CAMEO Stakeholders Report

Computer-Aided Management of Emergency Operations (CAMEO) is a suite of software applications used to plan for and respond to chemical emergencies. CAMEO was first released in 1986, and was jointly developed by the U.S. Environmental Protection Agency (US EPA) and the National Oceanic and Atmospheric Administration (NOAA) to assist front-line chemical emergency planners and responders. It has since undergone numerous modification and upgrades, and is a critical tool used today for chemical spills, other hazards, and emergency management. The CAMEO system integrates a chemical database and a method to manage the data, an air dispersion model, and a mapping capability. All modules work interactively to share and display critical information in a timely fashion. As a result of fatal chemical accidents in recent years, Executive Order (EO) 13650 (Improving Chemical Facility Safety and Security) was signed on August 1, 2013 for: Improving Operational Coordination with State, Local and Tribal partners Enhancing Federal Coordination Enhancing Information Collection and Sharing Modernizing Regulations, Guidance, Policy and Standards Identifying Best Practices. The CAMEO team has been working to address these EO requirements and the areas of action in a manner that will best meet the needs of CAMEO users and stakeholders

Recent investigations of early Roman cameo glass : Part 1 . Cameo manufacturing technique and rotary scratches of ancient glass vessels

The manufacturing traces of ancient cameo glass were investigated. These investigations had a surprising result: early Roman cameo glass vessels have not been cut from overlay blanks. Instead, their manufacture appears to be related to the molding of multi-layered cameo glass gems and to the contemporary relief ceramics. The basic principle of the assumed cameo glass manufacturing process has been experimentally verified. Independently, the typical rotary "Scratches" of ancient glass vessels were investigated. Rotary Scratches are also a typical feature of early Roman cameo glass. The investigation confirms that these Scratches are not grinding marks. They were obviously generated during the hot manufacturing process

Recent investigations of early Roman cameo glass : Part 2. X-ray fluorescence analyses induced by Synchrotron radiation

The elemental composition of 14 Roman cameo glass fragments was measured quantitatively b y X-ray fluorescence analysis induced by synchrotron radiation. The study was intended to learn more about the possible manufacturing techniques of these glasses. In the white cameo decor of nine fragments all belonging to vessels a higher lead oxide concentration was detected compared to the colored body. In contrast, lead oxide is not enhanced in the remaining five fragments from cameo disks or plates. The higher concentrations may be interpreted as flux added to lower the melting temperature of the white cameo layers of the vessels, thus supporting the recent hypothesis that these cameo vessels were manufactured with the help of a mold on a turning wheel, which had not to be used for the production of cameo plates

The History of the Cameo Theater

On March 20, 2015, Pastor Doug Robins held a church service in the Cameo Theater. He remembered one of the parishioners stating, “Hey, Pastor Doug, I went to a rave and I did Ecstasy right there, and now I’m coming to church here.” From its founding in 1940, up to the present day, the Cameo Theater has been a host to a number of diverse events, everything from religious services to raves. On June 11, 1940, the Cameo Theater opened and was owned by Carl Milentz

High sensitivity double beta decay study of 116-Cd and 100-Mo with the BOREXINO Counting Test Facility (CAMEO project)

The unique features (super-low background and large sensitive volume) of the CTF and BOREXINO set ups are used in the CAMEO project for a high sensitivity study of 100-Mo and 116-Cd neutrinoless double beta decay. Pilot measurements with 116-Cd and Monte Carlo simulations show that the sensitivity of the CAMEO experiment (in terms of the half-life limit for neutrinoless double beta decay) is (3-5) 10^24 yr with a 1 kg source of 100-Mo (116-Cd, 82-Se, and 150-Nd) and about 10^26 yr with 65 kg of enriched 116-CdWO_4 crystals placed in the liquid scintillator of the CTF. The last value corresponds to a limit on the neutrino mass of less than 0.06 eV. Similarly with 1000 kg of 116-CdWO_4 crystals located in the BOREXINO apparatus the neutrino mass limit can be pushed down to m_nu<0.02 eV.Comment: 29 pages, LaTex, 9 eps figure

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold