Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT

Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential regionof-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4%, 92%, 96% and 98.51% with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People’s Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7% sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients

Chemical evolution of classical and ultra-faint dwarf spheroidal galaxies

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2014 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reservedPeer reviewedFinal Published versio

The nature of Long-GRB host galaxies from chemical abundances

Gamma-ray bursts (GRBs) are the most energetic events after the Big Bang and they have been observed up to very high redshift. By means of measures of chemical abundances now available for the galaxies hosting such events,thought to originate from the explosion of very powerful supernovae (Type Ib/c), we have the opportunity to study the nature of these host galaxies. The aim of this paper is to identify the hosts of Long GRBs (LGRBs) observed both at low and high redshift to see whether the hosts can be galaxies of the same type observed at different cosmic epochs. We adopt detailed chemical evolution models for galaxies of different morphological type (ellipticals, spirals, irregulars) which follow the time evolution of the abundances of several chemical elements (H, He, $\alpha$-elements, Fe), and compare the results with the observed abundances and abundance ratios in galaxies hosting LGRBs. We find that the abundances and abundance ratios predicted by models devised for typical irregular galaxies can well fit the abundances in the hosts both at high and low redshift. We also find that the predicted Type Ib/c supernova rate for irregulars is in good agreement with observations. Models for spirals and particularly ellipticals do not fit the high-redshift hosts of LGRBs (DLA systems) nor the low redshift hosts: in particular, ellipticals cannot possibly be the hosts of gamma-ray-bursts at low redshift since they do not show any star formation, and therefore no supernovae Ib/c. We conclude that the observed abundance and abundance ratios in LGRBs hosts suggest that these hosts are irregular galaxies both at high and low redshift thus showing that the host galaxies belong to in an evolutionary sequence.Comment: 8 pages, 9 figures, two references adde

Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks

As email workloads keep rising, email servers need to handle this explosive growth while offering good quality of service to users. In this work, we focus on modeling the workload of the email servers of four universities (2 from Greece, 1 from the UK, 1 from Australia). We model all types of email traffic, including user and system emails, as well as spam. We initially tested some of the most popular distributions for workload characterization and used statistical tests to evaluate our findings. The significant differences in the prediction accuracy results for the four datasets led us to investigate the use of a Recurrent Neural Network (RNN) as time series modeling to model the server workload, which is a first for such a problem. Our results show that the use of RNN modeling leads in most cases to high modeling accuracy for all four campus email traffic datasets

Dark sectors 2016 Workshop: community report

This report, based on the Dark Sectors workshop at SLAC in April 2016, summarizes the scientific importance of searches for dark sector dark matter and forces at masses beneath the weak-scale, the status of this broad international field, the important milestones motivating future exploration, and promising experimental opportunities to reach these milestones over the next 5-10 years

Measurement of the CP-violating phase \phi s in Bs->J/\psi\pi+\pi- decays

Measurement of the mixing-induced CP-violating phase phi_s in Bs decays is of prime importance in probing new physics. Here 7421 +/- 105 signal events from the dominantly CP-odd final state J/\psi pi+ pi- are selected in 1/fb of pp collision data collected at sqrt{s} = 7 TeV with the LHCb detector. A time-dependent fit to the data yields a value of phi_s=-0.019^{+0.173+0.004}_{-0.174-0.003} rad, consistent with the Standard Model expectation. No evidence of direct CP violation is found.Comment: 15 pages, 10 figures; minor revisions on May 23, 201

Observation of an Excited Bc+ State

Using pp collision data corresponding to an integrated luminosity of 8.5 fb-1 recorded by the LHCb experiment at center-of-mass energies of s=7, 8, and 13 TeV, the observation of an excited Bc+ state in the Bc+π+π- invariant-mass spectrum is reported. The observed peak has a mass of 6841.2±0.6(stat)±0.1(syst)±0.8(Bc+) MeV/c2, where the last uncertainty is due to the limited knowledge of the Bc+ mass. It is consistent with expectations of the Bc∗(2S31)+ state reconstructed without the low-energy photon from the Bc∗(1S31)+→Bc+γ decay following Bc∗(2S31)+→Bc∗(1S31)+π+π-. A second state is seen with a global (local) statistical significance of 2.2σ (3.2σ) and a mass of 6872.1±1.3(stat)±0.1(syst)±0.8(Bc+) MeV/c2, and is consistent with the Bc(2S10)+ state. These mass measurements are the most precise to date

JunB transcription factor maintains skeletal muscle mass and promotes hypertrophy

Decreasing JunB expression causes muscle atrophy, whereas overexpression induces hypertrophy and blocks atrophy via myostatin inhibition and regulation of atrogin-1 and MuRF expression via FoxO3

Differential branching fraction and angular analysis of the decay B0→K∗0μ+μ−

The angular distribution and differential branching fraction of the decay B 0→ K ∗0 μ + μ − are studied using a data sample, collected by the LHCb experiment in pp collisions at s√=7 TeV, corresponding to an integrated luminosity of 1.0 fb−1. Several angular observables are measured in bins of the dimuon invariant mass squared, q 2. A first measurement of the zero-crossing point of the forward-backward asymmetry of the dimuon system is also presented. The zero-crossing point is measured to be q20=4.9±0.9GeV2/c4 , where the uncertainty is the sum of statistical and systematic uncertainties. The results are consistent with the Standard Model predictions