Rotary Compressor Noise Analysis Using Mechanisms and Electromagnetics Coupled Approach

This research is conducted to investigate noise source and design low noise compressors. For improving energy efficiency, the rotary compressor with variable speed brushless DC motor is increasingly adopted for appliances. However brushless DC motor makes more compressor vibration than constant speed motor compressor at high speed operating condition. Therefore it is necessary to reduce noise and vibration for improving air conditioner quality. In this study, compressor’s noise and vibration are simulated using structural and electromagnetics coupled methods. To simulate the actual motor movements, precession motion of rotor is applied for simulatio

GScluster: Network-weighted gene-set clustering analysis

Background: Gene-set analysis (GSA) has been commonly used to identify significantly altered pathways or functions from omics data. However, GSA often yields a long list of gene-sets, necessitating efficient post-processing for improved interpretation. Existing methods cluster the gene-sets based on the extent of their overlap to summarize the GSA results without considering interactions between gene-sets. Results: Here, we presented a novel network-weighted gene-set clustering that incorporates both the gene-set overlap and protein-protein interaction (PPI) networks. Three examples were demonstrated for microarray gene expression, GWAS summary, and RNA-sequencing data to which different GSA methods were applied. These examples as well as a global analysis show that the proposed method increases PPI densities and functional relevance of the resulting clusters. Additionally, distinct properties of gene-set distance measures were compared. The methods are implemented as an R/Shiny package GScluster that provides gene-set clustering and diverse functions for visualization of gene-sets and PPI networks. Conclusions: Network-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis

Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets

We present a novel approach to identify human microRNA (miRNA) regulatory modules (mRNA targets and relevant cell conditions) by biclustering a large collection of mRNA fold-change data for sequence-specific targets. Bicluster targets were assessed using validated messenger RNA (mRNA) targets and exhibited on an average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.4%) by incorporating functional networks of targets. We analyzed cancer-specific biclusters and found that the PI3K/Akt signaling pathway is strongly enriched with targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Indeed, five independent prognostic miRNAs were identified, and repression of bicluster targets and pathway activity by miR-29 was experimentally validated. In total, 29 898 biclusters for 459 human miRNAs were collected in the BiMIR database where biclusters are searchable for miRNAs, tissues, diseases, keywords and target genes

Synchrotron X-ray reflectivity studies of nanoporous organosilicate thin films with low dielectric constants

Quantitative, non-destructive X-ray reflectivity analysis using synchrotron radiation sources was successfully performed on nanoporous dielectric thin films prepared by thermal processing of blend films of a thermally curable polymethylsilsesquioxane dielectric precursor and a thermally labile triethoxy-silyl-terminated six-arm poly(epsilon-caprolactone) porogen in various compositions. In addition, thermogravimetric analysis and transmission electron microscopy analysis were carried out. These measurements provided important structural information about the nanoporous films. The thermal process used in this study was found to cause the porogen molecules to undergo efficiently sacrificial thermal degradation, generating closed, spherical nanopores in the dielectric film. The resultant nanoporous films exhibited a homogeneous, well defined structure with a thin skin layer and low surface roughness. In particular, no skin layer was formed in the porous film imprinted using a porogen loading of 30 wt%. The film porosities ranged from 0 to 33.8% over the porogen loading range of 0-30 wt%open131

Detection of an intermediate during the unfolding process of the dimeric ketosteroid isomerase

AbstractFailure to detect the intermediate in spite of its existence often leads to the conclusion that two-state transition in the unfolding process of the protein can be justified. In contrast to the previous equilibrium unfolding experiment fitted to a two-state model by circular dichroism and fluorescence spectroscopies, an equilibrium unfolding intermediate of a dimeric ketosteroid isomerase (KSI) could be detected by small angle X-ray scattering (SAXS) and analytical ultracentrifugation. The sizes of KSI were determined to be 18.7Å in 0M urea, 17.3Å in 5.2M urea, and 25.1Å in 7M urea by SAXS. The size of KSI in 5.2M urea was significantly decreased compared with those in 0M and 7M urea, suggesting the existence of a compact intermediate. Sedimentation velocity as obtained by ultracentrifugation confirmed that KSI in 5.2M urea is distinctly different from native and fully-unfolded forms. The sizes measured by pulse field gradient nuclear magnetic resonance (NMR) spectroscopy were consistent with those obtained by SAXS. Discrepancy of equilibrium unfolding studies between size measurement methods and optical spectroscopies might be due to the failure in detecting the intermediate by optical spectroscopic methods. Further characterization of the intermediate using 1H NMR spectroscopy and Kratky plot supported the existence of a partially-folded form of KSI which is distinct from those of native and fully-unfolded KSIs. Taken together, our results suggest that the formation of a compact intermediate should precede the association of monomers prior to the dimerization process during the folding of KSI

Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2

Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2

netGO: R-Shiny package for network-integrated pathway enrichment analysis

A Summary: We present an R-Shiny package, netGO, for novel network-integrated pathway enrichment analysis. The conventional Fisher's exact test (FET) considers the extent of overlap between target genes and pathway gene-sets, while recent network-based analysis tools consider only network interactions between the two. netGO implements an intuitive framework to integrate both the overlap and networks into a single score, and adaptively resamples genes based on network degrees to assess the pathway enrichment. In benchmark tests for gene expression and genome-wide association study (GWAS) data, netGO captured the relevant gene-sets better than existing tools, especially when analyzing a small number of genes. Specifically, netGO provides user-interactive visualization of the target genes, enriched gene-set and their network interactions for both netGO and FET results for further analysis. For this visualization, we also developed a standalone R-Shiny package shinyCyJS to connect R-shiny and the JavaScript version of cytoscape

Few-shot unlearning

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