Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection

Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system. Accurately detecting the abnormality of MTS is very critical for subsequent fault elimination. The scarcity of anomalies and manual labeling has led to the development of various self-supervised MTS anomaly detection (AD) methods, which optimize an overall objective/loss encompassing all metrics' regression objectives/losses. However, our empirical study uncovers the prevalence of conflicts among metrics' regression objectives, causing MTS models to grapple with different losses. This critical aspect significantly impacts detection performance but has been overlooked in existing approaches. To address this problem, by mimicking the design of multi-gate mixture-of-experts (MMoE), we introduce CAD, a Conflict-aware multivariate KPI Anomaly Detection algorithm. CAD offers an exclusive structure for each metric to mitigate potential conflicts while fostering inter-metric promotions. Upon thorough investigation, we find that the poor performance of vanilla MMoE mainly comes from the input-output misalignment settings of MTS formulation and convergence issues arising from expansive tasks. To address these challenges, we propose a straightforward yet effective task-oriented metric selection and p&s (personalized and shared) gating mechanism, which establishes CAD as the first practicable multi-task learning (MTL) based MTS AD model. Evaluations on multiple public datasets reveal that CAD obtains an average F1-score of 0.943 across three public datasets, notably outperforming state-of-the-art methods. Our code is accessible at https://github.com/dawnvince/MTS_CAD.Comment: 11 pages, ESEC/FSE industry track 202

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

Association of Platelet to lymphocyte ratio with non-culprit atherosclerotic plaque vulnerability in patients with acute coronary syndrome: an optical coherence tomography study

Abstract Background The platelet to lymphocyte ratio (PLR), an indirect inflammatory biomarker, has been recently demonstrated to be associated with severity of coronary artery disease. In the present study, we sought to investigate whether PLR is associated with vulnerable plaque characteristics of non-culprit lesions in patients with acute coronary syndrome (ACS). Methods The patients in our study were divided into two groups (high PLR group and low PLR group). A total of 119 non-culprit plaques from 71 patients with ACS were assessed by optical coherence tomography (OCT). Results The non-culprit plaques in high PLR group exhibited thinner fibrous cap thickness (FCT) (88.60 ± 44.70 vs. 119.28 ± 50.22 μm, P = 0.001), greater maximum lipid arc (271.73 ± 71.66 vs. 240.60 ± 76.69°, P = 0.027) and increased incidence of thin-cap fibroatheroma (TCFA) (34.0% vs. 15.9%, P = 0.022) compared with those in low PLR group. Meanwhile, PLR was negatively associated with FCT (r = −0.329, P < 0.001). Furthermore, multivariate regression analysis showed that PLR [OR: 1.023 (95% CI: 1.005–1.041), P = 0.012] and LDL-C [OR: 1.892 (95% CI: 1.106–3.239), P = 0.020] were significant predictors of TCFA. Conclusions High level of PLR may be associated with vulnerable plaque features of non-culprit lesions in patients with ACS. PLR, a cheap and easily available index, may surve as a useful inflammatory marker in reflecting plaque vulnerability

Ginsenoside Rb3 suppresses the nuclear translocation of NF-κB p65 induced by OGD-Rep in H9c2 cells.

<p>H9c2 cells were treated with 5 µM ginsenoside Rb3 followed by OGD-Rep. The cells were then subjected to an immunofluorescence assay to analyze the translocation of the NF-κB subunit p65. Ginsenoside Rb3 inhibited the nuclear translocation of p65 induced by OGD-Rep. The image was obtained at 20×magnification. Myosin heavy chain (MHC) was used as a marker of myocardial cells, and DAPI was used to stain nucleus. n = 6.</p

The effect of ginsenoside Rb3 on the upstream of NF-κB pathway in OGD-Rep-induced H9c2 cells.

<p>H9c2 cells were treated with ginsenoside Rb3 at different concentrations (5 µM or 2 µM) followed by OGD-Rep. The cells were harvested and subjected to Western blot assay to analyze the Akt protein levels, Akt phosphorylation, Foxo3a protein levels and Foxo3a phosphorylation (A), as well as JNK, JNK phosphorylation, ERK, ERK phosphorylation, p38 and p38 phosphorylation (B). The graph represented the quantitative measurement of the JNK phosphorylation and JNK protein levels. Beta-actin was used as an internal control. All data were shown as the mean ± SD. ^#P<0.05 vs. group without OGD-Rep. *P<0.05 vs. group treated with OGD-Rep alone. n = 3–4.</p

Ginsenoside Rb3 suppresses OGD-Rep-induced NF-κB activation in H9c2 cells.

<p>H9c2 cells were treated with ginsenoside Rb3 at different concentration (5 µM or 2 µM) followed by OGD-Rep. The cells were harvested and subjected to ELISA and Western blot assays. (A) The NF-κB activity was analyzed using a NF-κB p65 ELISA Kit, and the graph represented the ratio of the nuclear to the cytoplasmic p65 concentration. (B) The NF-κB p65 protein levels and NF-κB p65 phosphorylation were analyzed by Western blot assay. Beta-actin was used as an internal control. (C) The IκB-α protein levels and IκB-α phosphorylation were determined by Western blot assay, and beta-actin was used as an internal control. The graph represented the ratio of phosphorylated IκB-α to beta-actin. Ginsenoside Rb3 blocked OGD-Rep-induced increase in IκB-α phosphorylation levels. All data were shown as the mean ± SD. ^#P<0.05 vs. group without OGD-Rep. *P<0.05 vs. group treated with OGD-Rep alone. n = 4–6.</p