practical guidance for design of multilevel circulant modulation. The theoretical development is verified through full-scale simulations and downscaled experiments. The effectiveness of the proposed circulant modulation in achieving SM voltage balance in an MMDC is demonstrated