Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

Early detection of pulmonary cancer is the most promising way to enhance a patient's chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection(average FROC-score of 0.891, ranking the 1st place over all submitted results).Comment: MICCAI 2017 accepte

Three-Fold Diffraction Symmetry in Epitaxial Graphene and the SiC Substrate

The crystallographic symmetries and spatial distribution of stacking domains in graphene films on SiC have been studied by low energy electron diffraction (LEED) and dark field imaging in a low energy electron microscope (LEEM). We find that the graphene diffraction spots from 2 and 3 atomic layers of graphene have 3-fold symmetry consistent with AB (Bernal) stacking of the layers. On the contrary, graphene diffraction spots from the buffer layer and monolayer graphene have apparent 6-fold symmetry, although the 3-fold nature of the satellite spots indicates a more complex periodicity in the graphene sheets.Comment: An addendum has been added for the arXiv version only, including one figure with five panels. Published paper can be found at http://link.aps.org/doi/10.1103/PhysRevB.80.24140

Potentially curative treatment in patients with hepatocellular cancer—results from the liver cancer research network

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92135/1/apt5174.pd

Natural Two-Higgs-Doublet Model

We show that the Two-Higgs-Doublet Model (2HDM) constrained by the two-loop-order requirement of cancellation of quadratic divergences is consistent with the existing experimental constraints. The model allows to ameliorate the little hierarchy problem by suppressing the quadratic corrections to scalar masses and lifting the mass of the lightest Higgs boson. A strong source of CP violation emerges from the scalar potential. The cutoff originating from the naturality arguments is shifted from 0.6 TeV in the Standard Model to >6 TeV in the 2HDM, depending on the mass of the lightest scalar.Comment: 2 figures, submitted to Fortschritte der Physik, presented at the 10th Hellenic School on Elementary Particle Physics and Gravity, Corfu 201

Self-doping effects in epitaxially grown graphene

Self-doping in graphene has been studied by examining single-layer epitaxially grown graphene samples with differing characteristic lateral terrace widths. Low energy electron microscopy was used to gain real-space information about the graphene surface morphology, which was compared with data obtained by angle-resolved photoemission spectroscopy to study the effect of the monolayer graphene terrace width on the low energy dispersions. By altering the graphene terrace width, we report significant changes in the electronic structure and quasiparticle relaxation time of the material, in addition to a terrace width-dependent doping effect.Comment: Published in Applied Physics Letters 93, 243119 (2008

Genome-wide analysis reveals extensive functional interaction between DNA replication initiation and transcription in the genome of trypanosoma brucei

Identification of replication initiation sites, termed origins, is a crucial step in understanding genome transmission in any organism. Transcription of the Trypanosoma brucei genome is highly unusual, with each chromosome comprising a few discrete transcription units. To understand how DNA replication occurs in the context of such organization, we have performed genome-wide mapping of the binding sites of the replication initiator ORC1/CDC6 and have identified replication origins, revealing that both localize to the boundaries of the transcription units. A remarkably small number of active origins is seen, whose spacing is greater than in any other eukaryote. We show that replication and transcription in T. brucei have a profound functional overlap, as reducing ORC1/CDC6 levels leads to genome-wide increases in mRNA levels arising from the boundaries of the transcription units. In addition, ORC1/CDC6 loss causes derepression of silent Variant Surface Glycoprotein genes, which are critical for host immune evasion

A remembrance of things (best) forgotten: The 'allegorical past' and the feminist imagination

This is the author's PDF version of an article published in Feminist theology© 2012. The definitive version is available at http://fth.sagepub.com/This article discusses the US TV series Mad Men, which is set in an advertising agency in 1960s New York, in relation to two key elements which seem significant for a consideration of the current state of feminism in church and academy, both of which centre around what it means to remember or (not) to forget

A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.

A robust experimental evaluation of automated multi-label classification methods

Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effective methods were introduced, especially for traditional classification and regression problems. Apart from the AutoML success, several issues remain open. One issue, in particular, is the lack of ability of AutoML methods to deal with different types of data. Based on this scenario, this paper approaches AutoML for multi-label classification (MLC) problems. In MLC, each example can be simultaneously associated to several class labels, unlike the standard classification task, where an example is associated to just one class label. In this work, we provide a general comparison of five automated multi-label classification methods - two evolutionary methods, one Bayesian optimization method, one random search and one greedy search - on 14 datasets and three designed search spaces. Overall, we observe that the most prominent method is the one based on a canonical grammar-based genetic programming (GGP) search method, namely Auto-MEKAGGP. Auto-MEKAGGP presented the best average results in our comparison and was statistically better than all the other methods in different search spaces and evaluated measures, except when compared to the greedy search method