A backdoor attack against link prediction tasks with graph neural networks

Graph Neural Networks (GNNs) are a class of deep learning models capable of processing graph-structured data, and they have demonstrated significant performance in a variety of real-world applications. Recent studies have found that GNN models are vulnerable to backdoor attacks. When specific patterns (called backdoor triggers, e.g., subgraphs, nodes, etc.) appear in the input data, the backdoor embedded in the GNN models is activated, which misclassifies the input data into the target class label specified by the attacker, whereas when there are no backdoor triggers in the input, the backdoor embedded in the GNN models is not activated, and the models work normally. Backdoor attacks are highly stealthy and expose GNN models to serious security risks. Currently, research on backdoor attacks against GNNs mainly focus on tasks such as graph classification and node classification, and backdoor attacks against link prediction tasks are rarely studied. In this paper, we propose a backdoor attack against the link prediction tasks based on GNNs and reveal the existence of such security vulnerability in GNN models, which make the backdoored GNN models to incorrectly predict unlinked two nodes as having a link relationship when a trigger appear. The method uses a single node as the trigger and poison selected node pairs in the training graph, and then the backdoor will be embedded in the GNN models through the training process. In the inference stage, the backdoor in the GNN models can be activated by simply linking the trigger node to the two end nodes of the unlinked node pairs in the input data, causing the GNN models to produce incorrect link prediction results for the target node pairs

Investigation of genetic diversity and population structure of common wheat cultivars in northern China using DArT markers

<p>Abstract</p> <p>Background</p> <p>In order to help establish heterotic groups of Chinese northern wheat cultivars (lines), Diversity arrays technology (DArT) markers were used to investigate the genetic diversity and population structure of Chinese common wheat (<it>Triticum aestivum </it>L.).</p> <p>Results</p> <p>In total, 1637 of 7000 DArT markers were polymorphic and scored with high confidence among a collection of 111 lines composed mostly of cultivars and breeding lines from northern China. The polymorphism information content (PIC) of DArT markers ranged from 0.03 to 0.50, with an average of 0.40, with P > 80 (reliable markers). With principal-coordinates analysis (PCoA) of DArT data either from the whole genome or from the B-genome alone, all lines fell into one of two major groups reflecting 1RS/1BL type (1RS/1BL and non-1RS/1BL). Evidence of geographic clustering of genotypes was also observed using DArT markers from the A genome. Cluster analysis based on the unweighted pair-group method with algorithmic mean suggested the existence of two subgroups within the non-1RS/1BL group and four subgroups within the 1RS/1BL group. Furthermore, analysis of molecular variance (AMOVA) revealed highly significant (<it>P </it>< 0.001) genetic variance within and among subgroups and among groups.</p> <p>Conclusion</p> <p>These results provide valuable information for selecting crossing parents and establishing heterotic groups in the Chinese wheat-breeding program.</p

Location privacy without mutual trust: The spatial Bloom filter

Location-aware applications are one of the biggest innovations brought by the smartphone era, and are effectively changing our everyday lives. But we are only starting to grasp the privacy risks associated with constant tracking of our whereabouts. In order to continue using location-based services in the future without compromising our privacy and security, we need new, privacy-friendly applications and protocols. In this paper, we propose a new compact data structure based on Bloom filters, designed to store location information. The spatial Bloom filter (SBF), as we call it, is designed with privacy in mind, and we prove it by presenting two private positioning protocols based on the new primitive. The protocols keep the user's exact position private, but allow the provider of the service to learn when the user is close to specific points of interest, or inside predefined areas. At the same time, the points and areas of interest remain oblivious to the user. The two proposed protocols are aimed at different scenarios: a two-party setting, in which communication happens directly between the user and the service provider, and a three-party setting, in which the service provider outsources to a third party the communication with the user. A detailed evaluation of the efficiency and security of our solution shows that privacy can be achieved with minimal computational and communication overhead. The potential of spatial Bloom filters in terms of generality, security and compactness makes them ready for deployment, and may open the way for privacy preserving location-aware applications

Development of a new marker system for identifying the complex members of the low-molecular-weight glutenin subunit gene family in bread wheat (Triticum aestivum L.)

Low-molecular-weight glutenin subunits (LMW-GSs) play an important role in determining the bread-making quality of bread wheat. However, LMW-GSs display high polymorphic protein complexes encoded by multiple genes, and elucidating the complex LMW-GS gene family in bread wheat remains challenging. In the present study, using conventional polymerase chain reaction (PCR) with conserved primers and high-resolution capillary electrophoresis, we developed a new molecular marker system for identifying LMW-GS gene family members. Based on sequence alignment of 13 LMW-GS genes previously identified in the Chinese bread wheat variety Xiaoyan 54 and other genes available in GenBank, PCR primers were developed and assigned to conserved sequences spanning the length polymorphism regions of LMW-GS genes. After PCR amplification, 17 DNA fragments in Xiaoyan 54 were detected using capillary electrophoresis. In total, 13 fragments were identical to previously identified LMW-GS genes, and the other 4 were derived from unique LMW-GS genes by sequencing. This marker system was also used to identify LMW-GS genes in Chinese Spring and its group 1 nulli–tetrasomic lines. Among the 17 detected DNA fragments, 4 were located on chromosome 1A, 5 on 1B, and 8 on 1D. The results suggest that this marker system is useful for large-scale identification of LMW-GS genes in bread wheat varieties, and for the selection of desirable LMW-GS genes to improve the bread-making quality in wheat molecular breeding programmes

New Insights into the Organization, Recombination, Expression and Functional Mechanism of Low Molecular Weight Glutenin Subunit Genes in Bread Wheat

The bread-making quality of wheat is strongly influenced by multiple low molecular weight glutenin subunit (LMW-GS) proteins expressed in the seeds. However, the organization, recombination and expression of LMW-GS genes and their functional mechanism in bread-making are not well understood. Here we report a systematic molecular analysis of LMW-GS genes located at the orthologous Glu-3 loci (Glu-A3, B3 and D3) of bread wheat using complementary approaches (genome wide characterization of gene members, expression profiling, proteomic analysis). Fourteen unique LMW-GS genes were identified for Xiaoyan 54 (with superior bread-making quality). Molecular mapping and recombination analyses revealed that the three Glu-3 loci of Xiaoyan 54 harbored dissimilar numbers of LMW-GS genes and covered different genetic distances. The number of expressed LMW-GS in the seeds was higher in Xiaoyan 54 than in Jing 411 (with relatively poor bread-making quality). This correlated with the finding of higher numbers of active LMW-GS genes at the A3 and D3 loci in Xiaoyan 54. Association analysis using recombinant inbred lines suggested that positive interactions, conferred by genetic combinations of the Glu-3 locus alleles with more numerous active LMW-GS genes, were generally important for the recombinant progenies to attain high Zeleny sedimentation value (ZSV), an important indicator of bread-making quality. A higher number of active LMW-GS genes tended to lead to a more elevated ZSV, although this tendency was influenced by genetic background. This work provides substantial new insights into the genomic organization and expression of LMW-GS genes, and molecular genetic evidence suggesting that these genes contribute quantitatively to bread-making quality in hexaploid wheat. Our analysis also indicates that selection for high numbers of active LMW-GS genes can be used for improvement of bread-making quality in wheat breeding

Effect of Traverse Speed on the Defect Characteristic, Microstructure, and Mechanical Property of Friction Stir Welded T-Joints of Dissimilar Mg/Al Alloy

The AZ31 B/2024-T4 T-lap-joint was successfully fabricated by friction stir welding (FSW) with different welding parameters. The defect characteristics and metallurgical structure were observed and analyzed using optical microscope (OM) and scanning electron microscopy (SEM). Besides, the effects of defects and welding parameters on mechanical properties were investigated. The results show that an effective metallurgical reaction zone can be formed between Mg and Al (Mg-Al MRZ) and the island structures and lamellar structures appeared in the Mg-Al MRZ. The T-joints without tunnel defects can be obtained and the excellent mechanical properties of the T-joint were achieved using the welding speed of 50 mm/min. The tensile strength along the skin and the stringer was mainly affected by the kiss bonding defects

The Carotenoid Cleavage Dioxygenase Gene <em>CCD7-B</em>, at Large, Is Associated with Tillering in Common Wheat

Wheat, an important cereal crop, is responsible for the livelihoods of many people, and a component of national food security. Tillering, which determines plant architecture and spike number, is a critical agronomic trait of wheat. The carotenoid cleavage dioxygenase 7 (CCD7) has an important effect on the growth of tillers or lateral branches and lateral roots of plants. In order to study the relationship between CCD7 and tillering in wheat, CCD7-B was isolated from 10 Chinese wheat varieties with different tiller numbers. Subsequently, bioinformatics, allelic variation analysis, and field experiments were performed. Wheat CCD7-B belongs to the retinal pigment epithelial membrane receptor (RPE65) superfamily; it displays the greatest homology with monocot CCD7 proteins. Phylogenetic analysis of wheat CCD7-B proteins indicated division into dicotyledonous and monocotyledonous clades. Allelic variation analysis of CCD7-B via SrgAI enzyme digestion (a marker of cleaved amplified polymorphic sequences) suggested that 262 Chinese wheat micro-core collections and 121 Chinese wheat major cultivars from the Yellow and Huai River Valley winter wheat region can be divided into two groups: CCD7-B1 (C/T/T) and CCD7-B2 (G/C/A). CCD7-B1 showed better allelic variation than did CCD7-B2 for increasing the number of effective tillers of wheat varieties in China. This study provides reference data for the application of CCD7-B alleles to wheat breeding and supports further research regarding the mechanism of tillering in common wheat

Diversity, distribution of Puroindoline genes and their effect on kernel hardness in a diverse panel of Chinese wheat germplasm

Abstract Background Kernel hardness, which has great influence on the end-use properties of common wheat, is mainly controlled by Puroindoline genes, Pina and Pinb. Using EcoTILLING platform, we herein investigated the allelic variations of Pina and Pinb genes and their association with the Single Kernel Characterization System (SKCS) hardness index in a diverse panel of wheat germplasm. Results The kernel hardness varied from 1.4 to 102.7, displaying a wide range of hardness index. In total, six Pina and nine Pinb alleles resulting in 15 genotypes were detected in 1787 accessions. The most common alleles are the wild type Pina-D1a (90.4%) and Pina-D1b (7.4%) for Pina, and Pinb-D1b (43.6%), Pinb-D1a (41.1%) and Pinb-D1p (12.8%) for Pinb. All the genotypes have hard type kernel hardness of SKCS index (>60.0), except the wild types of Pina and Pinb combination (Pina-D1a/Pinb-D1a). The most frequent genotypes in Chinese and foreign cultivars was Pina-D1a/Pinb-D1b (46.3 and 39.0%, respectively) and in Chinese landraces was Pina-D1a/Pinb-D1a (54.2%). The frequencies of hard type accessions are increasing from 35.5% in the region IV, to 40.6 and 61.4% in the regions III and II, and then to 77.0% in the region I, while those of soft type are accordingly decreasing along with the increase of latitude. Varieties released after 2000 in Beijing, Hebei, Shandong and Henan have higher average kernel hardness index than that released before 2000. Conclusion The kernel hardness in a diverse panel of Chinese wheat germplasm revealed an increasing of kernel hardness generally along with the latitude across China. The wild type Pina-D1a and Pinb-D1a, and one Pinb mutant (Pinb-D1b) are the most common alleles of six Pina and nine Pinb alleles, and a new double null genotype (Pina-D1x/Pinb-D1ah) possessed relatively high SKCS hardness index. More hard type varieties were released in recent years with different prevalence of Pin-D1 combinations in different regions. This work would benefit the understanding of the selection and molecular processes of kernel hardness across China and different breeding stages, and provide useful information for the improvement of wheat quality in China