Conditional Enhanced Variational Autoencoder-Heterogeneous Graph Attention Neural Network: A Novel Fault Diagnosis Method for Electric Rudders Based on Heterogeneous Information.

In machine fault diagnosis, despite the wealth of information multi-sensor data provide for constructing high-quality graphs, existing graph data-driven diagnostic methods face challenges posed by handling these heterogeneous multi-sensor data. To address this issue, we propose CEVAE-HGANN, an innovative model for fault diagnosis based on the electric rudder, which can process heterogeneous data efficiently. Initially, we facilitate interaction between conditional information and the original features, followed by dimensional reduction via a conditional enhanced variational autoencoder, thereby achieving a more robust state representation. Subsequently, we define two meta-paths and employ both the Euclidean distance and Pearson coefficient in crafting an effective adjacency matrix to delineate the relationships among edges within the graph, thereby effectively representing the complex interrelations among these subsystems. Ultimately, we incorporate heterogeneous graph attention neural networks for classification, which emphasizes the connections among different subsystems, moving beyond the reliance on node-level fault identification and effectively capturing the complex interactions between subsystems. The experimental outcomes substantiate the superiority of the electric rudder-based CEVAE-HGANN model fault diagnosis.

FOC winding defect detection based on improved texture features and low-rank representation model.

The defect detection of fiber-optic coils (FOCs) plays an important role in the quality control of the FOC production. In order to overcome the problems of poor performance and low reliability of existing methods, this paper provides a solution for winding defect detection of FOCs based on low-rank representation (LRR) technology. First, we design a feature matrix, which represents the image. Then the LRR model is employed to formulate the defect detection task as a problem of low rank and sparse matrix decomposition. Meanwhile, Laplacian regularization is introduced as a smoothness constraint to expand the distance between defect regions and low-rank background. Experiments are performed on a real dataset to verify the algorithm. The results show that the proposed winding defect detection method of FOCs achieves the highest detection accuracy and lowest false alarm rate compared to other methods, verifying the effectiveness of the proposed method.

Rapid identification of pathogens associated with ventilator-associated pneumonia by Nanopore sequencing.

BACKGROUND: Aetiology detection is crucial in the diagnosis and treatment of ventilator-associated pneumonia (VAP). However, the detection method needs improvement. In this study, we used Nanopore sequencing to build a quick detection protocol and compared the efficiency of different methods for detecting 7 VAP pathogens. METHODS: The endotracheal aspirate (ETA) of 83 patients with suspected VAP from Peking University Third Hospital (PUTH) was collected, saponins were used to deplete host genomes, and PCR- or non-PCR-amplified library construction methods were used and compared. Sequence was performed with MinION equipment and local data analysis methods were used for sequencing and data analysis. RESULTS: Saponin depletion effectively removed 11 of 12 human genomes, while most pathogenic bacterial genome results showed no significant difference except for S. pneumoniae. Moreover, the average sequence time decreased from 19.6 h to 3.62 h. The non-PCR amplification method and PCR amplification method for library build has a similar average sensitivity (85.8% vs. 86.35%), but the non-PCR amplification method has a better average specificity (100% VS 91.15%), and required less time. The whole method takes 5-6 h from ETA extraction to pathogen classification. After analysing the 7 pathogens enrolled in our study, the average sensitivity of metagenomic sequencing was approximately 2.4 times higher than that of clinical culture (89.15% vs. 37.77%), and the average specificity was 98.8%. CONCLUSIONS: Using saponins to remove the human genome and a non-PCR amplification method to build libraries can be used for the identification of pathogens in the ETA of VAP patients within 6 h by MinION, which provides a new approach for the rapid identification of pathogens in clinical departments.

Automated defect detection and classification for fiber-optic coil based on wavelet transform and self-adaptive GA-SVM.

The quality monitoring of fiber-optic coil (FOC) in winding systems is usually done manually. Aiming at the problem of inefficient and low accuracy of manual detection, this article is dedicated to researching a defect detection framework based on machine vision, which provides a reliable method for automatic defect detection of FOC. For this purpose, a defect detection scheme that integrates wavelet transform and nonlocal means filtering is proposed to accurately locate the defect region. Then, based on the features constructed by wavelet coefficients, a support vector machine (SVM) is used as the classifier. Additionally, a self-adaptive genetic algorithm is proposed to optimize the parameters of the SVM to form the final classifier. Through experiments on the data set obtained by our designed imaging system, the results show that our method has good defect detection performance and high classification accuracy, which provides an optimal solution for the automatic detection of FOC.

A case of treatment for pulmonary infection caused by multidrug-resistant Acinetobacter baumannii.

Acinetobacter baumannii is a major pathogen in hospital-acquired infections notorious for its strong acquired resistance and complex drug resistance mechanisms. Owing to the lack of effective drugs, the mortality rate of extensively drug-resistant A. baumannii pneumonia can reach as high as 65%. This article analyzes a case where a combination of cefoperazone-sulbactam, polymyxin B, and minocycline with rifampicin successfully treated XDR-AB pulmonary infection. Combination therapy is effective and has a particular clinical value.

A challenging case of drug-related acute fibrinous and organizing pneumonia: A rare case report.

A 65-year-old man presented with intermittent fever and progressive shortness of breath. He responded poorly to antibiotics and corticosteroids (methylprednisolone 40 mg/d). Chest computed tomography scans showed diffuse consolidations and ground glass density patchy opacities in both lungs and these lesions progressed rapidly. The diagnosis of acute fibrinous and organizing pneumonia (AFOP) was confirmed through transbronchial cryobiopsy. This patient had prostate cancer with bone metastasis for 4 months and took the anti-prostate cancer medications including apalutamide and leuprorelin acetate. Considering his medication history, the patient was diagnosed with AFOP induced by anti-prostate cancer medications through panel discussion of multidisciplinary teams. Intravenous methylprednisolone of 500 mg/day was administered for 3 days and then slowly tapered. The patient's shortness of breath gradually subsided. In addition, the lesions in the lungs improved significantly on follow up imaging. AFOP induced by anti-prostate cancer medications is rare. To our knowledge, this is the first reported case and high-dose glucocorticoid treatment may be required in some of these cases.

Synthesis of Tetrasubstituted Alkenes by Rhodium-Catalyzed Regioselective Cyano Transfer.

We describe herein a novel, general, and robust approach to structurally diversified alkenyl nitriles through a Rh-catalyzed cyano transfer reaction between alkynyl-malononitrile derivatives and aryl/alkenyl boronic acids. This reaction exhibits high chemo- and regioselectivity and a broad substrate scope. The tetrasubstituted alkenyl dinitriles (34 examples, average 58% yield) are obtained through substrate tuning and ligand control.

Correlation of Clinical Symptoms and Sputum Inflammatory Markers with Air Pollutants in Stable COPD Patients in Beijing Area.

Short-term exposure to major air pollutants can increase the risk of acute exacerbation in chronic obstructive pulmonary disease (COPD) patients. However, evidence on the mechanism of acute exacerbation of COPD (AECOPD) caused by air pollutants is still limited. A total of 78 patients with stable COPD and 58 healthy controls were recruited in Peking University Third Hospital in China from December 2014 to January 2015. The correlation and lag effect over 7 days (lag1-7) of 6 air pollutants with clinical symptoms and inflammatory markers in induced sputum were analyzed. PM2.5, NO2 and CO were positively correlated with the COPD assessment test (CAT) score at lag 5, PM10 was positively correlated with the CAT score at lag 3, MMP-9 and IL-8 were positively correlated with PM2.5, PM10 and NO2 at lag 2, and CO was positively correlated with each other marker at lag 4. Short-term exposure to PM2.5, PM10, NO2, and CO can cause a neutrophil-mediated airway inflammatory response, followed by increased clinical symptoms. If the PM2.5, PM10, NO2 and CO exposure levels increase during air pollution monitoring, the early usage of medication or reduction of exposure to pollutants can effectively reduce the clinical symptoms of patients.

Performance and comparison of assessment models to predict 30-day mortality in patients with hospital-acquired pneumonia.

BACKGROUND: Hospital-acquired pneumonia (HAP) is the most common hospital-acquired infection in China with substantial morbidity and mortality. But no specific risk assessment model has been well validated in patients with HAP. The aim of this study was to investigate the published risk assessment models that could potentially be used to predict 30-day mortality in HAP patients in non-surgical departments. METHODS: This study was a single-center, retrospective study. In total, 223 patients diagnosed with HAP from 2012 to 2017 were included in this study. Clinical and laboratory data during the initial 24 hours after HAP diagnosis were collected to calculate the pneumonia severity index (PSI); consciousness, urea nitrogen, respiratory rate, blood pressure, and age ≥65 years (CURB-65); Acute Physiology and Chronic Health Evaluation II (APACHE II); Sequential Organ Failure Assessment (SOFA); and Quick Sequential Organ Failure Assessment (qSOFA) scores. The discriminatory power was tested by constructing receiver operating characteristic (ROC) curves, and the areas under the curve (AUCs) were calculated. RESULTS: The all-cause 30-day mortality rate was 18.4% (41/223). The PSI, CURB-65, SOFA, APACHE II, and qSOFA scores were significantly higher in non-survivors than in survivors (all P < 0.001). The discriminatory abilities of the APACHE II and SOFA scores were better than those of the CURB-65 and qSOFA scores (ROC AUC: APACHE II vs. CURB-65, 0.863 vs. 0.744, Z = 3.055, P = 0.002; APACHE II vs. qSOFA, 0.863 vs. 0.767, Z = 3.017, P = 0.003; SOFA vs. CURB-65, 0.856 vs. 0.744, Z = 2.589, P = 0.010; SOFA vs. qSOFA, 0.856 vs. 0.767, Z = 2.170, P = 0.030). The cut-off values we defined for the SOFA, APACHE II, and qSOFA scores were 4, 14, and 1. CONCLUSIONS: These results suggest that the APACHE II and SOFA scores determined during the initial 24 h after HAP diagnosis may be useful for the prediction of 30-day mortality in HAP patients in non-surgical departments. The qSOFA score may be a simple tool that can be used to quickly identify severe infections.

Disruption of histidine and energy homeostasis in chronic obstructive pulmonary disease.

Background: Chronic obstructive pulmonary disease (COPD) is a systemic condition that is too complex to be assessed by lung function alone. Metabolomics has the potential to help understand the mechanistic underpinnings that contribute to COPD pathogenesis. Since blood metabolomics may be affected by sex and body mass index (BMI), the aim of this study was to determine the metabolomic variability in male smokers with and without COPD who have a narrow BMI range. Methods: We compared the quantitative proton nuclear magnetic resonance acquired serum metabolomics of a male Chinese Han population of non-smokers without COPD, and smokers with and without COPD. We also assessed the impact of smoking status on metabolite concentrations and the associations between metabolite concentrations and inflammatory markers such as serum interleukin-6 and histamine, and blood cell differential (%). Metabolomics data were log-transformed and auto-scaled for parametric statistical analysis. Mean normalized metabolite concentration values and continuous demographic variables were compared by Student's t-test with Welch correction or ANOVA with post-hoc Tukey's test, as applicable; t-test p-values for metabolomics data were corrected for false discovery rate (FDR). A Pearson association matrix was built to evaluate the relationship between metabolite concentrations, clinical parameters and markers of inflammation. Results: Twenty-eight metabolites were identified and quantified. Creatine, glycine, histidine, and threonine concentrations were reduced in COPD patients compared to non-COPD smokers (FDR ≤15%). Concentrations of these metabolites were inversely correlated with interleukin-6 levels. COPD patients had overall dampening of metabolite concentrations including energy-related metabolic pathways such as creatine metabolism. They also had higher histamine levels and percent basophils compared to smokers without COPD. Conclusion: COPD is associated with alterations in the serum metabolome, including a disruption in the histidine-histamine and creatine metabolic pathways. These findings support the use of metabolomics to understand the pathogenic mechanisms involved in COPD.Trial registration www.clinicaltrials.gov, NCT03310177.

Symptom-related sputum microbiota in stable chronic obstructive pulmonary disease.

Background: The role of airway microbiota in COPD is highly debated. Symptomology assessment is vital for the management of clinically stable COPD patients; however, the link between symp toms and the airway microbiome is currently unknown. Purpose: The present study aimed to evaluate the relationship among stable COPD patients. Patients and methods: We conducted pyrosequencing of bacterial 16S rRNA using induced sputum samples in a Han Chinese cohort that included 40 clinically stable COPD patients and 19 healthy controls. Results: Alterations in community composition and core bacte rial taxa (Neisseria subflava, etc.) were observed in patients with severe symptoms compared with controls. The co-occurrence network indicated that the key microbiota enriched in COPD patients showed higher expression in patients with severe symptoms. The association pattern of symptoms with the sputum microbiome was obviously different from that of lung function in COPD patients. Conclusion: These findings broaden our insights into the relationship between the sputum microbiota and the symptom severity in COPD patients, emphasizing the role of symptoms in the airway microbiome, independent of lung function.