HGCVAE: Integrating Generative and Contrastive Learning for Heterogeneous Graph Learning

Generative self-supervised learning (SSL) has exhibited significant potential and garnered increasing interest in graph learning. In this study, we aim to explore the problem of generative SSL in the context of heterogeneous graph learning (HGL). The previous SSL approaches for heterogeneous graphs have primarily relied on contrastive learning, necessitating the design of complex views to capture heterogeneity. However, existing generative SSL methods have not fully leveraged the capabilities of generative models to address the challenges of HGL. In this paper, we present HGCVAE, a novel contrastive variational graph auto-encoder that liberates HGL from the burden of intricate heterogeneity capturing. Instead of focusing on complicated heterogeneity, HGCVAE harnesses the full potential of generative SSL. HGCVAE innovatively consolidates contrastive learning with generative SSL, introducing several key innovations. Firstly, we employ a progressive mechanism to generate high-quality hard negative samples for contrastive learning, utilizing the power of variational inference. Additionally, we present a dynamic mask strategy to ensure effective and stable learning. Moreover, we propose an enhanced scaled cosine error as the criterion for better attribute reconstruction. As an initial step in combining generative and contrastive SSL, HGCVAE achieves remarkable results compared to various state-of-the-art baselines, confirming its superiority

Weak feedback assisted random fiber laser from 45°-tilted fiber Bragg grating

We have demonstrated the realization of a high-polarization random fiber laser (RFL) output based on the hybrid Raman and Erbium gain with the tailored effect provided by a 45°-tilted fiber Bragg grating (45°-TFBG), revealing an improvement in the polarization extinction ratio (PER) and achieving a PER of ~15.3 dB. The hybrid RFL system incorporating the 45°-TFBG has been systematically characterized. The random lasing wavelength can be fixed under the extremely weak feedback effect of the 45°-TFBG with reflectivity of 0.09%. In addition, numerical simulation has verified that the weak feedback can boost the random lasing emission with fixed wavelength using a power balance model, which is in good accordance with the experiment results

Research Progress in Molecular Biology of Fish Immunoglobulin M (IgM)

Immunoglobulin (Ig) is a type of globulin produced by B lymphocytes during pathogenic infection of vertebrates. It has immune functions and can realize specific recognition and neutralization of corresponding antigens. As IgM is reported first in fish, IgM is the first antibody produced during immune responses and plays a vital role in systemic and mucosal immune tissues. IgM molecules have two forms: membrane-bound IgM (mIgM) and secreted IgM (sIgM). The latter is produced by plasmacytes and secreted into body fluid, existing as immunological effect molecules. The former embeds into B cytomembrane and exists as an antigen receptor. It binds with assistant molecules to form cell receptor compounds. This study reviews research progress on the structures and production processes of IgM genes in different fish species and the distribution characteristics of IgM on B cells, mediated signal pathways, and functions. It aims to enrich basic theoretical knowledge of fish immunology and provide some scientific references for disease control in fishes

Genetic mapping reveals a candidate gene CmoFL1 controlling fruit length in pumpkin

Fruit length (FL) is an important economical trait that affects fruit yield and appearance. Pumpkin (Cucurbita moschata Duch) contains a wealth genetic variation in fruit length. However, the natural variation underlying differences in pumpkin fruit length remains unclear. In this study, we constructed a F2 segregate population using KG1 producing long fruit and MBF producing short fruit as parents to identify the candidate gene for fruit length. By bulked segregant analysis (BSA-seq) and Kompetitive Allele-Specific PCR (KASP) approach of fine mapping, we obtained a 50.77 kb candidate region on chromosome 14 associated with the fruit length. Then, based on sequence variation, gene expression and promoter activity analyses, we identified a candidate gene (CmoFL1) encoding E3 ubiquitin ligase in this region may account for the variation of fruit length. One SNP variation in promoter of CmoFL1 changed the GT1CONSENSUS, and DUAL-LUC assay revealed that this variation significantly affected the promoter activity of CmoFL1. RNA-seq analysis indicated that CmoFL1 might associated with the cell division process and negatively regulate fruit length. Collectively, our work identifies an important allelic affecting fruit length, and provides a target gene manipulating fruit length in future pumpkin breeding

Do We Really Need Contrastive Learning for Graph Representation?

In recent years, contrastive learning has emerged as a dominant self-supervised paradigm, attracting numerous research interests in the field of graph learning. Graph contrastive learning (GCL) aims to embed augmented anchor samples close to each other while pushing the embeddings of other samples (negative samples) apart. However, existing GCL methods require large and diverse negative samples to ensure the quality of embeddings, and recent studies typically leverage samples excluding the anchor and positive samples as negative samples, potentially introducing false negative samples (negatives that share the same class as the anchor). Additionally, this practice can result in heavy computational burden and high time complexity of $O(N^2)$, which is particularly unaffordable for large graphs. To address these deficiencies, we leverage rank learning and propose a simple yet effective model, GraphRank. Specifically, we first generate two graph views through corruption. Then, we compute the similarity of pairwise nodes (anchor node and positive node) in both views, an arbitrary node in the latter view is selected as a negative node, and its similarity with the anchor node is computed. Based on this, we introduce rank-based learning to measure similarity scores which successfully relieve the false negative provlem and decreases the time complexity from $O(N^2)$ to $O(N)$. Moreover, we conducted extensive experiments across multiple graph tasks, demonstrating that GraphRank performs favorably against other cutting-edge GCL methods in various tasks

Eukaryotic translation initiation factor 2B-beta (eIF2B β), a new class of plant virus resistance gene

Recessive resistances to plant viruses in the Potyvirus genus have been found to be based on mutations in the plant eukaryotic translation initiation factors, eIF4E and eIF4G or their isoforms. Here we report that natural, monogenic recessive resistance to the potyvirus Turnip mosaic virus (TuMV) has been found in a number of mustard (Brassica juncea) accessions. Bulked segregant analysis and sequencing of resistant and susceptible plant lines indicated the resistance is controlled by a single recessive gene, recessive TuMV resistance 03 (retr03), an allele of the eukaryotic translation initiation factor 2B-beta (eIF2Bβ). Silencing of eIF2Bβ in a TuMV-susceptible mustard plant line and expression of eIF2Bβ from a TuMV-susceptible line in a TuMV-resistant mustard plant line confirmed the new resistance mechanism. A functional copy of a specific allele of eIF2Bβ is required for efficient TuMV infection. eIF2Bβ represents a new class of virus resistance gene conferring resistance to any pathogen. eIF2B acts as a guanine nucleotide exchange factor (GEF) for its GTP-binding protein partner eIF2 via interaction with eIF2·GTP at an early step in translation initiation. Further genotyping indicated that a single non-synonymous substitution (A120G) in the N-terminal region of eIF2Bβ was responsible for the TuMV resistance. A reproducible marker has been developed, facilitating marker-assisted selection for TuMV resistance in B. juncea. Our findings provide a new target for seeking natural resistance to potyviruses and new opportunities for the control of potyviruses using genome editing techniques targeted on eIF2Bβ

Experimental study on biaxial dynamical compressive test and PFC2D numerical simulation of artificial rock sample with single joint

Dynamic biaxial compression tests and Particle Flow Code numerical simulations of the cement mortar specimens with a single joint were carried out to study the mechanical properties and crack evolution of artificial rock samples with a single joint. The effects of lateral stress 2, loading rate V , the dip angle β (between the vertical loading direction and the joint) on the biaxial compressive strength b, and the evolution lawof crackwere investigated. Test results showed that; (1) when both the dip angle β and the loading rate V remained unchanged, the biaxial compressive strength b increased with the increase in the lateral stress 2, while 2 had no obvious effect on the crack evolution law; (2) when both the dip angle β and the lateral stress 2 were kept unchanged, the loading rate V had an insignificant effect on the biaxial compressive strength b and the crack evolution law; (3) when both the lateral stress 2 and the loading rate V were constant, the biaxial compressive strength b decreased first and then increased with the increase in the dip angle β ; however, the dip angle β did not significantly affect the crack evolution law. The conclusions obtained in this paper are presented for the first time