SimFusion: A Unified Similarity Measurement Algorithm for Multi-Type Interrelated Web Objects

In this paper, we use a Unified Relationship Matrix (URM) to represent a set of heterogeneous web objects (e.g., web pages, queries) and their interrelationships (e.g., hyperlink, user click-through relationships). We claim that iterative computations over the URM can help overcome the data sparseness problem (a common situation in the Web) and detect latent relationships among heterogeneous web objects, thus, can improve the quality of various information applications that require the combination of information from heterogeneous sources. To support our claim, we further propose a unified similarity-calculating algorithm, the SimFusion algorithm. By iteratively computing over the URM, the SimFusion algorithm can effectively integrate relationships from heterogeneous sources when measuring the similarity of two web objects. Experiments based on a real search engine query log and a large real web page collection demonstrate that the SimFusion algorithm can significantly improve similarity measurement of web objects over both traditional content based similarity-calculating algorithms and the cutting edge SimRank algorithm

VPA mediates bidirectional regulation of cell cycle progression through the PPP2R2A-Chk1 signaling axis in response to HU

Cell cycle checkpoint kinases play a pivotal role in protecting against replicative stress. In this study, valproic acid (VPA), a histone deacetylase inhibitor (HDACi), was found to promote breast cancer MCF-7 cells to traverse into G2/M phase for catastrophic injury by promoting PPP2R2A (the B-regulatory subunit of Phosphatase PP2A) to facilitate the dephosphorylation of Chk1 at Ser317 and Ser345. By contrast, VPA protected normal 16HBE cells from HU toxicity through decreasing PPP2R2A expression and increasing Chk1 phosphorylation. The effect of VPA on PPP2R2A was at the post-transcription level through HDAC1/2. The in vitro results were affirmed in vivo. Patients with lower PPP2R2A expression and higher pChk1 expression showed significantly worse survival. PPP2R2A D197 and N181 are essential for PPP2R2A-Chk1 signaling and VPA-mediated bidirectional effect on augmenting HU-induced tumor cell death and protecting normal cells

Effects of different positions on rehabilitation after rotator cuff repair under shoulder arthroscopy

Objective: Shoulder arthroscopic rotator cuff tear repair is currently the main treatment for full-thickness rotator cuff tears, and postoperative rehabilitation training is essential. However, pain and limitation of activity during the rehabilitation process will lead to poor results. Hence, identifying rehabilitation approaches is crucial. This study aimed to compare patient's rehabilitation outcomes and experience between rehabilitation in the supine position and in the standing position. Methods: This prospective study included patients diagnosed with full-thickness rotator cuff tears who underwent shoulder arthroscopic double-row rivet repair at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine from March 2019 to September 2021. The patients were randomly assigned to the standing rehabilitation exercise group (group A) and the supine rehabilitation exercise group (group B). All patients were followed up for 6 months to record and compare the visual analog scale (VAS) scores, shoulder range of motion, and rehabilitation compliance. Results: Altogether, 86 patients participated in the study, of whom 79 patients completed the 6-month follow-up. Groups A and B had 39 and 40 patients, respectively. Before operation, the VAS score, forward flexion and extension angle, and abduction angle were comparable between groups A and B. After operation, the patients in groups A and B all experienced a significant improvement in the VAS score, forward flexion and extension angle, and abduction angle (p < 0.05). In addition, patients in group B had better VAS score (4.58 ± 0.87 vs. 5.21 ± 1.13, p = 0.0068; 2.15 ± 0.66 vs. 2.51 ± 0.51, p = 0.0078; 0.78 ± 0.86 vs. 1.33 ± 0.81, p = 0.0015), forward flexion and extension angle (109.30 ± 2.87 ° vs. 102.33 ± 3.74°, p = 0.0001; 109.53 ± 3.39° vs. 104.18 ± 2.76°, p = 0.0001; 125.22 ± 6.05° vs. 117.59 ± 2.27°, p = 0.0001), and abduction angle (91.78 ± 2.77° vs. 82.92 ± 2.12°, p = 0.0001; 91.62 ± 2.78° vs. 82.82 ± 1.45°, p = 0.0001; 109.48 ± 3.37° vs. 100.10 ± 2.94°, p = 0.0001) at 2 wk, 6 wk and 6 m postoperatively. Conclusion: After 6 months of follow-up, the patients who performed rehabilitation exercises in the supine position achieved better rehabilitation outcomes than those who performed rehabilitation exercises while standing

Graph Embedding: A General Framework for Dimensionality Reduction

In the last decades, a large family of algorithms ─ supervised or unsupervised; stemming from statistic or geometry theory─have been proposed to provide different solutions to the problem of dimensionality reduction. In this paper, beyond the different motivations of these algorithms, we propose a general framework, graph embedding along with its linearization and kernelization, which in theory reveals the underlying objective shared by most previous algorithms. It presents a unified perspective to understand these algorithms; that is, each algorithm can be considered as the direct graph embedding or its linear/kernel extension of some specific graph characterizing certain statistic or geometry property of a data set. Furthermore, this framework is a general platform to develop new algorithm for dimensionality reduction. To this end, we propose a new supervised algorithm, Marginal Fisher Analysis (MFA), for dimensionality reduction by designing two graphs that characterize the intra-class compactness and interclass separability, respectively. MFA measures the intra-class compactness with the distance between each data point and its neighboring points of the same class, and measures the inter-class separability with the class margins; thus it overcomes the limitations of traditional Linear Discriminant Analysis algorithm in terms of data distribution assumptions and available projection directions. The toy problem on artificial data and the real face recognition experiments both show the superiority of our proposed MFA in comparison to LDA. 1

Maxwell-Equations Based on Mining Transient Electromagnetic Method for Coal Mine-Disaster Water Detection

Water-bearing geological structure is a serious threat to coalmine safety. This research focuses on detecting water-bearing geological structure by transient electromagnetic method. First, we introduce the principle of mining transient electromagnetic method, and then explain the technique of Finite Different Time Domain using in the transient electromagnetic method. Based on Maxwell equations, we derive the difference equations of electromagnetic field and study the responses of water-bearing geological structure using FDTD. Moreover, we establish the relationship between receiving electromagnetic field and geological information. The typical coal geological model of goaf-water is chosen to do the numerical simulation. Besides, we verify the availability of the method by numerical simulation using coal geological model. Finally, we use the method in the coalmine which is located in Linfen city in Shanxi province in China, and the detecting result is verified by drilling