Vibration analysis and control of an axially moving string by expanded Hamilton’s principle

Time-varying length axially moving string subjected to control force and transverse excitation force is analyzed. The string’s transverse vibration attenuation law is achieved. Suitable lateral displacement solution is proposed to meet the conditions of vibration based on the accurate control. The relationship among the transverse displacement, the angular velocity and the time is obtained and discussed. The effect of string’s speed, damping, tension variable on the transverse displacement of string are also taken into consideration

Motor neuron-derived Thsd7a is essential for zebrafish vascular development via the Notch-dll4 signaling pathway.

BackgroundDevelopment of neural and vascular systems displays astonishing similarities among vertebrates. This parallelism is under a precise control of complex guidance signals and neurovascular interactions. Previously, our group identified a highly conserved neural protein called thrombospondin type I domain containing 7A (THSD7A). Soluble THSD7A promoted and guided endothelial cell migration, tube formation and sprouting. In addition, we showed that thsd7a could be detected in the nervous system and was required for intersegmental vessels (ISV) patterning during zebrafish development. However, the exact origin of THSD7A and its effect on neurovascular interaction remains unclear.ResultsIn this study, we discovered that zebrafish thsd7a was expressed in the primary motor neurons. Knockdown of Thsd7a disrupted normal primary motor neuron formation and ISV sprouting in the Tg(kdr:EGFP/mnx1:TagRFP) double transgenic zebrafish. Interestingly, we found that Thsd7a morphants displayed distinct phenotypes that are very similar to the loss of Notch-delta like 4 (dll4) signaling. Transcript profiling further revealed that expression levels of notch1b and its downstream targets, vegfr2/3 and nrarpb, were down-regulated in the Thsd7a morphants. These data supported that zebrafish Thsd7a could regulate angiogenic sprouting via Notch-dll4 signaling during development.ConclusionsOur results suggested that motor neuron-derived Thsd7a plays a significant role in neurovascular interactions. Thsd7a could regulate ISV angiogenesis via Notch-dll4 signaling. Thus, Thsd7a is a potent angioneurin involved in the development of both neural and vascular systems

One-step Multi-view Clustering with Diverse Representation

Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity, preventing them from being applied to large-scale tasks. Multi-view clustering via matrix factorization is a representative to address this issue. However, most of them map the data matrices into a fixed dimension, which limits the expressiveness of the model. Moreover, a range of methods suffer from a two-step process, i.e., multimodal learning and the subsequent $k$-means, inevitably causing a sub-optimal clustering result. In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework. Specifically, we first project original data matrices into various latent spaces to attain comprehensive information and auto-weight them in a self-supervised manner. Then we directly use the information matrices under diverse dimensions to obtain consensus discrete clustering labels. The unified work of representation learning and clustering boosts the quality of the final results. Furthermore, we develop an efficient optimization algorithm to solve the resultant problem with proven convergence. Comprehensive experiments on various datasets demonstrate the promising clustering performance of our proposed method

Scalable Incomplete Multi-View Clustering with Structure Alignment

The success of existing multi-view clustering (MVC) relies on the assumption that all views are complete. However, samples are usually partially available due to data corruption or sensor malfunction, which raises the research of incomplete multi-view clustering (IMVC). Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID). Such the AUP-ID would cause inaccurate graph fusion and degrades clustering performance. To tackle these issues, we propose a novel incomplete anchor graph learning framework termed Scalable Incomplete Multi-View Clustering with Structure Alignment (SIMVC-SA). Specially, we construct the view-specific anchor graph to capture the complementary information from different views. In order to solve the AUP-ID, we propose a novel structure alignment module to refine the cross-view anchor correspondence. Meanwhile, the anchor graph construction and alignment are jointly optimized in our unified framework to enhance clustering quality. Through anchor graph construction instead of full graphs, the time and space complexity of the proposed SIMVC-SA is proven to be linearly correlated with the number of samples. Extensive experiments on seven incomplete benchmark datasets demonstrate the effectiveness and efficiency of our proposed method. Our code is publicly available at https://github.com/wy1019/SIMVC-SA

Heparan sulfates and heparan sulfate binding proteins in sepsis

Heparan sulfates (HSs) are the main components in the glycocalyx which covers endothelial cells and modulates vascular homeostasis through interactions with multiple Heparan sulfate binding proteins (HSBPs). During sepsis, heparanase increases and induces HS shedding. The process causes glycocalyx degradation, exacerbating inflammation and coagulation in sepsis. The circulating heparan sulfate fragments may serve as a host defense system by neutralizing dysregulated Heparan sulfate binding proteins or pro-inflammatory molecules in certain circumstances. Understanding heparan sulfates and heparan sulfate binding proteins in health and sepsis is critical to decipher the dysregulated host response in sepsis and advance drug development. In this review, we will overview the current understanding of HS in glycocalyx under septic condition and the dysfunctional heparan sulfate binding proteins as potential drug targets, particularly, high mobility group box 1 (HMGB1) and histones. Moreover, several drug candidates based on heparan sulfates or related to heparan sulfates, such as heparanase inhibitors or heparin-binding protein (HBP), will be discussed regarding their recent advances. By applying chemical or chemoenzymatic approaches, the structure-function relationship between heparan sulfates and heparan sulfate binding proteins is recently revealed with structurally defined heparan sulfates. Such homogenous heparan sulfates may further facilitate the investigation of the role of heparan sulfates in sepsis and the development of carbohydrate-based therapy

DealMVC: Dual Contrastive Calibration for Multi-view Clustering

Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe the following drawback, which limits the clustering performance from further improvement. The existing multi-view models mainly focus on the consistency of the same samples in different views while ignoring the circumstance of similar but different samples in cross-view scenarios. To solve this problem, we propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC). Specifically, we first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph. Moreover, to utilize the diversity of multi-view information, we propose a local contrastive calibration loss to constrain the consistency of pair-wise view features. The feature structure is regularized by reliable class information, thus guaranteeing similar samples have similar features in different views. During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels. In comparison with other state-of-the-art approaches, the comprehensive experimental results obtained from eight benchmark datasets provide substantial validation of the effectiveness and superiority of our algorithm. We release the code of DealMVC at https://github.com/xihongyang1999/DealMVC on GitHub