Illegal Community Detection in Bitcoin Transaction Networks.

Community detection is widely used in social networks to uncover groups of related vertices (nodes). In cryptocurrency transaction networks, community detection can help identify users that are most related to known illegal users. However, there are challenges in applying community detection in cryptocurrency transaction networks: (1) the use of pseudonymous addresses that are not directly linked to personal information make it difficult to interpret the detected communities; (2) on Bitcoin, a user usually owns multiple Bitcoin addresses, and nodes in transaction networks do not always represent users. Existing works on cluster analysis on Bitcoin transaction networks focus on addressing the later using different heuristics to cluster addresses that are controlled by the same user. This research focuses on illegal community detection containing one or more illegal Bitcoin addresses. We first investigate the structure of Bitcoin transaction networks and suitable community detection methods, then collect a set of illegal addresses and use them to label the detected communities. The results show that 0.06% of communities from daily transaction networks contain one or more illegal addresses when 2,313,344 illegal addresses are used to label the communities. The results also show that distance-based clustering methods and other methods depending on them, such as network representation learning, are not suitable for Bitcoin transaction networks while community quality optimization and label-propagation-based methods are the most suitable.

Semi-Supervised Semantic Segmentation of Remote Sensing Images Based on Dual Cross-Entropy Consistency.

Semantic segmentation is a growing topic in high-resolution remote sensing image processing. The information in remote sensing images is complex, and the effectiveness of most remote sensing image semantic segmentation methods depends on the number of labels; however, labeling images requires significant time and labor costs. To solve these problems, we propose a semi-supervised semantic segmentation method based on dual cross-entropy consistency and a teacher-student structure. First, we add a channel attention mechanism to the encoding network of the teacher model to reduce the predictive entropy of the pseudo label. Secondly, the two student networks share a common coding network to ensure consistent input information entropy, and a sharpening function is used to reduce the information entropy of unsupervised predictions for both student networks. Finally, we complete the alternate training of the models via two entropy-consistent tasks: (1) semi-supervising student prediction results via pseudo-labels generated from the teacher model, (2) cross-supervision between student models. Experimental results on publicly available datasets indicate that the suggested model can fully understand the hidden information in unlabeled images and reduce the information entropy in prediction, as well as reduce the number of required labeled images with guaranteed accuracy. This allows the new method to outperform the related semi-supervised semantic segmentation algorithm at half the proportion of labeled images.

Research of Software Defect Prediction Model Based on Complex Network and Graph Neural Network.

The goal of software defect prediction is to make predictions by mining the historical data using models. Current software defect prediction models mainly focus on the code features of software modules. However, they ignore the connection between software modules. This paper proposed a software defect prediction framework based on graph neural network from a complex network perspective. Firstly, we consider the software as a graph, where nodes represent the classes, and edges represent the dependencies between the classes. Then, we divide the graph into multiple subgraphs using the community detection algorithm. Thirdly, the representation vectors of the nodes are learned through the improved graph neural network model. Lastly, we use the representation vector of node to classify the software defects. The proposed model is tested on the PROMISE dataset, using two graph convolution methods, based on the spectral domain and spatial domain in the graph neural network. The investigation indicated that both convolution methods showed an improvement in various metrics, such as accuracy, F-measure, and MCC (Matthews correlation coefficient) by 86.6%, 85.8%, and 73.5%, and 87.5%, 85.9%, and 75.5%, respectively. The average improvement of various metrics was noted as 9.0%, 10.5%, and 17.5%, and 6.3%, 7.0%, and 12.1%, respectively, compared with the benchmark models.

ATF6-Mediated Unfolded Protein Response Facilitates Adeno-associated Virus 2 (AAV2) Transduction by Releasing the Suppression of the AAV Receptor on Endoplasmic Reticulum Stress.

Adeno-associated virus (AAV) is extensively used as a viral vector to deliver therapeutic genes during human gene therapy. A high-affinity cellular receptor (AAVR) for most serotypes was recently identified; however, its biological function as a gene product remains unclear. In this study, we used AAVR knockdown cell models to show that AAVR depletion significantly attenuated cells to activate unfolded protein response (UPR) pathways when exposed to the endoplasmic reticulum (ER) stress inducer, tunicamycin. By analyzing three major UPR pathways, we found that ATF6 signaling was most affected in an AAVR-dependent fashion, distinct from CHOP and XBP1 branches. AAVR capacity in UPR regulation required the full native AAVR protein, and AAV2 capsid binding to the receptor altered ATF6 dynamics. Conversely, the transduction efficiency of AAV2 was associated with changes in ATF6 signaling in host cells following treatment with different small molecules. Thus, AAVR served as an inhibitory molecule to repress UPR responses via a specificity for ATF6 signaling, and the AAV2 infection route involved the release from AAVR-mediated ATF6 repression, thereby facilitating viral intracellular trafficking and transduction. IMPORTANCE The native function of the AAVR as an ER-Golgi localized protein is largely unknown. We showed that AAVR acted as a functional molecule to regulate UPR signaling under induced ER stress. AAVR inhibited the activation of the transcription factor, ATF6, whereas receptor binding to AAV2 released the suppression effects. This finding has expanded our understanding of AAV infection biology in terms of the physiological properties of AAVR in host cells. Importantly, our research provides a possible strategy which may improve the efficiency of AAV-mediated gene delivery during gene therapy.

Suppression of TCAB1 expression induced cellular senescence by lessening proteasomal degradation of p21 in cancer cells.

BACKGROUND: TCAB1, a.k.a. WRAP53ß or WDR79, is an important molecule for the maintenance of Cajal bodies and critically involved in telomere elongation and DNA repair. Upregulation of TCAB1 were discovered in a variety types of cancers. However, the function of TCAB1 in tumor cell senescence remains absent. METHODS: The TCAB1 knockdown cell lines were constructed. The expression levels of TCAB1, p21, p16 and p53 were detected by qRT-PCR and western blotting. Staining of senescence-associated ß-galactosidase was used to detect senescent cells. The ubiquitination of the p21 was analysed by immunoprecipitation and in vivo ubiquitination assay. TCGA databases were employed to perform in silico analyses for the mRNA expression of TCAB1, p21, p16 and p53. RESULTS: Here, we discovered that knockdown of TCAB1 induced rapid progression of cellular senescence in A549, H1299 and HeLa cells. In exploiting the mechanism underlining the role of TCAB1 on senescence, we found a significant increase of p21 at the protein levels upon TCAB1 depletion, whereas the p21 mRNA expression was not altered. We verified that TCAB1 knockdown was able to shunt p21 from proteasomal degradation by regulating the ubiquitination of p21. In rescue assays, it was demonstrated that decreasing the expression of p21 or increasing the expression of TCAB1 were able to attenuate the cellular senescence process induced by TCAB1 silencing. CONCLUSIONS: This study revealed the importance of TCAB1 for its biological functions in the regulation of cell senescence. Our results will be helpful to understand the mechanisms of senescence in cancer cells, which could provide clues for designing novel strategies for developing effective treatment regimens.

A Generic Method for Fast and Sensitive Detection of Adeno-Associated Viruses Using Modified AAV Receptor Recombinant Proteins.

Adeno-Associated Viruses (AAV) are widely used gene-therapy vectors for both clinical applications and laboratory investigations. The titering of different AAV preparations is important for quality control purposes, as well as in comparative studies. However, currently available methods are limited in their ability to detect various serotypes with sensitivity and convenience. Here, we took advantage of a newly discovered AAV receptor protein with high affinity to multiple AAV serotypes, and developed an ELISA-like method named "VIRELISA" (virus receptor-linked immunosorbent assay) by adopting fusion with a streptavidin-binding peptide (SBP). It was demonstrated that optimized VIRELISA assays exhibited satisfactory performance for the titering of AAV2. The linear range of AAV2 was 1 × 105 v.g. to 5 × 109 v.g., with an LOD (limit of detection) of 5 × 104 v.g. Testing of VIRELISA for the quantification of AAV1 was also successful. Our study indicated that a generic protocol for the quantification of different serotypes of AAVs was feasible, reliable and cost-efficient. The applications of VIRELISA will not only be of benefit to laboratory research due to its simplicity, but could also potentially be used for monitoring the circulation AAV loads both in clinical trials and in wild type infection of a given AAV serotype.

Divergent engagements between adeno-associated viruses with their cellular receptor AAVR.

Adeno-associated virus (AAV) receptor (AAVR) is an essential receptor for the entry of multiple AAV serotypes with divergent rules; however, the mechanism remains unclear. Here, we determine the structures of the AAV1-AAVR and AAV5-AAVR complexes, revealing the molecular details by which PKD1 recognizes AAV5 and PKD2 is solely engaged with AAV1. PKD2 lies on the plateau region of the AAV1 capsid. However, the AAV5-AAVR interface is strikingly different, in which PKD1 is bound at the opposite side of the spike of the AAV5 capsid than the PKD2-interacting region of AAV1. Residues in strands F/G and the CD loop of PKD1 interact directly with AAV5, whereas residues in strands B/C/E and the BC loop of PKD2 make contact with AAV1. These findings further the understanding of the distinct mechanisms by which AAVR recognizes various AAV serotypes and provide an example of a single receptor engaging multiple viral serotypes with divergent rules.

Adeno-associated virus 2 bound to its cellular receptor AAVR.

Adeno-associated virus (AAV) is a leading vector for virus-based gene therapy. The receptor for AAV (AAVR; also named KIAA0319L) was recently identified, and the precise characterization of AAV-AAVR recognition is in immediate demand. Taking advantage of a particle-filtering algorithm, we report here the cryo-electron microscopy structure of the AAV2-AAVR complex at 2.8 Å resolution. This structure reveals that of the five Ig-like polycystic kidney disease (PKD) domains in AAVR, PKD2 binds directly to the spike region of the AAV2 capsid adjacent to the icosahedral three-fold axis. Residues in strands B and E, and the BC loop of AAVR PKD2 interact directly with the AAV2 capsid. The interacting residues in the AAV2 capsid are mainly in AAV-featured variable regions. Mutagenesis of the amino acids at the AAV2-AAVR interface reduces binding activity and viral infectivity. Our findings provide insights into the biology of AAV entry with high-resolution details, providing opportunities for the development of new AAV vectors for gene therapy.

Inhibition of Bevacizumab-induced Epithelial-Mesenchymal Transition by BATF2 Overexpression Involves the Suppression of Wnt/ß-Catenin Signaling in Glioblastoma Cells.

BACKGROUND/AIM: Bevacizumab (BV) has been used for the treatment of recurrent glioblastoma. However, it also induces epithelial-mesenchymal transition (EMT) in glioblastoma cells, which compromises its efficacy. BATF2 (basic leucine zipper ATF-like transcription factor 2), a multi-target transcriptional repressor, has been found to suppress cancer development partly through inhibition of Wnt/ß-catenin singling. The roles of BATF2 and Wnt/ß-catenin signaling in BV-induced EMT in glioblastoma cells were investigated in this study. MATERIALS AND METHODS: BV was used to treat U87MG cells, and TOP/FOP FLASH luciferase reporters were employed to determine the activity of Wnt/ß-catenin signaling. EMT markers were detected with quantitative reverse transcription-PCR and western blotting. Immunofluorescence (IF) was used to determine the compartmentation of ß-catenin. Wound-healing, TransWell and ECIS assays were used to analyze cell adhesion, invasion and migration. RESULTS: BV induced EMT phenotype in U87MG cells, and BATF2 overexpression significantly inhibited BV-induced EMT with suppression of Wnt/ß-catenin signaling. CONCLUSION: Our findings expanded the understanding of the role of BATF2 in tumors, and also suggested a potential of using BATF2 as a therapeutic target to hinder bevacizumab induced EMT in glioblastoma.