Effect of a Zn impurity on T_c and its implication to pairing symmetry in LaFeAsO1−x_{1-x}Fx_x

The effect of non-magnetic Zn impurity on superconductivity in LaFe$_{1-y}$Zn$_y$AsO$_{1-x}$F$_x$ system is studied systematically. In the presence of Zn impurity, the superconducting transition temperature increases in the under-doped regime, remains unchanged in the optimally doped regime, and is severely suppressed in the over-doped regime. Our results suggest a switch of the symmetry of the superconducting order parameters from a $s$-wave to $s_{\pm}$ or $d$-wave states as the charge carrier doping increases in FeAs-based superconductors.Comment: 4 pages, 4 figures. Format changed and a few revisons mad

Visual Genome: Connecting language and vision using crowdsourced dense image annotations

Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked “What vehicle is the person riding?”, computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that “the person is riding a horse-drawn carriage.” In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of (Formula presented.) objects, (Formula presented.) attributes, and (Formula presented.) pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs

Presence and diagnostic value of circulating tsncRNA for ovarian tumor

Abstract tRNA-derived small non-coding RNAs (tsncRNAs), a class of newly defined small non-coding RNA, have been considered to be involved in various cellular biological processes through regulating gene expression at both transcriptional and post-transcriptional level. However, the presence of circulating tsncRNAs and their diagnostic potential is largely unclear. In this study, we investigate the serum-derived public transcriptome data from ovarian tumor patients and non-cancer controls, and find that circulating tsncRNAs cover a high proportion of total small RNA and are non-random degradation products in serum (ranging from 2.5–29.4%), which are enriched in several specific types of related tRNA (e.g., Gly-tRNA). Particularly, four tsncRNAs are differentially expressed in serum from cancer patients compared to those from healthy controls, and can predict abnormal cell proliferation with high accuracy. Our results reveal the ubiquitous presence of circulating tsncRNAs in serum, and diagnostic potential of specific tsncRNAs for ovarian tumor