Supply groove effects on characteristics of squeeze film damper

Variation of film pressure was studied by short bearing hypothesis and differential method for grooving on film inner-face. The results were compared with those of FLUENT analysis. Then, effects of the position of groove were studied on film pressure and damping through differential method. The results shown that short bearing hypothesis cannot conform to the actual situation considering grooving. The effect of grooving on the pressure distribution of film was researched by differential method, and the results are consistent with the FLUENT simulation analysis. Side grooving has less effect on film pressure and damping, so it is recommended when the design and assembly conditions are allowed

A gene selection method for GeneChip array data with small sample sizes

<p>Abstract</p> <p>Background</p> <p>In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods.</p> <p>Results</p> <p>We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate.</p> <p>Conclusion</p> <p>Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations.</p

Independent component analysis of Alzheimer's DNA microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated by DNA microarray technology are usually complex, noisy, high-dimensional, and often hindered by low statistical power, their exploitation is difficult. To overcome these problems, two kinds of unsupervised analysis methods for microarray data: principal component analysis (PCA) and independent component analysis (ICA) have been developed to accomplish the task. PCA projects the data into a new space spanned by the principal components that are mutually orthonormal to each other. The constraint of mutual orthogonality and second-order statistics technique within PCA algorithms, however, may not be applied to the biological systems studied. Extracting and characterizing the most informative features of the biological signals, however, require higher-order statistics.</p> <p>Results</p> <p>ICA is one of the unsupervised algorithms that can extract higher-order statistical structures from data and has been applied to DNA microarray gene expression data analysis. We performed FastICA method on DNA microarray gene expression data from Alzheimer's disease (AD) hippocampal tissue samples and consequential gene clustering. Experimental results showed that the ICA method can improve the clustering results of AD samples and identify significant genes. More than 50 significant genes with high expression levels in severe AD were extracted, representing immunity-related protein, metal-related protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, cellular binding protein, and ribosomal protein. Within the aforementioned categories, our method also found 37 significant genes with low expression levels. Moreover, it is worth noting that some oncogenes and phosphorylation-related proteins are expressed in low levels. In comparison to the PCA and support vector machine recursive feature elimination (SVM-RFE) methods, which are widely used in microarray data analysis, ICA can identify more AD-related genes. Furthermore, we have validated and identified many genes that are associated with AD pathogenesis.</p> <p>Conclusion</p> <p>We demonstrated that ICA exploits higher-order statistics to identify gene expression profiles as linear combinations of elementary expression patterns that lead to the construction of potential AD-related pathogenic pathways. Our computing results also validated that the ICA model outperformed PCA and the SVM-RFE method. This report shows that ICA as a microarray data analysis tool can help us to elucidate the molecular taxonomy of AD and other multifactorial and polygenic complex diseases.</p

Prediction and optimization of a desulphurization system using CMAC neural network and genetic algorithm

In this paper, taking desulphurizing ratio and economic cost as two objectives, a ten-input two-output prediction model was structured and validated for desulphurization system. Cerebellar model articulation controller (CMAC) neural network and genetic algorithm (GA) were used for model building and optimization of cost respectively. In the model building process, the grey relation entropy analysis and uniform design method were used to screen the input variables and study the model parameters separately. Traditional regression analysis and proposed location number analysis method were adopted to analyze output errors of experiment group and predict the results of test group. Results show that regression analyses keep high fit degree with experiment group results while the fitting accuracies for test group are quite different. As for location number analysis, a power function between output errors and location numbers was fitted well with the data of experiment group and test group for SO2. Prediction model was initialized by location number analysis method. Model was validated and cost optimization case was performed with GA subsequently. The result shows that the optimal cost obtained from GA could be reduced by more than 30% compared with original optimal operating parameters under same constraints

Long Non Coding RNA MALAT1 Promotes Tumor Growth and Metastasis by Inducing Epithelial-Mesenchymal Transition in Oral Squamous Cell Carcinoma

The prognosis of advanced oral squamous cell carcinoma (OSCC) patients remains dismal, and a better understanding of the underlying mechanisms is critical for identifying effective targets with therapeutic potential to improve the survival of patients with OSCC. This study aims to clarify the clinical and biological significance of metastasis-associated long non-coding RNA, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in OSCC. We found that MALAT1 is overexpressed in OSCC tissues compared to normal oral mucosa by real-time PCR. MALAT1 served as a new prognostic factor in OSCC patients. When knockdown by small interfering RNA (siRNA) in OSCC cell lines TSCCA and Tca8113, MALAT1 was shown to be required for maintaining epithelial-mesenchymal transition (EMT) mediated cell migration and invasion. Western blot and immunofluorescence staining showed that MALAT1 knockdown significantly suppressed N-cadherin and Vimentin expression but induced E-cadherin expression in vitro. Meanwhile, both nucleus and cytoplasm levels of β-catenin and NF-κB were attenuated, while elevated MALAT1 level triggered the expression of β-catenin and NF-κB. More importantly, targeting MALAT1 inhibited TSCCA cell-induced xenograft tumor growth in vivo. Therefore, these findings provide mechanistic insight into the role of MALAT1 in regulating OSCC metastasis, suggesting that MALAT1 is an important prognostic factor and therapeutic target for OSCC

Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data

A subpathway is defined as the local region of a biological pathway with specific biological functions. With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression (defined as ICDS_M, ICDS_CNV, or ICDS_G, respectively). With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool (https://cran.r-project.org/web/packages/ICDS)

Fpr2 exacerbates Streptococcus suis-induced streptococcal toxic shock-like syndrome via attenuation of neutrophil recruitment

The life-threatening disease streptococcal toxic shock-like syndrome (STSLS), caused by the bacterial pathogen Streptococcus suis (S. suis). Proinflammatory markers, bacterial load, granulocyte recruitment, and neutrophil extracellular traps (NETs) levels were monitored in wild-type (WT) and Fpr2-/- mice suffering from STSLS. LXA4 and AnxA1, anti-inflammatory mediators related to Fpr2, were used to identity a potential role of the Fpr2 in STSLS development. We also elucidated the function of Fpr2 at different infection sites by comparing the STSLS model with the S. suis-meningitis model. Compared with the WT mice, Fpr2-/- mice exhibited a reduced inflammatory response and bacterial load, and increased neutrophil recruitment. Pretreatment with AnxA1 or LXA4 impaired leukocyte recruitment and increased both bacterial load and inflammatory reactions in WT but not Fpr2-/- mice experiencing STSLS. These results indicated that Fpr2 impairs neutrophil recruitment during STSLS, and this impairment is enhanced by AnxA1 or LXA4. By comparing the functions of Fpr2 in different S. suis infection models, inflammation and NETs was found to hinder bacterial clearance in S. suis meningitis, and conversely accelerate bacterial clearance in STSLS. Therefore, interference with neutrophil recruitment could potentially be harnessed to develop new treatments for this infectious disease

Age independent survival benefit for patients with small hepatocellular carcinoma undergoing percutaneous cryoablation: A propensity scores matching study

BackgroundHepatocellular carcinoma (HCC) is the major cause of malignancy-related deaths worldwide, and its incidence is likely to increase in the future as life expectancy increases. Therefore, the management of elderly patients with HCC has become a global issue. Aim of this study was to assess whether elderly patients with small HCC could obtain survival benefit from cryoablation (CRYO) in a real-world.Materials and methodsFrom July 2007 to June 2013, 185 patients with small HCC who underwent curative-intent percutaneous CRYO. All patients were divided into three groups according to age distribution. Overall survival (OS) and tumor-free survival (TFS) were compared between among of groups before and after the 1:1 propensity score matching, respectively. Univariate and multivariate Cox analyses were performed to determine the potential relationships between variables and prognostic outcomes.ResultsOne hundred and eighty-five patients (144 men, 41 women) received CRYO for small HCC, including 59 patients with age &lt;50 years, 105 patients with age between 50 and 65 years, and 21 patients with age &gt;65 years. The three age groups showed significant differences in the terms of underlying chronic liver disease and the number of patients with minor postoperative complications. After propensity score matching, the younger and elderly groups showed significant differences in mean OS (P=0.008) and tumor progression (P=0.050). However, no significant differences were shown in mean progression-free survival (PFS) (P=0.303). The Cox multivariate analysis showed that the Child-Pugh grade (HR=3.1, P&lt;0.001), albumin (HR=0.85, P=0.004) and total of bilirubin (HR=1, P=0.024) were the independent prognostic factor for mean OS.ConclusionOur propensity-score-matched study suggested that elderly patients with small HCC can achieve acceptable prognostic outcomes with PFS similar to those of younger patients with small HCC after treatment with CRYO, while Child-Pugh grade, bilirubin and serum albumin levels were associated with the prognosis of small HCCs