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

Distinct Fermi Surface Topology and Nodeless Superconducting Gap in (Tl0.58Rb0.42)Fe1.72Se2 Superconductor

High resolution angle-resolved photoemission measurements have been carried out to study the electronic structure and superconducting gap of the (Tl$_{0.58}$Rb$_{0.42}$)Fe$_{1.72}$Se$_2$ superconductor with a T$_c$=32 K. The Fermi surface topology consists of two electron-like Fermi surface sheets around $\Gamma$ point which is distinct from that in all other iron-based compounds reported so far. The Fermi surface around the M point shows a nearly isotropic superconducting gap of $\sim$12 meV. The large Fermi surface near the $\Gamma$ point also shows a nearly isotropic superconducting gap of $\sim$15 meV while no superconducting gap opening is clearly observed for the inner tiny Fermi surface. Our observed new Fermi surface topology and its associated superconducting gap will provide key insights and constraints in understanding superconductivity mechanism in the iron-based superconductors.Comment: 4 pages, 4 figure

Tunable Dirac Fermion Dynamics in Topological Insulators

Three-dimensional topological insulators are characterized by insulating bulk state and metallic surface state involving Dirac fermions that behave as massless relativistic particles. These Dirac fermions are responsible for achieving a number of novel and exotic quantum phenomena in the topological insulators and for their potential applications in spintronics and quantum computations. It is thus essential to understand the electron dynamics of the Dirac fermions, i.e., how they interact with other electrons, phonons and disorders. Here we report super-high resolution angle-resolved photoemission studies on the Dirac fermion dynamics in the prototypical Bi2(Te,Se)3 topological insulators. We have directly revealed signatures of the electron-phonon coupling in these topological insulators and found that the electron-disorder interaction is the dominant factor in the scattering process. The Dirac fermion dynamics in Bi2(Te3-xSex) topological insulators can be tuned by varying the composition, x, or by controlling the charge carriers. Our findings provide crucial information in understanding the electron dynamics of the Dirac fermions in topological insulators and in engineering their surface state for fundamental studies and potential applications.Comment: 14 Pages, 4 Figure

Extraction of Electron Self-Energy and Gap Function in the Superconducting State of Bi_2Sr_2CaCu_2O_8 Superconductor via Laser-Based Angle-Resolved Photoemission

Super-high resolution laser-based angle-resolved photoemission measurements have been performed on a high temperature superconductor Bi_2Sr_2CaCu_2O_8. The band back-bending characteristic of the Bogoliubov-like quasiparticle dispersion is clearly revealed at low temperature in the superconducting state. This makes it possible for the first time to experimentally extract the complex electron self-energy and the complex gap function in the superconducting state. The resultant electron self-energy and gap function exhibit features at ~54 meV and ~40 meV, in addition to the superconducting gap-induced structure at lower binding energy and a broad featureless structure at higher binding energy. These information will provide key insight and constraints on the origin of electron pairing in high temperature superconductors.Comment: 4 pages, 4 figure

Exploring matrix factorization techniques for significant genes identification of Alzheimer’s disease microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>The wide use of high-throughput DNA microarray technology provide an increasingly detailed view of human transcriptome from hundreds to thousands of genes. Although biomedical researchers typically design microarray experiments to explore specific biological contexts, the relationships between genes are hard to identified because they are complex and noisy high-dimensional data and are often hindered by low statistical power. The main challenge now is to extract valuable biological information from the colossal amount of data to gain insight into biological processes and the mechanisms of human disease. To overcome the challenge requires mathematical and computational methods that are versatile enough to capture the underlying biological features and simple enough to be applied efficiently to large datasets.</p> <p>Methods</p> <p>Unsupervised machine learning approaches provide new and efficient analysis of gene expression profiles. In our study, two unsupervised knowledge-based matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF) are integrated to identify significant genes and related pathways in microarray gene expression dataset of Alzheimer’s disease. The advantage of these two approaches is they can be performed as a biclustering method by which genes and conditions can be clustered simultaneously. Furthermore, they can group genes into different categories for identifying related diagnostic pathways and regulatory networks. The difference between these two method lies in ICA assume statistical independence of the expression modes, while NMF need positivity constrains to generate localized gene expression profiles.</p> <p>Results</p> <p>In our work, we performed FastICA and non-smooth NMF methods on DNA microarray gene expression data of Alzheimer’s disease respectively. The simulation results shows that both of the methods can clearly classify severe AD samples from control samples, and the biological analysis of the identified significant genes and their related pathways demonstrated that these genes play a prominent role in AD and relate the activation patterns to AD phenotypes. It is validated that the combination of these two methods is efficient.</p> <p>Conclusions</p> <p>Unsupervised matrix factorization methods provide efficient tools to analyze high-throughput microarray dataset. According to the facts that different unsupervised approaches explore correlations in the high-dimensional data space and identify relevant subspace base on different hypotheses, integrating these methods to explore the underlying biological information from microarray dataset is an efficient approach. By combining the significant genes identified by both ICA and NMF, the biological analysis shows great efficient for elucidating the molecular taxonomy of Alzheimer’s disease and enable better experimental design to further identify potential pathways and therapeutic targets of AD.</p

Tissue Regeneration: A Silk Road

Silk proteins are natural biopolymers that have extensive structural possibilities for chemical and mechanical modifications to facilitate novel properties, functions, and applications in the biomedical field. The versatile processability of silk fibroins (SF) into different forms such as gels, films, foams, membranes, scaffolds, and nanofibers makes it appealing in a variety of applications that require mechanically superior, biocompatible, biodegradable, and functionalizable biomaterials. There is no doubt that nature is the world’s best biological engineer, with simple, exquisite but powerful designs that have inspired novel technologies. By understanding the surface interaction of silk materials with living cells, unique characteristics can be implemented through structural modifications, such as controllable wettability, high-strength adhesiveness, and reflectivity properties, suggesting its potential suitability for surgical, optical, and other biomedical applications. All of the interesting features of SF, such as tunable biodegradation, anti-bacterial properties, and mechanical properties combined with potential self-healing modifications, make it ideal for future tissue engineering applications. In this review, we first demonstrate the current understanding of the structures and mechanical properties of SF and the various functionalizations of SF matrices through chemical and physical manipulations. Then the diverse applications of SF architectures and scaffolds for different regenerative medicine will be discussed in detail, including their current applications in bone, eye, nerve, skin, tendon, ligament, and cartilage regeneration