Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data

Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. However, most previous works assumed that each view is complete and aligned. This leads to an inevitable deterioration in their performance when encountering practical problems such as missing or unaligned views. To address the challenge of representation learning on partially aligned multi-view data, we propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Compared with current approaches, the proposed method has the following merits: (1) our model is an end-to-end framework that simultaneously performs view-specific representation learning via view-specific autoencoders and cluster-level data aligning by combining multi-view information with the cross-view graph contrastive learning; (2) it is easy to apply our model to explore information from three or more modalities/sources as the cross-view graph contrastive learning is devised. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks

Effects of tumor necrosis factor-α on glucose uptake in human granulosa cells under high androgen conditions

Objective(s): Hyperandrogenism is a key pathological characteristic of polycystic ovary syndrome (PCOS). Tumor necrosis factor α (TNF-α) is both an adipokine and a chronic inflammatory factor, which has been proven to be involved in the pathologic process of PCOS. This study aimed to determine how TNF-α affects glucose uptake in human granulosa cells in the presence of high testosterone concentration. Materials and Methods: KGN cell line was treated with testosterone and TNF-α alone or co-culture combination for 24 hr, or starved for 24 hr. Quantitative real-time polymerase chain reaction (qPCR) and western blot were performed to measure glucose transporter type 4 (GLUT4) message RNA (mRNA) and protein expression in treated KGN cells. Glucose uptake and GLUT4 expression were detected by immunofluorescence (IF). Furthermore, western blot was performed to measure the contents in the nuclear factor kappa-B (NF-κB) pathway. Meantime, upon addition of TNF-α receptor II (TNFRII) inhibitor or Inhibitor of nuclear factor kappa-B kinase subunit beta (IKKβ) antagonist to block the TNFRII-IKKβ-NF-κB signaling pathway, the glucose uptake in KGN cells and GLUT4 translocation to cytomembrane were detected by IF, and related proteins in TNFRII-IKKβ-NF-κB were detected by western blot.Results: The glucose uptake in Testosterone + TNF-α group was lowered significantly, and Total GLUT4 mRNA and proteins were reduced significantly. GLUT4 translocation to cytomembrane was tarnished visibly; concurrently, the phosphorylated proteins in the TNFRII-IKKβ-NF-κB signaling pathway were enhanced significantly. Furthermore, upon addition of TNFRII inhibitor or IKKβ inhibitor to block the TNFRII-IKKβ-NF-κB signaling pathway, the glucose uptake of treated granulosa cells was improved. Conclusion: TNFRII and IKKβ antagonists may improve glucose uptake in granulosa cells induced by TNF-α by blocking the TNFRII-IKKβ-NF-κB signaling pathway under high androgen conditions

Survey of Image Adversarial Example Defense Techniques

The rapid and extensive growth of artificial intelligence introduces new security challenges. The generation and defense of adversarial examples for deep neural networks is one of the hot spots. Deep neural networks are  most widely used in the field of images and most easily cheated by image adversarial examples. The research on the defense techniques for image adversarial examples is an important tool to improve the security of AI applications. There is no standard explanation for the existence of image adversarial examples, but it can be observed and understood from different dimensions, which can provide insights for proposing targeted defense approaches. This paper sorts out and analyzes current mainstream hypotheses of the reason for the existence of adversarial examples, such as the blind spot hypothesis, linear hypothesis, decision boundary hypothesis, and feature hypothesis, and the correlations between various hypotheses and typical adversarial example generation methods. Based on this, this paper summarizes the image adversarial example defense techniques in two dimensions, model-based and data-based, and compares and analyzes the adaptation scenarios, advantages and disadvantages of different technical methods. Most of the existing image adversarial example defense techniques are aimed at defending against specific adversarial example generation methods, and there is no universal defense theory and method yet. In the real application, it needs to consider the specific application scenarios, potential security risks and other factors, optimize and combine the configuration in the existing defense methods. Future researchers can deepen their technical research in terms of generalized defense theory, evaluation of defense effectiveness, and systematic protection strategies

Correlated states in twisted double bilayer graphene

Electron-electron interactions play an important role in graphene and related systems and can induce exotic quantum states, especially in a stacked bilayer with a small twist angle. For bilayer graphene where the two layers are twisted by a "magic angle", flat band and strong many-body effects lead to correlated insulating states and superconductivity. In contrast to monolayer graphene, the band structure of untwisted bilayer graphene can be further tuned by a displacement field, providing an extra degree of freedom to control the flat band that should appear when two bilayers are stacked on top of each other. Here, we report the discovery and characterization of such displacement-field tunable electronic phases in twisted double bilayer graphene. We observe insulating states at a half-filled conduction band in an intermediate range of displacement fields. Furthermore, the resistance gap in the correlated insulator increases with respect to the in-plane magnetic fields and we find that the g factor according to spin Zeeman effect is ~2, indicating spin polarization at half filling. These results establish the twisted double bilayer graphene as an easily tunable platform for exploring quantum many-body states

Transcriptome analysis reveals salt-stress-regulated biological processes and key pathways in roots of cotton (Gossypium hirsutum L.)

AbstractHigh salinity is one of the main factors limiting cotton growth and productivity. The genes that regulate salt stress in TM-1 upland cotton were monitored using microarray and real-time PCR (RT-PCR) with samples taken from roots. Microarray analysis showed that 1503 probe sets were up-regulated and 1490 probe sets were down-regulated in plants exposed for 3h to 100mM NaCl, and RT-PCR analysis validated 42 relevant/related genes. The distribution of enriched gene ontology terms showed such important processes as the response to water stress and pathways of hormone metabolism and signal transduction were induced by the NaCl treatment. Some key regulatory gene families involved in abiotic and biotic sources of stress such as WRKY, ERF, and JAZ were differentially expressed. Our transcriptome analysis might provide some useful insights into salt-mediated signal transduction pathways in cotton and offer a number of candidate genes as potential markers of tolerance to salt stress

Cardiac Arrhythmia classification based on 3D recurrence plot analysis and deep learning

Artificial intelligence (AI) aided cardiac arrhythmia (CA) classification has been an emerging research topic. Existing AI-based classification methods commonly analyze electrocardiogram (ECG) signals in lower dimensions, using one-dimensional (1D) temporal signals or two-dimensional (2D) images, which, however, may have limited capability in characterizing lead-wise spatiotemporal correlations, which are critical to the classification accuracy. In addition, existing methods mostly assume that the ECG data are linear temporal signals. This assumption may not accurately represent the nonlinear, nonstationary nature of the cardiac electrophysiological process. In this work, we have developed a three-dimensional (3D) recurrence plot (RP)-based deep learning algorithm to explore the nonlinear recurrent features of ECG and Vectorcardiography (VCG) signals, aiming to improve the arrhythmia classification performance. The 3D ECG/VCG images are generated from standard 12 lead ECG and 3 lead VCG signals for neural network training, validation, and testing. The superiority and effectiveness of the proposed method are validated by various experiments. Based on the PTB-XL dataset, the proposed method achieved an average F1 score of 0.9254 for the 3D ECG-based case and 0.9350 for the 3D VCG-based case. In contrast, recently published 1D and 2D ECG-based CA classification methods yielded lower average F1 scores of 0.843 and 0.9015, respectively. Thus, the improved performance and visual interpretability make the proposed 3D RP-based method appealing for practical CA classification

Combination of 4-1BB and DAP10 promotes proliferation and persistence of NKG2D(bbz) CAR-T cells

Chimeric antigen receptor (CAR)-T cell therapy has been shown to have considerable therapeutic effects in hematological malignancies, and NKG2D(z) CAR-T cell therapy has been verified to be safe based on clinical trials. However, due to the poor persistence of NKG2D(z) CAR-T cells, their therapeutic effect is not obvious. Here, we constructed NKG2D(bbz) CAR-T cells that can simultaneously activate 4-1BB and DAP10 costimulatory signaling. They were found to be cytotoxic to the target cells in vitro and in vivo. They exhibited low differentiation, low exhaustion, and good proliferation. Importantly, the proportions of central memory T (Tcm) and stem cell-like memory T (Tscm) cell subsets were strikingly increased. After long-term incubation with the target cells, they displayed reduced exhaustion compared to NKG2D(z) CAR-T cells. Further, in the presence of the phosphoinositide 3-kinase (PI3K) inhibitor LY294002, they exhibited reduced exhaustion and apoptosis, upregulated Bcl2 expression, and an increased proportion of Tcm cell subsets. Finally, NKG2D(bbz) CAR-T cells had better antitumor effects in vivo. In summary, the results showed that NKG2D(bbz) CAR-T cells may be valuable for cellular immunotherapy of cancer

Layer-by-Layer Epitaxy of Multilayer MoS2 Wafers

Two-dimensional (2D) semiconductor of MoS2 has great potential for advanced electronics technologies beyond silicon1-9. So far, high-quality monolayer MoS2 wafers10-12 are already available and various demonstrations from individual transistors to integrated circuits have also been shown13-15. In addition to the monolayer, multilayers have narrower band gaps but improved carrier mobilities and current capacities over the monolayer5,16-18. However, achieving high-quality multilayer MoS2 wafers remains a challenge. Here we report the growth of high quality multilayer MoS2 4-inch wafers via the layer-by-layer epitaxy process. The epitaxy leads to well-defined stacking orders between adjacent epitaxial layers and offers a delicate control of layer numbers up to 6. Systematic evaluations on the atomic structures and electronic properties were carried out for achieved wafers with different layer numbers. Significant improvements on device performances were found in thicker-layer field effect transistors (FETs), as expected. For example, the average field-effect mobility ({\mu}FE) at room temperature (RT) can increase from ~80 cm2V-1s-1 for monolayer to ~110/145 cm2V-1s-1 for bilayer/trilayer devices. The highest RT {\mu}FE=234.7 cm2V-1s-1 and a record-high on-current densities of 1.704 mA{\mu}m-1 at Vds=2 V were also achieved in trilayer MoS2 FETs with a high on/off ratio exceeding 107. Our work hence moves a step closer to practical applications of 2D MoS2 in electronics.Comment: 13 pages,4 Figure