Plastic stabilizers (PSs) are chemical additives that are widely used to inhibit the degradation of plastics. However, their safety concerns and potential carcinogenic risks remain unclear. This study employed network toxicology strategies to elucidate the potential toxic effects and underlying molecular mechanisms of representative PSs, including 2,6-di-tert-butylphenol (2,6-DTB), tert-butylhydroquinone (TBHQ), and 2-(2H-benzotriazol-2-yl)-4,6-di-tert-pentylphenol (UV-328) in breast cancer (BC). Herein, we identified 69 potential genes related to PSs exposure and BC, and optimized five core targets: GSK3B, MAPK14, PARP1, PIM1, and TRDMT1, through subsequent LASSO and SVM algorithms. Based on these core genes, we constructed risk score and nomogram models, both of which revealed that high expression of these five core genes predicts poor prognosis in BC patients. Additionally, molecular docking and dynamic simulations indicated high-affinity interactions between PSs and these core targets (binding energies < -5 kcal/mol). Further correlation analysis with prediction analysis of microarray 50 (PAM50) revealed increased expression of all core genes in the basal-like subtype, especially PIM1 and TRDMT1, which also exhibited the highest risk scores. In vitro, PSs transcriptionally upregulated MAPK14, PIM1, and TRDMT1, with STAT3 mediating their transcription. Importantly, cell counting kit-8 and wound healing assays demonstrated that PSs promote BC cell proliferation and migration. Our research re-evaluates the carcinogenic risks of plastic stabilizers and suggests that PSs may enhance breast cancer progression via targets such as MAPK14, PIM1, and TRDMT1. This study introduces a new approach for evaluating the safety of plastic additives and offers novel insights into the toxicological effects of PSs.

Modern neural recording techniques such as two-photon imaging or Neuropixel probes allow to acquire vast time-series datasets with responses of hundreds or thousands of neurons. Contrastive learning is a powerful self-supervised framework for learning representations of complex datasets. Existing applications for neural time series rely on generic data augmentations and do not exploit the multi-trial data structure inherent in many neural datasets. Here we present TRACE, a new contrastive learning framework that averages across different subsets of trials to generate positive pairs. TRACE allows to directly learn a two-dimensional embedding, combining ideas from contrastive learning and neighbor embeddings. We show that TRACE outperforms other methods, resolving fine response differences in simulated data. Further, using in vivo recordings, we show that the representations learned by TRACE capture both biologically relevant continuous variation, cell-type-related cluster structure, and can assist data quality control.

Anomaly detection (such as telecom fraud detection and medical image detection) has attracted the increasing attention of people. The complex interaction between multiple entities widely exists in the network, which can reflect specific human behavior patterns. Such patterns can be modeled by higher-order network structures, thus benefiting anomaly detection on attributed networks. However, due to the lack of an effective mechanism in most existing graph learning methods, these complex interaction patterns fail to be applied in detecting anomalies, hindering the progress of anomaly detection to some extent. In order to address the aforementioned issue, we present a higher-order structure based anomaly detection (GUIDE) method. We exploit attribute autoencoder and structure autoencoder to reconstruct node attributes and higher-order structures, respectively. Moreover, we design a graph attention layer to evaluate the significance of neighbors to nodes through their higher-order structure differences. Finally, we leverage node attribute and higher-order structure reconstruction errors to find anomalies. Extensive experiments on five real-world datasets (i.e., ACM, Citation, Cora, DBLP, and Pubmed) are implemented to verify the effectiveness of GUIDE. Experimental results in terms of ROC-AUC, PR-AUC, and Recall@K show that GUIDE significantly outperforms the state-of-art methods.

In the age of the Internet of everything, the Edge-Cloud collaborative service support has become a very promising development direction in the application field of the Internet of things. However, when various service components are deployed both on the cloud and the edge, the subsidence and decentralization of computing resources also have a great impact on the performance of composite services. This paper proposes an optimal composite service selection model based on Petri nets. In order to compare the composite service paths, this paper adopts a dynamic evaluation model of composite service quality based on Petri nets. Firstly, all kinds of Petri net models for the implementation structure of composite services are constructed. And then the QoS computing rules corresponding to these structures are given based on the QoS of each service component on the edge of the cloud. Finally, the dynamic execution process of each composite service with a feasible path is simulated through Petri net, and the overall performance of the service is evaluated based on the dynamic simulation of its Petri net model, finally, the optimal service path is selected among the combined services that meet the user's requirements.. Through case analysis and comparison, the feasibility and effectiveness of the method are verified by an example analysis.