Manipulation of Ag nanoparticles utilizing noncontact atomic force microscopy

We have developed a scheme to manipulate metallic aerosol particles on silicon dioxide substrates using an atomic force microscope. The method utilizes the noncontact mode both for locating and moving nanoparticles of size 10–100 nm. The main advantage of our technique is the possibility of “seeing” the moving particle in real time. Our method avoids well sticking problems that typically hamper the manipulation in the contact mode.Peer reviewe

Noise of a single electron transistor on a Si3N4 membrane

We have investigated the influence of electron-beam writing on the creation of charge trapping centers which cause 1/f noise in single electron transistors (SET). Two Al/AlOx/Al devices were compared: one where the SET is on a {100} silicon wafer covered by a 120-nm-thick layer of Si3N4, and another one in which the Si was etched away from below the nitride membrane before patterning the SET. The background charge noise was found to be 1×10 exp −3 e/√Hz at 10 Hz in both devices, independent of the substrate thickness.Peer reviewe

Averaged Methods for Vortex-String Evolution

We discuss friction-dominated vortex-string evolution using a new analytic model recently developed by the authors. By treating the average string velocity, as well as the characteristic lengthscale, as dynamical variables, we can provide a quantitative picture of the complete evolution of a vortex-string network. Previously known scaling laws are confirmed, and new quantitative predictions regarding loop production and evolution are made.Comment: REVTeX, 21 pages, 23 .eps files included. Submitted to Phys. Rev. B. Minor changes---but some key concepts clarifie

Elevated atmospheric CO2 and humidity delay leaf fall in Betula pendula, but not in Alnus glutinosa or Populus tremula × tremuloides

Context: Anthropogenic activity has increased the level of atmospheric CO2, which is driving an increase of global temperatures and associated changes in precipitation patterns. At Northern latitudes, one of the likely consequences of global warming is increased precipitation and air humidity. Aims: In this work, the effects of both elevated atmospheric CO2 and increased air humidity on trees commonly growing in northern European forests were assessed. Methods: The work was carried out under field conditions by using Free Air Carbon dioxide Enrichment (FACE) and Free Air Humidity Manipulation (FAHM) systems. Leaf litter fall was measured over 4 years (FACE) or 5 years (FAHM) to determine the effects of FACE and FAHM on leaf phenology. Results: Increasing air humidity delayed leaf litter fall in Betula pendula, but not in Populus tremula × tremuloides. Similarly, under elevated atmospheric CO2, leaf litter fall was delayed in Betula pendula, but not in Alnus glutinosa. Increased CO2 appeared to interact with periods of low precipitation in summer and high ozone levels during these periods to effect leaf fall. Conclusions: This work shows that increased CO2 and humidity delay leaf fall, but this effect is species specific

Learning Transcriptional Regulatory Relationships Using Sparse Graphical Models

Understanding the organization and function of transcriptional regulatory networks by analyzing high-throughput gene expression profiles is a key problem in computational biology. The challenges in this work are 1) the lack of complete knowledge of the regulatory relationship between the regulators and the associated genes, 2) the potential for spurious associations due to confounding factors, and 3) the number of parameters to learn is usually larger than the number of available microarray experiments. We present a sparse (L1 regularized) graphical model to address these challenges. Our model incorporates known transcription factors and introduces hidden variables to represent possible unknown transcription and confounding factors. The expression level of a gene is modeled as a linear combination of the expression levels of known transcription factors and hidden factors. Using gene expression data covering 39,296 oligonucleotide probes from 1109 human liver samples, we demonstrate that our model better predicts out-of-sample data than a model with no hidden variables. We also show that some of the gene sets associated with hidden variables are strongly correlated with Gene Ontology categories. The software including source code is available at http://grnl1.codeplex.com

Evidence for Divergent Evolution of Growth Temperature Preference in Sympatric Saccharomyces Species

The genus Saccharomyces currently includes eight species in addition to the model yeast Saccharomyces cerevisiae, most of which can be consistently isolated from tree bark and soil. We recently found sympatric pairs of Saccharomyces species, composed of one cryotolerant and one thermotolerant species in oak bark samples of various geographic origins. In order to contribute to explain the occurrence in sympatry of Saccharomyces species, we screened Saccharomyces genomic data for protein divergence that might be correlated to distinct growth temperature preferences of the species, using the dN/dS ratio as a measure of protein evolution rates and pair-wise species comparisons. In addition to proteins previously implicated in growth at suboptimal temperatures, we found that glycolytic enzymes were among the proteins exhibiting higher than expected divergence when one cryotolerant and one thermotolerant species are compared. By measuring glycolytic fluxes and glycolytic enzymatic activities in different species and at different temperatures, we subsequently show that the unusual divergence of glycolytic genes may be related to divergent evolution of the glycolytic pathway aligning its performance to the growth temperature profiles of the different species. In general, our results support the view that growth temperature preference is a trait that may have undergone divergent selection in the course of ecological speciation in Saccharomyces

Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.National Institutes of Health (U.S.) (Grant GM081871

Meta-analysis of type 2 Diabetes in African Americans Consortium

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94)<P<5 × 10(-8), odds ratio (OR)  = 1.09 to 1.36). Fine-mapping revealed that 88 of 158 previously identified T2D or glucose homeostasis loci demonstrated nominal to highly significant association (2.2 × 10(-23) < locus-wide P<0.05). These novel and previously identified loci yielded a sibling relative risk of 1.19, explaining 17.5% of the phenotypic variance of T2D on the liability scale in African Americans. Overall, this study identified two novel susceptibility loci for T2D in African Americans. A substantial number of previously reported loci are transferable to African Americans after accounting for linkage disequilibrium, enabling fine mapping of causal variants in trans-ethnic meta-analysis studies.Peer reviewe