503 research outputs found
Efficient simulations with electronic open boundaries
We present a reformulation of the Hairy Probe method for introducing electronic open boundaries that is appropriate for steady state calculations involving non-orthogonal atomic basis sets. As a check on the correctness of the method we investigate a perfect atomic wire of Cu atoms, and a perfect non-orthogonal chain of H atoms. For both atom chains we find that the conductance has a value of exactly one quantum unit, and that this is rather insensitive to the strength of coupling of the probes to the system, provided values of the coupling are of the same order as the mean inter-level spacing of the system without probes. For the Cu atom chain we find in addition that away from the regions with probes attached, the potential in the wire is uniform, while within them it follows a predicted exponential variation with position. We then apply the method to an initial investigation of the suitability of graphene as a contact material for molecular electronics. We perform calculations on a carbon nanoribbon to determine the correct coupling strength of the probes to the graphene, and obtain a conductance of about two quantum units corresponding to two bands crossing the Fermi surface. We then compute the current through a benzene molecule attached to two graphene contacts and find only a very weak current because of the disruption of the π-conjugation by the covalent bond between the benzene and the graphene. In all cases we find that very strong or weak probe couplings suppress the current
Geochemical characterization of oceanic basalts using Artificial Neural Network
The geochemical discriminate diagrams help to distinguish the volcanics recovered from different tectonic settings but these diagrams tend to group the ocean floor basalts (OFB) under one class i.e., as mid-oceanic ridge basalts (MORB). Hence, a method is specifically needed to identify the OFB as normal (N-MORB), enriched (E-MORB) and ocean island basalts (OIB)
Boundary Conditions and Unitarity: the Maxwell-Chern-Simons System in AdS_3/CFT_2
We consider the holography of the Abelian Maxwell-Chern-Simons (MCS) system
in Lorentzian three-dimensional asymptotically-AdS spacetimes, and discuss a
broad class of boundary conditions consistent with conservation of the
symplectic structure. As is well-known, the MCS theory contains a massive
sector dual to a vector operator in the boundary theory, and a topological
sector consisting of flat connections dual to U(1) chiral currents; the
boundary conditions we examine include double-trace deformations in these two
sectors, as well as a class of boundary conditions that mix the vector
operators with the chiral currents. We carefully study the symplectic product
of bulk modes and show that almost all such boundary conditions induce
instabilities and/or ghost excitations, consistent with violations of unitarity
bounds in the dual theory.Comment: 50+1 pages, 6 figures, PDFLaTeX; v2: added references, corrected
typo
A DNA-based method for studying root responses to drought in field-grown wheat genotypes
Root systems are critical for water and nutrient acquisition by crops. Current methods measuring root biomass and length are slow and labour-intensive for studying root responses to environmental stresses in the field. Here, we report the development of a method that measures changes in the root DNA concentration in soil and detects root responses to drought in controlled environment and field trials. To allow comparison of soil DNA concentrations from different wheat genotypes, we also developed a procedure for correcting genotypic differences in the copy number of the target DNA sequence. The new method eliminates the need for separation of roots from soil and permits large-scale phenotyping of root responses to drought or other environmental and disease stresses in the field.Chun Y. Huang, Haydn Kuchel, James Edwards, Sharla Hall, Boris Parent, Paul Eckermann, Herdina, Diana M. Hartley, Peter Langridge & Alan C. McKa
High Resolution Detection and Analysis of CpG Dinucleotides Methylation Using MBD-Seq Technology
Methyl-CpG binding domain protein sequencing (MBD-seq) is widely used to survey DNA methylation patterns. However, the optimal experimental parameters for MBD-seq remain unclear and the data analysis remains challenging. In this study, we generated high depth MBD-seq data in MCF-7 cell and developed a bi-asymmetric-Laplace model (BALM) to perform data analysis. We found that optimal efficiency of MBD-seq experiments was achieved by sequencing ∼100 million unique mapped tags from a combination of 500 mM and 1000 mM salt concentration elution in MCF-7 cells. Clonal bisulfite sequencing results showed that the methylation status of each CpG dinucleotides in the tested regions was accurately detected with high resolution using the proposed model. These results demonstrated the combination of MBD-seq and BALM could serve as a useful tool to investigate DNA methylome due to its low cost, high specificity, efficiency and resolution
Host epigenetic modifications by oncogenic viruses
Epigenetic alterations represent an important step in the initiation and progression of most human cancers, but it is difficult to differentiate the early cancer causing alterations from later consequences. Oncogenic viruses can induce transformation via expression of only a small number of viral genes. Therefore, the mechanisms by which oncogenic viruses cause cancer may provide clues as to which epigenetic alterations are critical in early carcinogenesis
ChromaSig: A Probabilistic Approach to Finding Common Chromatin Signatures in the Human Genome
Computational methods to identify functional genomic elements using genetic information have been very successful in determining gene structure and in identifying a handful of cis-regulatory elements. But the vast majority of regulatory elements have yet to be discovered, and it has become increasingly apparent that their discovery will not come from using genetic information alone. Recently, high-throughput technologies have enabled the creation of information-rich epigenetic maps, most notably for histone modifications. However, tools that search for functional elements using this epigenetic information have been lacking. Here, we describe an unsupervised learning method called ChromaSig to find, in an unbiased fashion, commonly occurring chromatin signatures in both tiling microarray and sequencing data. Applying this algorithm to nine chromatin marks across a 1% sampling of the human genome in HeLa cells, we recover eight clusters of distinct chromatin signatures, five of which correspond to known patterns associated with transcriptional promoters and enhancers. Interestingly, we observe that the distinct chromatin signatures found at enhancers mark distinct functional classes of enhancers in terms of transcription factor and coactivator binding. In addition, we identify three clusters of novel chromatin signatures that contain evolutionarily conserved sequences and potential cis-regulatory elements. Applying ChromaSig to a panel of 21 chromatin marks mapped genomewide by ChIP-Seq reveals 16 classes of genomic elements marked by distinct chromatin signatures. Interestingly, four classes containing enrichment for repressive histone modifications appear to be locally heterochromatic sites and are enriched in quickly evolving regions of the genome. The utility of this approach in uncovering novel, functionally significant genomic elements will aid future efforts of genome annotation via chromatin modifications
Comparative genomics of the class 4 histone deacetylase family indicates a complex evolutionary history
BACKGROUND: Histone deacetylases are enzymes that modify core histones and play key roles in transcriptional regulation, chromatin assembly, DNA repair, and recombination in eukaryotes. Three types of related histone deacetylases (classes 1, 2, and 4) are widely found in eukaryotes, and structurally related proteins have also been found in some prokaryotes. Here we focus on the evolutionary history of the class 4 histone deacetylase family. RESULTS: Through sequence similarity searches against sequenced genomes and expressed sequence tag data, we identified members of the class 4 histone deacetylase family in 45 eukaryotic and 37 eubacterial species representative of very distant evolutionary lineages. Multiple phylogenetic analyses indicate that the phylogeny of these proteins is, in many respects, at odds with the phylogeny of the species in which they are found. In addition, the eukaryotic members of the class 4 histone deacetylase family clearly display an anomalous phyletic distribution. CONCLUSION: The unexpected phylogenetic relationships within the class 4 histone deacetylase family and the anomalous phyletic distribution of these proteins within eukaryotes might be explained by two mechanisms: ancient gene duplication followed by differential gene losses and/or horizontal gene transfer. We discuss both possibilities in this report, and suggest that the evolutionary history of the class 4 histone deacetylase family may have been shaped by horizontal gene transfers
Systematic Planning of Genome-Scale Experiments in Poorly Studied Species
Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could readily be adapted to the design of other types of large-scale experiments as well as other groups of organisms
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