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Sepsis-induced acute lung injury (ALI) is associated with dysregulated inflammatory reactions. MiR-19b-3p level was reported to be downregulated in patients with sepsis. In order to evaluate the role of miR-19b-3p in sepsis, cecum ligation and puncture (CLP)-induced mouse sepsis model and lpopolysaccharide (LPS)-treated pulmonary microvascular endothelial cells (PMVECs) were used. For in vivo study, lung tissue was harvested for hematoxylin and eosin (H&E) staining, TNF-α, IL-6, IL-1β and p-p65, p-IκB measuring. Cell apoptosis was assessed by TUNEL assay. For in vitro study, cell proliferation and apoptosis were detected by CCK-8 and flow cytometry, respectively. Methylation of miR-19b-3p promoter was measured by methylation-specific PCR (MSP) assay. The target of miR-19b-3p was determined by dual-luciferase reporter gene assay. The level of miR-19b-3p was determined to be downregulated in vitro and in vivo. In addition, miR-19b-3p protected mice from inflammation injury through inhibiting NF-κB signaling pathway. Overexpression of miR-19b-3p increased cell viability, decreased apoptosis and pro-inflammatory cytokines secretion in LPS-treated PMVECs. Besides these, KLF7 was confirmed as the target of miR-19b-3p. And methylation of miR-19b-3p was the reason of decreased miR-19b-3p level. In conclusion, miR-19b-3p protected cells from sepsis-induced inflammation injury via inhibiting NF-κB signaling pathway, and KLF7 was a potential target. This article is protected by copyright. All rights reserved.

Background: The COVID-19 global pandemic has posed unprecedented challenges to health care systems all over the world. The speed of the viral spread results in a tsunami of patients, which begs for a reliable screening tool using readily available data to predict disease progression.Methods: Multicenter retrospective cohort study was performed to develop and validate a triage model. Patient demographic and non-laboratory clinical data were recorded. Using only the data from Zhongnan Hospital, step-wise multivariable logistic regression was performed, and a prognostic nomogram was constructed based on the independent variables identifies. The discrimination and calibration of the model were validated. External independent validation was performed to further address the utility of this model using data from Jinyintan Hospital.Results: A total of 716 confirmed COVID-19 cases from Zhongnan Hospital were included for model construction. Men, increased age, fever, hypertension, cardio-cerebrovascular disease, dyspnea, cough, and myalgia are independent risk factors for disease progression. External independent validation was carried out in a cohort with 201 cases from Jinyintan Hospital. The area under the curve (AUC) was 0.787 (95% confidence interval [CI]: 0.747–0.827) in the training group and 0.704 (95% CI: 0.632–0.777) in the validation group.Conclusions: We developed a novel triage model based on basic and clinical data. Our model could be used as a pragmatic screening aid to allow for cost efficient screening to be carried out such as over the phone, which may reduce disease propagation through limiting unnecessary contact. This may help allocation of limited medical resources.

Identifying radio anomalies is one of the main purposes of radio monitoring. The current radio anomaly signal identification is mainly finished manually by the Radio monitor, using professional radio knowledge and their work experience. However, because the anomaly signal is hidden in the "massive" data, accompanied by a large amount of noise, and also data imbalance, the anomaly signal is difficult to find. In this paper, we combine the data unevenness processing method SMOTE and the support vector machine (SVM), gradient lifting tree (GDBT), and other classification algorithms to identification the anomaly signal during radio monitoring. Experimental results show that our method can improve the efficiency of existing radio anomaly signal recognization. Moreover, our experiments also show that data imbalance processing plays a key role in anomaly signal recognition.

Deep neural networks have been shown vulnerable to adversarial attacks launched by adversarial examples. These examples’ transferability makes an attack in the real-world feasible, which poses a security threat to deep learning. Considering the limited representation capacity of a single deep model, the transferability of an adversarial example generated by a single attack model would cause the failure of attacking other different models. In this paper, we propose a new adversarial attack method, named EnsembleFool, which flexibly integrates multiple models to enhance adversarial examples’ transferability. Specifically, the model confidence concerning an input example reveals the risk of a successful attack. In an iterative attacking case, the result of a previous attack could guide us to enforce a new attack that possesses a higher probability of success. Regarding this, we design a series of integration strategies to improve the adversarial examples in each iteration. Extensive experiments on ImageNet indicate that the proposed method has superior attack performance and transferability than state-of-the-art methods.

A new method of incipient fault diagnosis for analog circuits based on MHMM-SVM was presented. The model of MHMM has the ability of dealing with continuous dynamic signals and is suitable to depict for the samples that are in the same kinds, the model of SVM is based on Structural Risk Minimization and is adapt in classifying the different kinds. The two models are complementary for each other, and the method made them fused. At first, the dimensions of the experimental samples were decreased and divorced easily with the LDA technology, and secondly, the MHMM-SVM model was built using the samples. Finally, from experimental results that are compared with MHMM-based and SVM-based diagnostician methods, the conclusion can be drawn that the method has certain advantages for the incipient fault diagnosis.