Analysis of effective thermal conductivity of fibrous materials

The objective of this research is to gain a better understanding of the various mechanisms of heat transfer through fibrous materials and to gain insight into how fill-gas pressure influences the effective thermal conductivity. By way of first principles and some empiricism, two mathematical models are constructed to correlate experimental data. The data are obtained from a test series measuring the effective thermal conductivity of Nomex using a two-sided guarded hot-plate heater apparatus. Tests are conducted for certain mean temperatures and fill-gases over a range of pressures varying from vacuum to atmospheric conditions. The models are then evaluated to determine their effectiveness in representing the effective thermal conductivity of a fibrous material. The models presented herein predict the effective thermal conductivity of Nomex extremely well. Since the influence of gas conduction is determined to be the most influential component in predicting the effective thermal conductivity of a fibrous material, an improved representation of gas conduction is developed. Finally, some recommendations for extension to other random-oriented fiber materials are made concerning the usefulness of each model depending on their advantages and disadvantages

The Unfolded Protein Response and its potential role in Huntington's disease

Huntington's disease (HD) is a progressive, neurodegenerative disease with fatal outcome. Although the disease-causing gene (huntingtin) has been known for some time, the exact cause of neuronal cell death is still unknown. One potential mechanism contributing to the massive loss of neurons in the brain of HD patients might be the unfolded protein response (UPR), which is activated by accumulation of misfolded proteins in the endoplasmatic reticulum (ER). As an adaptive response to counter-balance accumulation of un- or misfolded proteins, the UPR upregulates transcription of chaperones, temporarily attenuates new translation, and activates protein degradation via the proteasome. However, it is known that persistent ER stress and activated UPR can cause cell death by triggering of apoptosis. Nevertheless, the evidence linking UPR with HD progression remains inconclusive. Here, we present first analyses of UPR activation during HD based on available expression data. To elucidate the potential role of UPR as a disease-relevant process, we examine its connection to cell death and inflammatory processes. Due to the complexity of these molecular mechanisms, a systems biology approach was pursued

Are we overestimating the number of cell-cycling genes? The impact of background models for time series data.

Periodic processes play fundamental roles in organisms. Prominent examples are the cell cycle and the circadian clock. Microarray array technology has enabled us to screen complete sets of transcripts for possible association with such fundamental periodic processes on a system-wide level. Frequently, quite a large number of genes has been detected as periodically expressed. However, the small overlap of identified genes between different studies has shaded considerable doubts about the reliability of the detected periodic expression. In this study, we show that a major reason for the lacking agreement is the use of an inadequate background model for the determination of significance. We demonstrate that the choice of background model has considerable impact on the statistical significance of periodic expression. For illustration, we reanalyzed two microarray studies of the yeast cell cycle. Our evaluation strongly indicates that the results of previous analyses might have been overoptimistic and that the use of more suitable background model promises to give more realistic resultsinfo:eu-repo/semantics/publishedVersio

A Systems Biology Approach towards Deciphering the Unfolded Protein Response in Huntington's Disease

Although the disease causing gene huntingtin has been known for some time, the exact cause of neuronal cell death during _Huntington's disease_ (HD) remains unknown. One potential mechanism contributing to the massive loss of neurons in HD brains might be the _Unfolded Protein Response_ (UPR) which is activated by accumulation of misfolded proteins in the endoplasmic reticulum (ER). As an adaptive response, UPR upregulates transcription of chaperones, temporarily attenuating new translation and activates protein degradation via the proteasome. However, at high levels of ER stress, UPR signalling can contribute to neuronal apoptosis.

Our primary aims include (a) construction of the UPR signalling network, (b) curation and bioinformatical identification of UPR target genes and finally (c) examination of HD gene expression data sets for UPR transcriptional signatures and differential regulation of UPR pathways.

The UPR signalling pathway is reconstructed based on literature review and using the "Unified Interactome database":http://www.unihi.org. Lists of UPR target genes detected by previous experiments or as predicted by computational analysis are compiled. This allows us to perform enrichment analysis for differential HD gene expression and to assess whether UPR expression signatures are prominent during HD pathogenesis.

Results: The canonical UPR pathway is complemented with additional protein interaction data allowing us to assess its embedding into the cellular context and to identify potential modifiers as well as novel drug targets.

Conclusions: The in depth systems biology analysis can give us valuable insights about the involvement of the UPR in HD.
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Model selection and efficiency testing for normalization of cDNA microarray data

In this study we present two novel normalization schemes for cDNA microarrays. They are based on iterative local regression and optimization of model parameters by generalized cross-validation. Permutation tests assessing the efficiency of normalization demonstrated that the proposed schemes have an improved ability to remove systematic errors and to reduce variability in microarray data. The analysis also reveals that without parameter optimization local regression is frequently insufficient to remove systematic errors in microarray data

Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria.

Cyanobacteria are essential primary producers in marine ecosystems, playing an important role in both carbon and nitrogen cycles. In the last decade, various genome sequencing and metagenomic projects have generated large amounts of genetic data for cyanobacteria. This wealth of data provides researchers with a new basis for the study of molecular adaptation, ecology and evolution of cyanobacteria, as well as for developing biotechnological applications. It also facilitates the use of multiplex techniques, i.e., expression profiling by high-throughput technologies such as microarrays, RNA-seq, and proteomics. However, exploration and analysis of these data is challenging, and often requires advanced computational methods. Also, they need to be integrated into our existing framework of knowledge to use them to draw reliable biological conclusions. Here, systems biology provides important tools. Especially, the construction and analysis of molecular networks has emerged as a powerful systems-level framework, with which to integrate such data, and to better understand biological relevant processes in these organisms. In this review, we provide an overview of the advances and experimental approaches undertaken using multiplex data from genomic, transcriptomic, proteomic, and metabolomic studies in cyanobacteria. Furthermore, we summarize currently available web-based tools dedicated to cyanobacteria, i.e., CyanoBase, CyanoEXpress, ProPortal, Cyanorak, CyanoBIKE, and CINPER. Finally, we present a case study for the freshwater model cyanobacteria, Synechocystis sp. PCC6803, to show the power of meta-analysis, and the potential to extrapolate acquired knowledge to the ecologically important marine cyanobacteria genus, Prochlorococcus

Comparison and consolidation of microarray data sets of human tissue expression

<p>Abstract</p> <p>Background</p> <p>Human tissue displays a remarkable diversity in structure and function. To understand how such diversity emerges from the same DNA, systematic measurements of gene expression across different tissues in the human body are essential. Several recent studies addressed this formidable task using microarray technologies. These large tissue expression data sets have provided us an important basis for biomedical research. However, it is well known that microarray data can be compromised by high noise level and various experimental artefacts. Critical comparison of different data sets can help to reveal such errors and to avoid pitfalls in their application.</p> <p>Results</p> <p>We present here the first comparison and integration of four freely available tissue expression data sets generated using three different microarray platforms and containing a total of 377 microarray hybridizations. When assessing the tissue expression of genes, we found that the results considerably depend on the chosen data set. Nevertheless, the comparison also revealed statistically significant similarity of gene expression profiles across different platforms. This enabled us to construct consolidated lists of platform-independent tissue-specific genes using a set of complementary measures. Follow-up analyses showed that results based on consolidated data tend to be more reliable.</p> <p>Conclusions</p> <p>Our study strongly indicates that the consolidation of the four different tissue expression data sets can increase data quality and can lead to biologically more meaningful results. The provided compendium of platform-independent gene lists should facilitate the identification of novel tissue-specific marker genes.</p

CyanoEXpress: A web database for exploration and visualisation of the integrated transcriptome of cyanobacterium Synechocystis sp. PCC6803.

UNLABELLED: Synechocystis sp. PCC6803 is one of the best studied cyanobacteria and an important model organism for our understanding of photosynthesis. The early availability of its complete genome sequence initiated numerous transcriptome studies, which have generated a wealth of expression data. Analysis of the accumulated data can be a powerful tool to study transcription in a comprehensive manner and to reveal underlying regulatory mechanisms, as well as to annotate genes whose functions are yet unknown. However, use of divergent microarray platforms, as well as distributed data storage make meta-analyses of Synechocystis expression data highly challenging, especially for researchers with limited bioinformatic expertise and resources. To facilitate utilisation of the accumulated expression data for a wider research community, we have developed CyanoEXpress, a web database for interactive exploration and visualisation of transcriptional response patterns in Synechocystis. CyanoEXpress currently comprises expression data for 3073 genes and 178 environmental and genetic perturbations obtained in 31 independent studies. At present, CyanoEXpress constitutes the most comprehensive collection of expression data available for Synechocystis and can be freely accessed. AVAILABILITY: The database is available for free at http://cyanoexpress.sysbiolab.eu