Multi-behavior Recommendation with SVD Graph Neural Networks

Graph Neural Networks (GNNs) has been extensively employed in the field of recommender systems, offering users personalized recommendations and yielding remarkable outcomes. Recently, GNNs incorporating contrastive learning have demonstrated promising performance in handling sparse data problem of recommendation system. However, existing contrastive learning methods still have limitations in addressing the cold-start problem and resisting noise interference especially for multi-behavior recommendation. To mitigate the aforementioned issues, the present research posits a GNNs based multi-behavior recommendation model MB-SVD that utilizes Singular Value Decomposition (SVD) graphs to enhance model performance. In particular, MB-SVD considers user preferences under different behaviors, improving recommendation effectiveness while better addressing the cold-start problem. Our model introduces an innovative methodology, which subsume multi-behavior contrastive learning paradigm to proficiently discern the intricate interconnections among heterogeneous manifestations of user behavior and generates SVD graphs to automate the distillation of crucial multi-behavior self-supervised information for robust graph augmentation. Furthermore, the SVD based framework reduces the embedding dimensions and computational load. Thorough experimentation showcases the remarkable performance of our proposed MB-SVD approach in multi-behavior recommendation endeavors across diverse real-world datasets

Region-Wise Attentive Multi-View Representation Learning for Urban Region Embeddings

Urban region embedding is an important and yet highly challenging issue due to the complexity and constantly changing nature of urban data. To address the challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER) to capture multi-view dependencies and learn expressive representations of urban regions without the constraints of rigid neighbourhood region conditions. Our model focus on learn urban region representation from multi-source urban data. First, we capture the multi-view correlations from mobility flow patterns, POI semantics and check-in dynamics. Then, we adopt global graph attention networks to learn similarity of any two vertices in graphs. To comprehensively consider and share features of multiple views, a two-stage fusion module is further proposed to learn weights with external attention to fuse multi-view embeddings. Extensive experiments for two downstream tasks on real-world datasets demonstrate that our model outperforms state-of-the-art methods by up to 17\% improvement

Hybrid Augmented Automated Graph Contrastive Learning

Graph augmentations are essential for graph contrastive learning. Most existing works use pre-defined random augmentations, which are usually unable to adapt to different input graphs and fail to consider the impact of different nodes and edges on graph semantics. To address this issue, we propose a framework called Hybrid Augmented Automated Graph Contrastive Learning (HAGCL). HAGCL consists of a feature-level learnable view generator and an edge-level learnable view generator. The view generators are end-to-end differentiable to learn the probability distribution of views conditioned on the input graph. It insures to learn the most semantically meaningful structure in terms of features and topology, respectively. Furthermore, we propose an improved joint training strategy, which can achieve better results than previous works without resorting to any weak label information in the downstream tasks and extensive evaluation of additional work

Asymmetric Diffusion Based Channel-Adaptive Secure Wireless Semantic Communications

Semantic communication has emerged as a new deep learning-based communication paradigm that drives the research of end-to-end data transmission in tasks like image classification, and image reconstruction. However, the security problem caused by semantic attacks has not been well explored, resulting in vulnerabilities within semantic communication systems exposed to potential semantic perturbations. In this paper, we propose a secure semantic communication system, DiffuSeC, which leverages the diffusion model and deep reinforcement learning (DRL) to address this issue. With the diffusing module in the sender end and the asymmetric denoising module in the receiver end, the DiffuSeC mitigates the perturbations added by semantic attacks, including data source attacks and channel attacks. To further improve the robustness under unstable channel conditions caused by semantic attacks, we developed a DRL-based channel-adaptive diffusion step selection scheme to achieve stable performance under fluctuating environments. A timestep synchronization scheme is designed for diffusion timestep coordination between the two ends. Simulation results demonstrate that the proposed DiffuSeC shows higher robust accuracy than previous works under a wide range of channel conditions, and can quickly adjust the model state according to signal-to-noise ratios (SNRs) in unstable environments

AELA-DLSTMs: Attention-enabled and location-aware double LSTMs for aspect-level sentiment classification

Aspect-level sentiment classification, as a fine-grained task in sentiment classification, aiming to extract sentiment polarity from opinions towards a specific aspect word, has been made tremendous improvements in recent years. There are three key factors for aspect-level sentiment classification: contextual semantic information towards aspect words, correlations between aspect words and their context words, and location information of context words with regard to aspect words. In this paper, two models named AE-DLSTMs (Attention-Enabled Double LSTMs) and AELA-DLSTMs (Attention-Enabled and Location-Aware Double LSTMs) are proposed for aspect-level sentiment classification. AE-DLSTMs takes full advantage of the DLSTMs (Double LSTMs) which can capture the contextual semantic information in both forward and backward directions towards aspect words. Meanwhile, a novel attention weights generating method that combines aspect words with their contextual semantic information is designed so that those weights can make better use of the correlations between aspect words and their context words. Besides, we observe that context words with different distances or different directions towards aspect words have different contributions in sentiment polarity. Based on AE-DLSTMs, the location information of context words by assigning different weights is incorporated in AELA-DLSTMs to improve the accuracy. Experiments are conducted on two English datasets and one Chinese dataset. The experimental results have confirmed that our models can make remarkable improvements and outperform all the baseline models in all datasets, improving the accuracy of 1.67 percent to 4.77 percent in different datasets compared with baseline models

Regulatory network of GSK3-like kinases and their role in plant stress response

Glycogen synthase kinase 3 (GSK3) family members are evolutionally conserved Ser/Thr protein kinases in mammals and plants. In plants, the GSK3s function as signaling hubs to integrate the perception and transduction of diverse signals required for plant development. Despite their role in the regulation of plant growth and development, emerging research has shed light on their multilayer function in plant stress responses. Here we review recent advances in the regulatory network of GSK3s and the involvement of GSK3s in plant adaptation to various abiotic and biotic stresses. We also discuss the molecular mechanisms underlying how plants cope with environmental stresses through GSK3s-hormones crosstalk, a pivotal biochemical pathway in plant stress responses. We believe that our overview of the versatile physiological functions of GSK3s and underlined molecular mechanism of GSK3s in plant stress response will not only opens further research on this important topic but also provide opportunities for developing stress-resilient crops through the use of genetic engineering technology

Oridonin nanosuspension was more effective than free oridonin on G2/M cell cycle arrest and apoptosis in the human pancreatic cancer PANC-1 cell line

Oridonin, a diterpenoid isolated from Rabdosia rubescencs, has been reported to have antitumor effects. However, low solubility has limited its clinical applications. Preparation of drugs in the form of nanosuspensions is an extensively utilized protocol. In this study, we investigated the anticancer activity of oridonin and oridonin nanosuspension on human pancreatic carcinoma PANC-1 cells. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay was performed to investigate the effect of oridonin on cell growth. Propidium iodide and Hoechst 33342 staining were used to detect morphologic changes. The percentage of apoptosis and cell cycle progression was determined by flow cytometric method staining with propidium iodide. Annexin V-fluorescein isothiocyanate (FITC)/PI staining was used to evaluate cell apoptosis by flow cytometry. Caspase-3 activity was measured by spectrophotometry. The apoptotic and cell cycle protein expression were determined by Western blot analysis. Both oridonin and oridonin nanosuspension induced apoptosis and G2/M phase cell cycle arrest, and the latter had a more significant cytotoxic effect. The ratio of Bcl-2/Bax protein expression was decreased and caspase- 3 activity was stimulated. The expression of cyclin B1 and p-cdc2 (T161) was suppressed. Our results showed that oridonin nanosuspension was more effective than free oridonin on G2/M cell cycle arrest and apoptosis in the human pancreatic cancer PANC-1 cell line

Mitochondrial ferritin attenuates cerebral ischaemia/reperfusion injury by inhibiting ferroptosis

Ischaemic stroke is becoming the most common cerebral disease in aging populations, but the underlying molecular mechanism of the disease has not yet been fully elucidated. Increasing evidence has indicated that an excess of iron contributes to brain damage in cerebral ischaemia/reperfusion (I/R) injury. Although mitochondrial ferritin (FtMt) plays a critical role in iron homeostasis, the molecular function of FtMt in I/R remains unknown. We herein report that FtMt levels are upregulated in the ischaemic brains of mice. Mice lacking FtMt experience more severe brain damage and neurological deficits, accompanied by typical molecular features of ferroptosis, including increased lipid peroxidation and disturbed glutathione (GSH) after cerebral I/R. Conversely, FtMt overexpression reverses these changes. Further investigation shows that Ftmt ablation promotes I/R-induced inflammation and hepcidin-mediated decreases in ferroportin1, thus markedly increasing total and chelatable iron. The elevated iron consequently facilitates ferroptosis in the brain of I/R. In brief, our results provide evidence that FtMt plays a critical role in protecting against cerebral I/R-induced ferroptosis and subsequent brain damage, thus providing a new potential target for the treatment/prevention of ischaemic stroke

Association analysis of important agronomical traits of maize inbred lines with SSRs

Abstract The genetic markers of important traits are evaluated in order to improve the maize inbred lines. Ninety-four maize inbred lines were used to assess the genetic and phenotypic diversity and make association analysis of 26 agronomical traits with 204 genome-wide SSR markers, which were divided into five subpopulations by a model based population structure analysis. The population consisted of 94 maize inbred lines, presented high genetic diversity and significant linkage disequilibrium (LD), and could be used in the detection of genome-wide SSR marker-phenotype association. Although a total of 106 loci were associated with the trait of the mean results of two years at P<0.01 level, thirty-nine association loci were detected with an MLM association analysis model to existing significant association (P<0.05) with 17 traits in two years, simultaneously, in which there were three loci associated with PH, four loci with AD, five loci with KRN, three loci with HKW, etc. Five association loci were new discovery, which were bnlg2162, bnlg1118, phi077, umc1161 with BYC, and bnlg1118 with GLN. The strongest association loci were umc1917 with AD and HKW (P<0.01), umc2025 with CD (P<0.0001), etc. The number of associated loci detected on chromosome 1 was thirteen, which was more than chromosome 2 and 5(5), and more than chromosome 4(4), etc. The above results were useful for genetic improvement and molecular maker-assisted breeding in maize