Structure and electronic transport in graphene wrinkles

Wrinkling is a ubiquitous phenomenon in two-dimensional membranes. In particular, in the large-scale growth of graphene on metallic substrates, high densities of wrinkles are commonly observed. Despite their prevalence and potential impact on large-scale graphene electronics, relatively little is known about their structural morphology and electronic properties. Surveying the graphene landscape using atomic force microscopy, we found that wrinkles reach a certain maximum height before folding over. Calculations of the energetics explain the morphological transition, and indicate that the tall ripples are collapsed into narrow standing wrinkles by van der Waals forces, analogous to large-diameter nanotubes. Quantum transport calculations show that conductance through these collapsed wrinkle structures is limited mainly by a density-of-states bottleneck and by interlayer tunneling across the collapsed bilayer region. Also through systematic measurements across large numbers of devices with wide folded wrinkles, we find a distinct anisotropy in their electrical resistivity, consistent with our transport simulations. These results highlight the coupling between morphology and electronic properties, which has important practical implications for large-scale high-speed graphene electronics.Comment: 5 figures supplemental information in separated fil

FRIOD: a deeply integrated feature-rich interactive system for effective and efficient outlier detection

In this paper, we propose an novel interactive outlier detection system called feature-rich interactive outlier detection (FRIOD), which features a deep integration of human interaction to improve detection performance and greatly streamline the detection process. A user-friendly interactive mechanism is developed to allow easy and intuitive user interaction in all the major stages of the underlying outlier detection algorithm which includes dense cell selection, location-aware distance thresholding, and final top outlier validation. By doing so, we can mitigate the major difficulty of the competitive outlier detection methods in specifying the key parameter values, such as the density and distance thresholds. An innovative optimization approach is also proposed to optimize the grid-based space partitioning, which is a critical step of FRIOD. Such optimization fully considers the high-quality outliers it detects with the aid of human interaction. The experimental evaluation demonstrates that FRIOD can improve the quality of the detected outliers and make the detection process more intuitive, effective, and efficient

Byzantine Stochastic Gradient Descent

This paper studies the problem of distributed stochastic optimization in an adversarial setting where, out of the $m$ machines which allegedly compute stochastic gradients every iteration, an $\alpha$-fraction are Byzantine, and can behave arbitrarily and adversarially. Our main result is a variant of stochastic gradient descent (SGD) which finds $\varepsilon$-approximate minimizers of convex functions in $T = \tilde{O}\big( \frac{1}{\varepsilon^2 m} + \frac{\alpha^2}{\varepsilon^2} \big)$ iterations. In contrast, traditional mini-batch SGD needs $T = O\big( \frac{1}{\varepsilon^2 m} \big)$ iterations, but cannot tolerate Byzantine failures. Further, we provide a lower bound showing that, up to logarithmic factors, our algorithm is information-theoretically optimal both in terms of sampling complexity and time complexity

Encrypted accelerated least squares regression.

Information that is stored in an encrypted format is, by definition, usually not amenable to statistical analysis or machine learning methods. In this paper we present detailed analysis of coordinate and accelerated gradient descent algorithms which are capable of fitting least squares and penalised ridge regression models, using data encrypted under a fully homomorphic encryption scheme. Gradient descent is shown to dominate in terms of encrypted computational speed, and theoretical results are proven to give parameter bounds which ensure correctness of decryption. The characteristics of encrypted computation are empirically shown to favour a non-standard acceleration technique. This demonstrates the possibility of approximating conventional statistical regression methods using encrypted data without compromising privacy

Sparse panicle1 is required for inflorescence development in Setaria viridis and maize.

Setaria viridis is a rapid-life-cycle model panicoid grass. To identify genes that may contribute to inflorescence architecture and thus have the potential to influence grain yield in related crops such as maize, we conducted an N-nitroso-N-methylurea (NMU) mutagenesis of S. viridis and screened for visible inflorescence mutant phenotypes. Of the approximately 2,700 M2 families screened, we identified four recessive sparse panicle mutants (spp1-spp4) characterized by reduced and uneven branching of the inflorescence. To identify the gene underlying the sparse panicle1 (spp1) phenotype, we performed bulked segregant analysis and deep sequencing to fine map it to an approximately 1 Mb interval. Within this interval, we identified disruptive mutations in two genes. Complementation tests between spp1 and spp3 revealed they were allelic, and deep sequencing of spp3 identified an independent disruptive mutation in SvAUX1 (AUXIN1), one of the two genes in the ∼1 Mb interval and the only gene disruption shared between spp1 and spp3. SvAUX1 was found to affect both inflorescence development and root gravitropism in S. viridis. A search for orthologous mutant alleles in maize confirmed a very similar role of ZmAUX1 in maize, which highlights the utility of S. viridis in accelerating functional genomic studies in maize

Glibenclamide Induces Collagen IV Catabolism in High Glucose-Stimulated Mesangial Cells

We have shown the full prevention of mesangial expansion in insulin-deficient diabetic rats by treatment with clinically-relevant dosages of glibenclamide (Glib). Studies in mesangial cells (MCs) also demonstrated reduction in the high glucose (HG)-induced accumulation of collagens, proposing that this was due to increased catabolism. In the present study, we investigated the signaling pathways that may be implicated in Glib action. Rat primary MCs were exposed to HG for 8 weeks with or without Glib in therapeutic (0.01 μM) or supratherapeutic (1.0 μM) concentrations. We found that HG increased collagen IV protein accumulation and PAI-1 mRNA and protein expression, in association with decreased cAMP generating capacity and decreased PKA activity. Low Glib increased collagen IV mRNA but fully prevented collagen IV protein accumulation and PAI-1 overexpression while enhancing cAMP formation and PKA activity. MMP2 mRNA, protein expression and gelatinolytic activity were also enhanced. High Glib was, overall, ineffective. In conclusion, low dosage/concentration Glib prevents HG-induced collagen accumulation in MC by enhancing collagen catabolism in a cAMP-PKA-mediated PAI-1 inhibition

De Novo Transcriptome of the Hemimetabolous German Cockroach (\u3ci\u3eBlattella germanica\u3c/i\u3e)

Background: The German cockroach, Blattella germanica, is an important insect pest that transmits various pathogens mechanically and causes severe allergic diseases. This insect has long served as a model system for studies of insect biology, physiology and ecology. However, the lack of genome or transcriptome information heavily hinder our further understanding about the German cockroach in every aspect at a molecular level and on a genome-wide scale. To explore the transcriptome and identify unique sequences of interest, we subjected the B. germanica transcriptome to massively parallel pyrosequencing and generated the first reference transcriptome for B. germanica. Methodology/Principal Findings: A total of 1,365,609 raw reads with an average length of 529 bp were generated via pyrosequencing the mixed cDNA library from different life stages of German cockroach including maturing oothecae, nymphs, adult females and males. The raw reads were de novo assembled to 48,800 contigs and 3,961 singletons with highquality unique sequences. These sequences were annotated and classified functionally in terms of BLAST, GO and KEGG, and the genes putatively coding detoxification enzyme systems, insecticide targets, key components in systematic RNA interference, immunity and chemoreception pathways were identified. A total of 3,601 SSRs (Simple Sequence Repeats) loci were also predicted. Conclusions/Significance: The whole transcriptome pyrosequencing data from this study provides a usable genetic resource for future identification of potential functional genes involved in various biological processes