The roller bearing fault diagnosis methods with harmonic wavelet packet and multi-classification relevance vector machine

Roller bearings are widely used elements in rotary machines. How to monitor the working conditions of roller bearings are focus study in the world. Monitoring the vibration signals of roller bearings is important indirect mean for that they reveal the characteristics and feature of roller bearing faults. Therefore, monitor the vibration signals and diagnose the working states of roller bearings are widely used to ensure the safety operation of the machines. This paper studies a novel roller bearing faults discrimination method with harmonic wavelet packet and Multi-classification Relevance Vector Machine (MRVM). Indeed, the fault discrimination is a pattern recognition process including feature extraction and faulty patterns recognition. Therefore, this paper collects vibration signals and decomposes them with harmonic wavelet packet. After the wavelet coefficients of each node are available, compute the vector energy by corresponding coefficients. The feature vector is prepared after the vector energy has been standardized. With MRVM, the paper proposes three fault discrimination methods in order to identify good bearing, bearing with inner race fault, bearing with outer race fault and bearing with roller fault. The Decision Tree (DT) model, One Against Rest (OAR) model and One Against One (OAO) model are used to propose the classification methods respectively. The proposed OAO model is simplified in order to improve the computation efficiency and simplify the architecture of the model. Finally, capture the vibration signal from the roller bearing stand of electric engineering lab and the roller bearing fault simulation stand QPZZ-II to illustrate the proposed methods. The proposed feature extraction method with harmonic wavelet packet is compared with conventional wavelet packet. With the previous feature vectors, the accuracy and efficiency of the three fault discrimination methods are compared. The accuracy and efficiency of three fault discrimination methods are compared under different conditions including developing faults, noise involving and several faults developing simultaneously. Experiment results show that the proposed feature extraction method is more effective than conventional method and the simplified OAO-RVM model possess the best fault discrimination accuracy and DT-RVM model possesses the better computation efficiency

Spectrum-effect relationship between HPLC fingerprints and inhibitory activity in MUC5AC mucin of Pinelliae Rhizoma Praeparatum Cum Alumine

Purpose: To investigate the spectrum-effect relationship between HPLC fingerprints and the inhibitory effect on MUC5AC mucin of Pinelliae Rhizoma Praeparatum Cum Alumine (PRPCA).Methods: The fingerprints of 20 PRPCA batches were established using HPLC and their similarities or differences were analyzed using hierarchical cluster analysis (HCA) and principal component analysis (PCA). The inhibitory effects of MUC5AC mucin were evaluated in LPS-treated NCI-H292 cells. The spectrum-effect relationship between common chromatographic peaks and MUC5AC inhibition was established using a partial least squares-discriminant analysis (PLS-DA).Results: Fifteen common chromatographic peaks were identified by analyzing HPLC fingerprints, with uridine, tyrosine, uracil, and inosine found as possible markers to distinguish the PRPCA from different sources. Spectrum-effect relationship analysis showed that the chromatographic peaks 5, 6, 10 (vernine), 12 (5-hydroxymethylfurfural), 14 (tryptophan) and 15 (adenosine) were closely associated with the inhibitory effect on MUC5AC mucin.Conclusion: The spectrum-effect relationship between HPLC fingerprints and the inhibitory effect on MUC5AC mucin of PRPCA was successfully established in the present study. Our findings further reveal the material basis of PRPCA and provide an effective method for its quality control

Deep functional factor models: forecasting high-dimensional functional time series via Bayesian nonparametric factorization

This paper introduces the Deep Functional Factor Model (DF2M), a Bayesian nonparametric model designed for analysis of high-dimensional functional time series. DF2M is built upon the Indian Buffet Process and the multi-task Gaussian Process, incorporating a deep kernel function that captures non-Markovian and nonlinear temporal dynamics. Unlike many black-box deep learning models, DF2M offers an explainable approach to utilizing neural networks by constructing a factor model and integrating deep neural networks within the kernel function. Additionally, we develop a computationally efficient variational inference algorithm to infer DF2M. Empirical results from four real-world datasets demonstrate that DF2M provides better explainability and superior predictive accuracy compared to conventional deep learning models for high-dimensional functional time series

Prevalence and risk factors of abnormal left ventricular geometrical patterns in untreated hypertensive patients

BACKGROUND: The various prevalence of LVH and abnormal LV geometry have been reported in different populations. So far, only a few reports are available on the prevalence of LV geometric patterns in a large Chinese untreated hypertensive population. METHODS: A total of 9,286 subjects (5167 men and 4119 women) completed the survey and 1641 untreated hypertensive patients (1044 males and 597 females) enrolled in the present study. The LV geometry was classified into four patterns: normal; abnormal,defined as concentric remodeling;concentric or eccentric hypertrophy based on the values of left ventricular mass index (LVMI) and relative wall thickness (RWT). Logistic regression model was applied to determine the odds ratio (OR) and 95% confidence intervals (CI) of the risk factors of left ventricular hypertrophy. RESULTS: The prevalence of LVH was 20.2% in untreated hypertensive patients, much higher in women (30.8%) than in men (14.2%) (P < 0.01). The prevalence of LV geometrical patterns was 34.9%, 11.1%, 9.1% for concentric remodeling, concentric and eccentric hypertrophy,respectively. After adjustment by using Logistic regression model, the risk factors for LVH and abnormal LV geometry were age, female, systolic blood pressure, and body mass index. And low high density lipoprotein maybe a positive factor. CONCLUSIONS: The prevalence of LVH and abnormal LV geometric patterns was higher in women than in men and increased with age. It is crucial to improve the awareness rate of hypertension and control the risk factors of CV complications in untreated hypertensive population

Mitochondrial Somatic Mutations and the Lack of Viral Genomic Variation in Recurrent Respiratory Papillomatosis

Recurrent Respiratory Papillomatosis (RRP) is a rare disease of the aerodigestive tract caused by the Human Papilloma Virus (HPV) that manifests as profoundly altered phonatory and upper respiratory anatomy. Current therapies are primarily symptomatic; enhanced insight regarding disease-specific biology of RRP is critical to improved therapeutics for this challenging population. Multiplex PCR was performed on oral rinses collected from twenty-three patients with adult-onset RRP every three months for one year. Twenty-two (95.6%) subjects had an initial HPV positive oral rinse. Of those subjects, 77.2% had an additional positive oral rinse over 12 months. A subset of rinses were then compared to tissue samples in the same patient employing HPViewer to determine HPV subtype concordance. Multiple HPV copies (60-787 per human cell) were detected in RRP tissue in each patient, but a single dominant HPV was found in individual samples. These data confirm persistent oral HPV infection in the majority of patients with RRP. In addition, three novel HPV6 isolates were found and identical HPV strains, at very low levels, were identified in oral rinses in two patients suggesting potential HPV subtype concordance. Finally, somatic heteroplasmic mtDNA mutations were observed in RRP tissue with 1.8 mutations per sample and two nonsynonymous variants. These data provide foundational insight into both the underlying pathophysiology of RRP, but also potential targets for intervention in this challenging patient cohort

Foregut microbiome in development of esophageal adenocarcinoma

Esophageal adenocarcinoma (EA), the type of cancer linked to heartburn due to gastroesophageal reflux diseases (GERD), has increased six fold in the past 30 years. This cannot currently be explained by the usual environmental or by host genetic factors. EA is the end result of a sequence of GERD-related diseases, preceded by reflux esophagitis (RE) and Barrett&#x2019;s esophagus (BE). Preliminary studies by Pei and colleagues at NYU on elderly male veterans identified two types of microbiotas in the esophagus. Patients who carry the type II microbiota are &#x3e;15 fold likely to have esophagitis and BE than those harboring the type I microbiota. In a small scale study, we also found that 3 of 3 cases of EA harbored the type II biota. The findings have opened a new approach to understanding the recent surge in the incidence of EA. &#xd;&#xa;&#xd;&#xa;Our long-term goal is to identify the cause of GERD sequence. The hypothesis to be tested is that changes in the foregut microbiome are associated with EA and its precursors, RE and BE in GERD sequence. We will conduct a case control study to demonstrate the microbiome disease association in every stage of GERD sequence, as well as analyze the trend in changes in the microbiome along disease progression toward EA, by two specific aims. Aim 1 is to conduct a comprehensive population survey of the foregut microbiome and demonstrate its association with GERD sequence. Furthermore, spatial relationship between the esophageal microbiota and upstream (mouth) and downstream (stomach) foregut microbiotas as well as temporal stability of the microbiome-disease association will also be examined. Aim 2 is to define the distal esophageal metagenome and demonstrate its association with GERD sequence. Detailed analyses will include pathway-disease and gene-disease associations. Archaea, fungi and viruses, if identified, also will be correlated with the diseases. A significant association between the foregut microbiome and GERD sequence, if demonstrated, will be the first step for eventually testing whether an abnormal microbiome is required for the development of the sequence of phenotypic changes toward EA. If EA and its precursors represent a microecological disease, treating the cause of GERD might become possible, for example, by normalizing the microbiota through use of antibiotics, probiotics, or prebiotics. Causative therapy of GERD could prevent its progression and reverse the current trend of increasing incidence of EA

Oral Microbiome Profiles: 16S rRNA Pyrosequencing and Microarray Assay Comparison

The human oral microbiome is potentially related to diverse health conditions and high-throughput technology provides the possibility of surveying microbial community structure at high resolution. We compared two oral microbiome survey methods: broad-based microbiome identification by 16S rRNA gene sequencing and targeted characterization of microbes by custom DNA microarray.Oral wash samples were collected from 20 individuals at Memorial Sloan-Kettering Cancer Center. 16S rRNA gene survey was performed by 454 pyrosequencing of the V3–V5 region (450 bp). Targeted identification by DNA microarray was carried out with the Human Oral Microbe Identification Microarray (HOMIM). Correlations and relative abundance were compared at phylum and genus level, between 16S rRNA sequence read ratio and HOMIM hybridization intensity.; Correlation = 0.70–0.84).Microbiome community profiles assessed by 16S rRNA pyrosequencing and HOMIM were highly correlated at the phylum level and, when comparing the more commonly detected taxa, also at the genus level. Both methods are currently suitable for high-throughput epidemiologic investigations relating identified and more common oral microbial taxa to disease risk; yet, pyrosequencing may provide a broader spectrum of taxa identification, a distinct sequence-read record, and greater detection sensitivity