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Abstract Anomaly detection is a highly important task in the field of data analysis. Traditional anomaly detection approaches often strongly depend on data size, structure and features, while introducing the idea of ensemble into anomaly detection can greatly improve the generalization ability. Ensemble-based anomaly detection methods still face some challenges, however, such as data imbalance, time and space demand and the selection of base detectors. To this end, we propose a selective ensemble method for anomaly detection based on parallel learning (SEAD-PL). First, a differentiated stratified sampling method is designed to alleviate the problem of data imbalance. Then, a distributed parallel training frame is built to address the problem of excessive time and space consumption for base detector training. Finally, a clustering-based ensemble selection strategy is introduced to balance the accuracy and diversity of base detectors. Experiments are performed on six datasets, which demonstrate that the proposed method has obvious advantages over four selected methods.

The avionics network supports high-safety-level flight operations, with the analysis of transmission failures serving as a crucial means for its safety evaluation. Due to the time-dependent nature of the failure probability in avionics networks, traditional constant and unchangeable probability values can deviate from the actual situation under specific conditions. This deviation may lead to inadequate responses to occasional events and potentially cause flight accidents. A Dynamic Fault Tree (DFT) model for civil aircraft avionics network transmission failures, based on an optimized extended fuzzy algorithm, is introduced in this paper. Initially focusing on event correlations, a DFT is established for the transmission failure of the Avionics Full Duplex Switched Ethernet (AFDX). Subsequently, considering the variations between events, triangular fuzzy processing is applied to the event failure rates based on relative confidence levels. Finally, by optimizing the weakest t-norm operator, the failure probability intervals are aggregated and the fuzzy scale is regulated. Experimental results demonstrate that, compared to the static-minimum t-norm and traditional weakest t-norm methods, the proposed approach enhances the accuracy of the fuzzy failure probability intervals by 66.15% and 40.59%, respectively. Concurrently, it maintains consistency in the ranking of event importance, highlighting the superior effectiveness of the proposed method in analyzing transmission failures in avionics networks.

Background and aimsDyslipidemia is known to contribute to arterial stiffness, while the inverse association remains unknown. This study aimed to explore the association of baseline arterial stiffness and its changes, as determined by brachial-ankle pulse wave velocity (baPWV), with dyslipidemia onset in the general population.MethodsThis study enrolled participants from Beijing Health Management Cohort using measurements of the first visit from 2012 to 2013 as baseline, and followed until the dyslipidemia onset or the end of 2019. Unadjusted and adjusted Cox proportional regression models were used to evaluate the associations of baseline baPWV and baPWV transition (persistent low, onset, remitted and persistent high) with incident dyslipidemia.ResultsOf 4362 individuals (mean age: 55.5 years), 1490 (34.2%) developed dyslipidemia during a median follow-up of 5.9 years. After adjusting for potential confounders, participants with elevated arterial stiffness at baseline had an increased risk of dyslipidemia (HR, 1.194; 95% CI, 1.050-1.358). Compared with persistent low baPWV, new-onset and persistent high baPWV were associated with a 51.2% and 37.1% excess risk of dyslipidemia.ConclusionThe findings indicated that arterial stiffness is an early risk factor of dyslipidemia, suggesting a bidirectional association between arterial stiffness and lipid metabolism.

PurposeThyroid hormones sensitivity is a newly proposed clinical entity closely related with metabolic health. Prior studies have reported the cross-sectional relationship between thyroid hormones sensitivity and diabetes; however, the longitudinal association is unclear to date. We aimed to explore the relationship between impaired thyroid hormone sensitivity at baseline and diabetes onset using a cohort design.MethodsThis study enrolled 7283 euthyroid participants at the first visit between 2008 and 2009, and then annually followed until diabetes onset or 2019. Thyrotropin (TSH), free triiodothyronine (FT3) and free thyroxine (FT4) were measured to calculate thyroid hormone sensitivity by thyroid feedback quantile-based index (TFQI), Chinese-referenced parametric thyroid feedback quantile-based index (PTFQI), thyrotropin index (TSHI), thyrotroph thyroxine resistance index (TT4RI) and FT3/FT4 ratio. Cox proportional hazard model and cross-lagged panel analysis were used.ResultsThe mean baseline age was 44.2 ± 11.9 years, including 4170 (57.3%) male. During a median follow-up of 5.2 years, 359 cases developed diabetes. There was no significant association between thyroid hormones sensitivity indices and diabetes onset, and adjusted hazard ratios per unit (95% CIs) were 0.89 (0.65-1.23) for TFQI, 0.91 (0.57-1.45) for PTFQI, 0.95 (0.70-1.29) for TSHI, 0.98 (0.70-1.01) for TT4RI and 2.12 (0.17-5.78) for FT3/FT4 ratio. Cross-lagged analysis supported the temporal association from fasting glucose to impaired thyroid hormones sensitivity indices.ConclusionsOur findings could not demonstrate that thyroid hormones sensitivity status is a predictor of diabetes onset in the euthyroid population. Elevated fasting glucose (above 7.0 mmol/L) appeared to precede impaired sensitivity indices of thyroid hormones.

BackgroundStroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients.PurposeWe aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke.Materials and methodsA total of 476 patients were enrolled in the study and divided into the training set (n = 381) and testing set (n = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)–binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients–the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation.ResultsIn the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781–0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model.ConclusionThe novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency.SummaryCombining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay.Key ResultsUsing a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800).

ObjectiveTo develop a deep learning-based model using esophageal thickness to detect esophageal cancer from unenhanced chest CT images.MethodsWe retrospectively identified 141 patients with esophageal cancer and 273 patients negative for esophageal cancer (at the time of imaging) for model training. Unenhanced chest CT images were collected and used to build a convolutional neural network (CNN) model for diagnosing esophageal cancer. The CNN is a VB-Net segmentation network that segments the esophagus and automatically quantifies the thickness of the esophageal wall and detect positions of esophageal lesions. To validate this model, 52 false negatives and 48 normal cases were collected further as the second dataset. The average performance of three radiologists and that of the same radiologists aided by the model were compared.ResultsThe sensitivity and specificity of the esophageal cancer detection model were 88.8% and 90.9%, respectively, for the validation dataset set. Of the 52 missed esophageal cancer cases and the 48 normal cases, the sensitivity, specificity, and accuracy of the deep learning esophageal cancer detection model were 69%, 61%, and 65%, respectively. The independent results of the radiologists had a sensitivity of 25%, 31%, and 27%; specificity of 78%, 75%, and 75%; and accuracy of 53%, 54%, and 53%. With the aid of the model, the results of the radiologists were improved to a sensitivity of 77%, 81%, and 75%; specificity of 75%, 74%, and 74%; and accuracy of 76%, 77%, and 75%, respectively.ConclusionsDeep learning-based model can effectively detect esophageal cancer in unenhanced chest CT scans to improve the incidental detection of esophageal cancer.

In order to have more accurate control over the compression molding of automobile coat rack, improve the quality of molding products, and achieve the goal of lightweight design, a novel mechanical model for the main two-layer composite structure of the coat rack is proposed. In this regard, the main factors affecting the mechanical properties of the composite structure are obtained. The hot air convection is selected for the sheet preheating. During the experiment, the hot air temperature, preheating time, molding pressure, and pressing holding time are set to 250°C, 110 s, 13 MPa, and 80 s, respectively. Moreover, the error compensation method is applied to compensate for the shrinkage of the product during solidification and cooling. The LS-DYNA finite element software is used to simulate the molding process of the main body of the coat rack, and the node force information with large deformation is obtained accordingly. The load mapping is used as the boundary condition of mold topology optimization, and the compression molding of the main body of the coat rack is optimized. A lightweight design process and method for the compression molding of automotive interior parts and a mathematical model for the optimization of the solid isotropic material penalty (SIMP) (power law) material interpolation of the concave and convex molds are established. Based on the variable density method, OptiStruct is used for the lightweight design of the convex and concave molds of the main body of the coat rack, which reduces the mold weight by 15.6% and meets the requirements of production quality and lightweight.

In this work, we propose a novel cascaded V-Nets method to segment brain tumor substructures in multimodal brain magnetic resonance imaging. Although V-Net has been successfully used in many segmentation tasks, we demonstrate that its performance could be further enhanced by using a cascaded structure and ensemble strategy. Briefly, our baseline V-Net consists of four levels with encoding and decoding paths and intra- and inter-path skip connections. Focal loss is chosen to improve performance on hard samples as well as balance the positive and negative samples. We further propose three preprocessing pipelines for multimodal magnetic resonance images to train different models. By ensembling the segmentation probability maps obtained from these models, segmentation result is further improved. In other hand, we propose to segment the whole tumor first, and then divide it into tumor necrosis, edema, and enhancing tumor. Experimental results on BraTS 2018 online validation set achieve average Dice scores of 0.9048, 0.8364, and 0.7748 for whole tumor, tumor core and enhancing tumor, respectively. The corresponding values for BraTS 2018 online testing set are 0.8761, 0.7953, and 0.7364, respectively. We also evaluate the proposed method in two additional data sets from local hospitals comprising of 28 and 28 subjects, and the best results are 0.8635, 0.8036, and 0.7217, respectively. We further make a prediction of patient overall survival by ensembling multiple classifiers for long, mid and short groups, and achieve accuracy of 0.519, mean square error of 367240 and Spearman correlation coefficient of 0.168 for BraTS 2018 online testing set.

Background: Depression manifests as a mental disorder characterized by a low mood, suicidal tendencies, disturbances in sleep-wake cycles, psychomotor agitation, and pronounced feelings of hopelessness and anhedonia. Baicalin, a natural flavonoid compound, shows significant promise in alleviating depressive symptoms in animals. This study aims to assess the impact of baicalin on experimental models of depression. Methods: A systematic search of electronic databases was conducted using the search terms “baicalin” AND “depression” OR “depressed” OR “anti-depression”. Preclinical animal models representing experimental depression were included in the analysis. The risk of bias in the included studies was evaluated using the CAMARADES tools. Results: Baicalin significantly increased sucrose preference test (SPT) [SMD= 21.31, 95%CI (16.32, 26.31), P < 0.00001]. mThe tail suspension test (TST) duration significantly decreased in the baicalin group compared to the model group [SMD = −39.3, 95%CI (−49.71, −28.89), P < 0.0001]. Furthermore, baicalin reduced immobility time in rats subjected to the forced swim test (FST) [SMD = −39.73, 95%CI (−48.77, −30.69) P < 0.0001]. Compared to the model group, baicalin treatment also significantly increased the frequency of crossings in the open field test (OFT) [SMD = 32.44, 95%CI (17.74, 47.13), P < 0.00001]. Conclusion: Baicalin significantly improves the manifestations of depressive symptoms. The effect of baicalin against depression is exerted through its anti-inflammatory actions, inhibition of oxidative stress, regulation of the HPA axis, and restoration of neuroplasticity. Future studies will be needed to further explore how these promising preclinical findings can be translated into clinical treatment for depression. [ABSTRACT FROM AUTHOR]