Structural Imbalance Aware Graph Augmentation Learning

Graph machine learning (GML) has made great progress in node classification, link prediction, graph classification and so on. However, graphs in reality are often structurally imbalanced, that is, only a few hub nodes have a denser local structure and higher influence. The imbalance may compromise the robustness of existing GML models, especially in learning tail nodes. This paper proposes a selective graph augmentation method (SAug) to solve this problem. Firstly, a Pagerank-based sampling strategy is designed to identify hub nodes and tail nodes in the graph. Secondly, a selective augmentation strategy is proposed, which drops the noisy neighbors of hub nodes on one side, and discovers the latent neighbors and generates pseudo neighbors for tail nodes on the other side. It can also alleviate the structural imbalance between two types of nodes. Finally, a GNN model will be retrained on the augmented graph. Extensive experiments demonstrate that SAug can significantly improve the backbone GNNs and achieve superior performance to its competitors of graph augmentation methods and hub/tail aware methods.Comment: 13 pages, 11 figures, 7 table

Mechanisms of Smoothened Regulation in Hedgehog Signaling

The seven-transmembrane protein, Smoothened (SMO), has shown to be critical for the hedgehog (HH) signal transduction on the cell membrane (and the cilium in vertebrates). SMO is subjected to multiple types of post-translational regulations, including phosphorylation, ubiquitination, and sumoylation, which alter SMO intracellular trafficking and cell surface accumulation. Recently, SMO is also shown to be regulated by small molecules, such as oxysterol, cholesterol, and phospholipid. The activity of SMO must be very well balanced by these different mechanisms in vivo because the malfunction of SMO will not only cause developmental defects in early stages, but also induce cancers in late stages. Here, we discuss the activation and inactivation of SMO by different mechanisms to better understand how SMO is regulated by the graded HH signaling activity that eventually governs distinct development outcomes

Vasculoprotective effects of rosiglitazone through modulating renin-angiotensin system in vivo and vitro

<p>Abstract</p> <p>Background</p> <p>The peroxisome proliferator-activated receptor-γ (PPARγ) agonist rosiglitazone has been suggested to exert cardiovascular protection through the improvement of lipid metabolism, anti-inflammation, anti-proliferation etc. However, whether renin-angiotensin system (RAS) is involved in the vascular protective effects of PPARγ agonists is not fully understood. The present study aimed to investigate the effects of the renin-angiotensin system in vascular protection mediated by PPARγ agonists.</p> <p>Objective</p> <p>To investigate the actions of the renin-angiotensin system in vascular protection mediated by activation of PPARγ in vivo and in vitro.</p> <p>Methods</p> <p>Rats were fed a regular diet (n = 8), a cholesterol-rich diet plus methylthiouracil (80 mg/Kg/day, n = 10), a cholesterol-rich diet plus methylthiouracil and rosiglitazone (4 mg/kg/day, n = 10). The rosiglitazone treatment was started from one month after the start of cholesterol-rich diet plus methylthiouracil, and lasted five months. Cultured vascular smooth muscle cells (VSMCs) were pretreated with 1 μmol/L angiotensin II (ANG II) for 6 h and randomly divided into the control group; the ANG II group (1 μmol/L ANG II); the groups respectively treated with different concentration rosiglitazone (20, 30, 50) μmol/L for 12 h; the groups treated with 30 μmol/L rosiglitazone for (6, 12, 24) h. Morphology changes of the aortic tissues were observed by hematoxylin and eosin stain. The VSMC growth was detected by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) colorimetric assay. Angiotensin II and expression of angiotensin receptors were determined by radioimmunoassay, reverse transcription polymerase chain reaction (RT-PCR), western blot, and immunohistochemistry.</p> <p>Results</p> <p>After 6 months, lipid deposition, VSMC proliferation and migration toward intima were observed in aortic tissues in the rats on a cholesterol-rich diet plus methylthiouracil, while these pathological changes induced by the cholesterol-rich diet were significantly suppressed by rosiglitazone. In addition, VSMC proliferation induced by ANG II was markedly inhibited by rosiglitazone. Rosiglitazone markedly down-regulated expression of angiotensin type 1 receptor (AT₁R) and up-regulated expression of angiotensin type 2 receptor (AT₂R) in the aortic tissues and ANG II-treated VSMCs.</p> <p>Conclusions</p> <p>The present study demonstrated that PPARγ agonist rosiglitazone suppressed ANG II-induced VSMC proliferation in vitro and early atherosclerotic formation evoked by cholesterol-rich diet in vivo. These vasculoprotective effects of rosiglitazone were mediated at least partially by reduction in local tissue ANG II concentration, down-regulation of AT₁R expression and up-regulation of AT₂R expression both at the mRNA and protein levels.</p

Modeling User Viewing Flow using Large Language Models for Article Recommendation

This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles. Specifically, we employ a user constant viewing flow modeling method to summarize the user's general interest to recommend articles. We utilize Large Language Models (LLMs) to capture constant user preferences from previously clicked articles, such as skills and positions. Then we design the user instant viewing flow modeling method to build interactions between user-clicked article history and candidate articles. It attentively reads the representations of user-clicked articles and aims to learn the user's different interest views to match the candidate article. Our experimental results on the Alibaba Technology Association (ATA) website show the advantage of SINGLE, which achieves 2.4% improvements over previous baseline models in the online A/B test. Our further analyses illustrate that SINGLE has the ability to build a more tailored recommendation system by mimicking different article viewing behaviors of users and recommending more appropriate and diverse articles to match user interests.Comment: 8 pages

Combating acquired resistance to MAPK inhibitors in melanoma by targeting Abl1/2-mediated reactivation of MEK/ERK/MYC signaling.

Metastatic melanoma remains an incurable disease for many patients due to the limited success of targeted and immunotherapies. BRAF and MEK inhibitors reduce metastatic burden for patients with melanomas harboring BRAF mutations; however, most eventually relapse due to acquired resistance. Here, we demonstrate that ABL1/2 kinase activities and/or expression are potentiated in cell lines and patient samples following resistance, and ABL1/2 drive BRAF and BRAF/MEK inhibitor resistance by inducing reactivation of MEK/ERK/MYC signaling. Silencing/inhibiting ABL1/2 blocks pathway reactivation, and resensitizes resistant cells to BRAF/MEK inhibitors, whereas expression of constitutively active ABL1/2 is sufficient to promote resistance. Significantly, nilotinib (2nd generation ABL1/2 inhibitor) reverses resistance, in vivo, causing prolonged regression of resistant tumors, and also, prevents BRAFi/MEKi resistance from developing in the first place. These data indicate that repurposing the FDA-approved leukemia drug, nilotinib, may be effective for prolonging survival for patients harboring BRAF-mutant melanomas

Explainable machine learning models for predicting 30-day readmission in pediatric pulmonary hypertension: A multicenter, retrospective study

BackgroundShort-term readmission for pediatric pulmonary hypertension (PH) is associated with a substantial social and personal burden. However, tools to predict individualized readmission risk are lacking. This study aimed to develop machine learning models to predict 30-day unplanned readmission in children with PH.MethodsThis study collected data on pediatric inpatients with PH from the Chongqing Medical University Medical Data Platform from January 2012 to January 2019. Key clinical variables were selected by the least absolute shrinkage and the selection operator. Prediction models were selected from 15 machine learning algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC). The outcome of the predictive model was interpreted by SHapley Additive exPlanations (SHAP).ResultsA total of 5,913 pediatric patients with PH were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.81 (95% CI: 0.77–0.86), high accuracy for 0.74 (95% CI: 0.72–0.76), sensitivity 0.78 (95% CI: 0.69–0.87), and specificity 0.74 (95% CI: 0.72–0.76). Age, length of stay (LOS), congenital heart surgery, and nonmedical order discharge showed the greatest impact on 30-day readmission in pediatric PH, according to SHAP results.ConclusionsThis study developed a CatBoost model to predict the risk of unplanned 30-day readmission in pediatric patients with PH, which showed more significant performance compared with traditional logistic regression. We found that age, LOS, congenital heart surgery, and nonmedical order discharge were important factors for 30-day readmission in pediatric PH

Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting

Graph convolutional neural networks (GCNN) have become an increasingly active field of research. It models the spatial dependencies of nodes in a graph with a pre-defined Laplacian matrix based on node distances. However, in many application scenarios, spatial dependencies change over time, and the use of fixed Laplacian matrix cannot capture the change. To track the spatial dependencies among traffic data, we propose a dynamic spatio-temporal GCNN for accurate traffic forecasting. The core of our deep learning framework is the finding of the change of Laplacian matrix with a dynamic Laplacian matrix estimator. To enable timely learning with a low complexity, we creatively incorporate tensor decomposition into the deep learning framework, where real-time traffic data are decomposed into a global component that is stable and depends on long-term temporal-spatial traffic relationship and a local component that captures the traffic fluctuations. We propose a novel design to estimate the dynamic Laplacian matrix of the graph with above two components based on our theoretical derivation, and introduce our design basis. The forecasting performance is evaluated with two realtime traffic datasets. Experiment results demonstrate that our network can achieve up to 25% accuracy improvement

Conserved and Diversified Mechanism of Autophagy between Plants and Animals upon Various Stresses

Autophagy is a highly conserved degradation mechanism in eukaryotes, executing the breakdown of unwanted cell components and subsequent recycling of cellular material for stress relief through vacuole-dependence in plants and yeast while it is lysosome-dependent in animal manner. Upon stress, different types of autophagy are stimulated to operate certain biological processes by employing specific selective autophagy receptors (SARs), which hijack the cargo proteins or organelles to the autophagy machinery for subsequent destruction in the vacuole/lysosome. Despite recent advances in autophagy, the conserved and diversified mechanism of autophagy in response to various stresses between plants and animals still remain a mystery. In this review, we intend to summarize and discuss the characterization of the SARs and their corresponding processes, expectantly advancing the scope and perspective of the evolutionary fate of autophagy between plants and animals

Ginsenoside Rb3 protects cardiomyocytes against ischemia-reperfusion injury via the inhibition of JNK-mediated NF-κB pathway: a mouse cardiomyocyte model.

Ginsenoside Rb3 is extracted from the plant Panax ginseng and plays important roles in cardiovascular diseases, including myocardial ischemia-reperfusion (I/R) injury. NF-κB is an important transcription factor involved in I/R injury. However, the underlying mechanism of ginsenoside Rb3 in myocardial I/R injury remains poorly understood. In the current study, a model of myocardial I/R injury was induced via oxygen and glucose deprivation (OGD) followed by reperfusion (OGD-Rep) in mouse cardiac myoblast H9c2 cells. Our data demonstrate that ginsenoside Rb3 suppresses OGD-Rep-induced cell apoptosis by the suppression of ROS generation. By detecting the NF-κB signaling pathway, we discover that the protective effect of ginsenoside Rb3 on the OGD-Rep injury is closely related to the inhibition of NF-κB activity. Ginsenoside Rb3 inhibits the upregulation of phospho-IκB-α and nuclear translocation of NF-κB subunit p65 which are induced by ORD-Rep injury. In addition, the extract also inhibits the OGD-Rep-induced increase in the expression of inflammation-related factors, such as IL-6, TNF-α, monocyte chemotactic protein-1 (MCP-1), MMP-2 and MMP-9. However, LPS treatment alleviates the protective roles of ginsenoside Rb3 and activates the NF-κB pathway. Finally, the upstream factors of NF-κB were analyzed, including the Akt/Foxo3a and MAPK signaling pathways. We find that ginsenoside Rb3 pretreatment only decreases the phosphorylation of JNK induced by OGD-Rep injury, an indicator of the MAPK pathway. Importantly, an inhibitor of phospho-JNK, SP600125, protects against OGD-Rep induced apoptosis and inhibited NF-κB signaling pathway, similar to the roles of ginsenoside Rb3. Taken together, our results demonstrate that the protective effect of ginsenoside Rb3 on the OGD-Rep injury is attributed to the inhibition of JNK-mediated NF-κB activation, suggesting that ginsenoside Rb3 has the potential to serve as a novel therapeutic agent for myocardial I/R injury