First Time Rapid and Accurate Detection of Massive Number of Metal Absorption Lines in the Early Universe Using Deep Neural Network

Science.gov (United States)

Zhao, Yinan; Ge, Jian; Yuan, Xiaoyong; Li, Xiaolin; Zhao, Tiffany; Wang, Cindy

2018-01-01

Metal absorption line systems in the distant quasar spectra have been used as one of the most powerful tools to probe gas content in the early Universe. The MgII λλ 2796, 2803 doublet is one of the most popular metal absorption lines and has been used to trace gas and global star formation at redshifts between ~0.5 to 2.5. In the past, machine learning algorithms have been used to detect absorption lines systems in the large sky survey, such as Principle Component Analysis, Gaussian Process and decision tree, but the overall detection process is not only complicated, but also time consuming. It usually takes a few months to go through the entire quasar spectral dataset from each of the Sloan Digital Sky Survey (SDSS) data release. In this work, we applied the deep neural network, or “ deep learning” algorithms, in the most recently SDSS DR14 quasar spectra and were able to randomly search 20000 quasar spectra and detect 2887 strong Mg II absorption features in just 9 seconds. Our detection algorithms were verified with previously released DR12 and DR7 data and published Mg II catalog and the detection accuracy is 90%. This is the first time that deep neural network has demonstrated its promising power in both speed and accuracy in replacing tedious, repetitive human work in searching for narrow absorption patterns in a big dataset. We will present our detection algorithms and also statistical results of the newly detected Mg II absorption lines.

Two case studies on the origin of aqueous sulphate in deep crystalline rocks

International Nuclear Information System (INIS)

Michelot, J.L.; Fontes, J.C.

1987-01-01

The paper reports preliminary results obtained from studies in Central Sweden (Stripa) and in Northern Switzerland (Boettstein). The isotopic compositions ( 34 S, 18 O) of dissolved sulphates in shallow and deep groundwaters from the Stripa test site show that (1) the origins of the salinity in the shallow and in the deep groundwaters are probably different, (2) the low sulphate content of the waters collected from the upper part of the deep aquifer system could be derived from the shallow aqueous sulphate through bacterial reduction, (3) a deeper bulk of sulphate can be identified. After examining several hypotheses, a Permian or Triassic origin is attributed to this deep sulphate. Boettstein is the first drilled borehole of the NAGRA (Swiss National Co-operative for the Storage of Radioactive Wastes programme). In the 34 S versus 18 O diagram, most of the representative points of samples collected at different depths (from apparently different water bodies), lie along a straight line. It seems that this line cannot be a reduction line, nor a precipitation line (gypsum or anhydrite). It is thus interpreted as a mixing line. The end members of this mixing line are still unknown. However, a deep brine is present at the bottom of the system, probably related to brines circulating in the Permian channel found at the same depth, a few kilometres away. A working hypothesis involving this deep brine as a source for both end members of the mixing, through two different processes, is presented, with the problem of possible connections between the different water bodies. (author). 16 refs, 5 figs, 2 tabs

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

Science.gov (United States)

Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

2018-03-01

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

Measurement of fish movements at depths to 6000 m using a deep-ocean lander incorporating a short base-line sonar utilizing miniature code-activated transponder technology

Science.gov (United States)

Bagley, P. M.; Bradley, S.; Priede, I. G.; Gray, P.

1999-12-01

Most research on animal behaviour in the deep ocean (to depths of 6000 m) is restricted to the capture of dead specimens or viewing activity over small areas of the sea floor by means of cameras or submersibles. This paper describes the use of a miniature acoustic code-activated transponder (CAT) tag and short base-line sonar to track the movements of deep-sea fish in two dimensions over an area 1 km in diameter centred on a lander platform. The CAT tags and sonar are transported to the deep-sea floor by means of a subsea mooring which is ballasted so that it lands and remains on the sea floor for the duration of the tracking experiment (the lander). A description of the CAT, lander and short base-line sonar is given. Results are presented to illustrate the operation of the system.

Deep learning for SAR image formation

Science.gov (United States)

Mason, Eric; Yonel, Bariscan; Yazici, Birsen

2017-04-01

The recent success of deep learning has lead to growing interest in applying these methods to signal processing problems. This paper explores the applications of deep learning to synthetic aperture radar (SAR) image formation. We review deep learning from a perspective relevant to SAR image formation. Our objective is to address SAR image formation in the presence of uncertainties in the SAR forward model. We present a recurrent auto-encoder network architecture based on the iterative shrinkage thresholding algorithm (ISTA) that incorporates SAR modeling. We then present an off-line training method using stochastic gradient descent and discuss the challenges and key steps of learning. Lastly, we show experimentally that our method can be used to form focused images in the presence of phase uncertainties. We demonstrate that the resulting algorithm has faster convergence and decreased reconstruction error than that of ISTA.

A DEEP X-RAY VIEW OF THE BARE AGN ARK 120. I. REVEALING THE SOFT X-RAY LINE EMISSION

Energy Technology Data Exchange (ETDEWEB)

Reeves, J. N.; Braito, V. [Center for Space Science and Technology, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 (United States); Porquet, D. [Observatoire Astronomique de Strasbourg, Université de Strasbourg, CNRS, UMR 7550, 11 rue de l’Université, F-67000 Strasbourg (France); Nardini, E. [Astrophysics Group, School of Physical and Geographical Sciences, Keele University, Keele, Staffordshire, ST5 5BG (United Kingdom); Lobban, A. [Dept of Physics and Astronomy, University of Leicester, University Road, Leicester LE1 7RH (United Kingdom); Turner, T. J., E-mail: jreeves@umbc.edu, E-mail: j.n.reeves@keele.ac.uk [Department of Physics, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 (United States)

2016-09-10

The Seyfert 1 galaxy Ark 120 is a prototype example of the so-called class of bare nucleus active galactic nuclei (AGNs), whereby there is no known evidence for the presence of ionized gas along the direct line of sight. Here deep (>400 ks exposure), high-resolution X-ray spectroscopy of Ark 120 is presented from XMM-Newton observations that were carried out in 2014 March, together with simultaneous Chandra /High Energy Transmission Grating exposures. The high-resolution spectra confirmed the lack of intrinsic absorbing gas associated with Ark 120, with the only X-ray absorption present originating from the interstellar medium (ISM) of our own Galaxy, with a possible slight enhancement of the oxygen abundance required with respect to the expected ISM values in the solar neighborhood. However, the presence of several soft X-ray emission lines are revealed for the first time in the XMM-Newton RGS spectrum, associated with the AGN and arising from the He- and H-like ions of N, O, Ne, and Mg. The He-like line profiles of N, O, and Ne appear velocity broadened, with typical FWHMs of ∼5000 km s{sup −1}, whereas the H-like profiles are unresolved. From the clean measurement of the He-like triplets, we deduce that the broad lines arise from a gas of density n {sub e} ∼ 10{sup 11} cm{sup −3}, while the photoionization calculations infer that the emitting gas covers at least 10% of 4 π steradian. Thus the broad soft X-ray profiles appear coincident with an X-ray component of the optical–UV broad-line region on sub-parsec scales, whereas the narrow profiles originate on larger parsec scales, perhaps coincident with the AGN narrow-line region. The observations show that Ark 120 is not intrinsically bare and substantial X-ray-emitting gas exists out of our direct line of sight toward this AGN.

THE ALMA SPECTROSCOPIC SURVEY IN THE HUBBLE ULTRA DEEP FIELD: IMPLICATIONS FOR SPECTRAL LINE INTENSITY MAPPING AT MILLIMETER WAVELENGTHS AND CMB SPECTRAL DISTORTIONS

Energy Technology Data Exchange (ETDEWEB)

Carilli, C. L.; Walter, F. [National Radio Astronomy Observatory, P.O. Box 0, Socorro, NM 87801 (United States); Chluba, J. [Jodrell Bank Centre for Astrophysics, University of Manchester, Oxford Road, M13 9PL (United Kingdom); Decarli, R. [Max-Planck Institute for Astronomy, D-69117 Heidelberg (Germany); Aravena, M. [Nucleo de Astronomia, Facultad de Ingenieria, Universidad Diego Portales, Av. Ejercito 441, Santiago (Chile); Wagg, J. [Square Kilometre Array Organisation, Lower Withington, Cheshire (United Kingdom); Popping, G. [European Southern Observatory, Karl-Schwarzschild-Strasse 2, D-85748, Garching (Germany); Cortes, P. [Joint ALMA Observatory—ESO, Av. Alonso de Cordova, 3104, Santiago (Chile); Hodge, J. [Leiden Observatory, Leiden University, Niels Bohrweg 2, NL2333 RA Leiden (Netherlands); Weiss, A. [Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, D-53121 Bonn (Germany); Bertoldi, F. [Argelander Institute for Astronomy, University of Bonn, Auf dem Hügel 71, D-53121 Bonn (Germany); Riechers, D., E-mail: ccarilli@aoc.nrao.edu [Cornell University, 220 Space Sciences Building, Ithaca, NY 14853 (United States)

2016-12-10

We present direct estimates of the mean sky brightness temperature in observing bands around 99 and 242 GHz due to line emission from distant galaxies. These values are calculated from the summed line emission observed in a blind, deep survey for spectral line emission from high redshift galaxies using ALMA (the ALMA spectral deep field observations “ASPECS” survey). In the 99 GHz band, the mean brightness will be dominated by rotational transitions of CO from intermediate and high redshift galaxies. In the 242 GHz band, the emission could be a combination of higher order CO lines, and possibly [C ii] 158 μ m line emission from very high redshift galaxies ( z  ∼ 6–7). The mean line surface brightness is a quantity that is relevant to measurements of spectral distortions of the cosmic microwave background, and as a potential tool for studying large-scale structures in the early universe using intensity mapping. While the cosmic volume and the number of detections are admittedly small, this pilot survey provides a direct measure of the mean line surface brightness, independent of conversion factors, excitation, or other galaxy formation model assumptions. The mean surface brightness in the 99 GHZ band is: T{sub B}  = 0.94 ± 0.09 μ K. In the 242 GHz band, the mean brightness is: T{sub B}  = 0.55 ± 0.033 μ K. These should be interpreted as lower limits on the average sky signal, since we only include lines detected individually in the blind survey, while in a low resolution intensity mapping experiment, there will also be the summed contribution from lower luminosity galaxies that cannot be detected individually in the current blind survey.

Dihydrochalcone Compounds Isolated from Crabapple Leaves Showed Anticancer Effects on Human Cancer Cell Lines

Directory of Open Access Journals (Sweden)

Xiaoxiao Qin

2015-11-01

Full Text Available Seven dihydrochalcone compounds were isolated from the leaves of Malus crabapples, cv. “Radiant”, and their chemical structures were elucidated by UV, IR, ESI-MS, 1H-NMR and 13C-NMR analyses. These compounds, which include trilobatin (A1, phloretin (A2, 3-hydroxyphloretin (A3, phloretin rutinoside (A4, phlorizin (A5, 6′′-O-coumaroyl-4′-O-glucopyranosylphloretin (A6, and 3′′′-methoxy-6′′-O-feruloy-4′-O-glucopyranosyl-phloretin (A7, all belong to the phloretin class and its derivatives. Compounds A6 and A7 are two new rare dihydrochalcone compounds. The results of a MTT cancer cell growth inhibition assay demonstrated that phloretin and these derivatives showed significant positive anticancer activities against several human cancer cell lines, including the A549 human lung cancer cell line, Bel 7402 liver cancer cell line, HepG2 human ileocecal cancer cell line, and HT-29 human colon cancer cell line. A7 had significant effects on all cancer cell lines, suggesting potential applications for phloretin and its derivatives. Adding a methoxyl group to phloretin dramatically increases phloretin’s anticancer activity.

The reverse transcription inhibitor abacavir shows anticancer activity in prostate cancer cell lines.

Directory of Open Access Journals (Sweden)

Francesca Carlini

Full Text Available BACKGROUND: Transposable Elements (TEs comprise nearly 45% of the entire genome and are part of sophisticated regulatory network systems that control developmental processes in normal and pathological conditions. The retroviral/retrotransposon gene machinery consists mainly of Long Interspersed Nuclear Elements (LINEs-1 and Human Endogenous Retroviruses (HERVs that code for their own endogenous reverse transcriptase (RT. Interestingly, RT is typically expressed at high levels in cancer cells. Recent studies report that RT inhibition by non-nucleoside reverse transcriptase inhibitors (NNRTIs induces growth arrest and cell differentiation in vitro and antagonizes growth of human tumors in animal model. In the present study we analyze the anticancer activity of Abacavir (ABC, a nucleoside reverse transcription inhibitor (NRTI, on PC3 and LNCaP prostate cancer cell lines. PRINCIPAL FINDINGS: ABC significantly reduces cell growth, migration and invasion processes, considerably slows S phase progression, induces senescence and cell death in prostate cancer cells. Consistent with these observations, microarray analysis on PC3 cells shows that ABC induces specific and dose-dependent changes in gene expression, involving multiple cellular pathways. Notably, by quantitative Real-Time PCR we found that LINE-1 ORF1 and ORF2 mRNA levels were significantly up-regulated by ABC treatment. CONCLUSIONS: Our results demonstrate the potential of ABC as anticancer agent able to induce antiproliferative activity and trigger senescence in prostate cancer cells. Noteworthy, we show that ABC elicits up-regulation of LINE-1 expression, suggesting the involvement of these elements in the observed cellular modifications.

Deep Percolation in Arid Piedmont Slopes: Multiple Lines of Evidence Show How Land Use Change and Ecohydrological Properties Affect Groundwater Recharge

Science.gov (United States)

Schreiner-McGraw, A.; Vivoni, E. R.; Browning, D. M.

2017-12-01

A critical hydrologic process in arid regions is the contribution of episodic streamflow in ephemeral channels to groundwater recharge. This process has traditionally been studied in channels that drain large watersheds (10s to 100s km2). In this study, we aim to characterize the provision of the ecosystem services of surface and groundwater supply in a first-order watershed (4.6 ha) in an arid piedmont slope of the Jornada Experimental Range (JER). We use an observational and modeling approach to estimate deep percolation. During a 6 year study period, we observed 428 mm of percolation (P) and 39 mm of runoff (Q); ratios of P to rainfall (R) of P/R = 0.27 and Q/R = 0.02. Utilizing an instrument network and site measurements, we determine that percolation occurs primarily inside channel reaches when these receive runoff from upland hillslopes and find that a monthly rainfall threshold of 62 mm is needed for significant percolation to be generated. In order to quantify the mechanisms leading to this threshold response, we develop a channel transmission loss module for the TIN-based Real-time Integrated Basin Simulator (tRIBS) and test the model thoroughly against the available observations over the study period. For these purposes, we make use of image classifications from Unmanned Aerial Vehicle flights, a ground-based phenocam, and species-level measurements to parameterize vegetation processes in the model. We then conduct an extensive set of sensitivity experiments to determine the relative roles of channel, soil, and vegetation properties on modifying the relation between monthly rainfall and percolation. Additionally, we test how the observed vegetation transitions in the JER over the last 150 years affect the deep percolation and runoff estimates. By quantifying mechanisms through which vegetation changes affect water resource provision, this work provides new insights on the ecohydrological controls on the water yield of arid piedmont slopes.

Large lattice relaxation deep levels in neutron-irradiated GaN

International Nuclear Information System (INIS)

Li, S.; Zhang, J.D.; Beling, C.D.; Wang, K.; Wang, R.X.; Gong, M.; Sarkar, C.K.

2005-01-01

Deep level transient spectroscopy (DLTS) and deep level optical spectroscopy (DLOS) measurements have been carried out in neutron-irradiated n-type hydride-vapor-phase-epitaxy-grown GaN. A defect center characterized by a DLTS line, labeled as N1, is observed at E C -E T =0.17 eV. Another line, labeled as N2, at E C -E T =0.23 eV, seems to be induced at the same rate as N1 under irradiation and may be identified with E1. Other defects native to wurtzite GaN such as the C and E2 lines appear to enhance under neutron irradiation. The DLOS results show that the defects N1 and N2 have large Frank-Condon shifts of 0.64 and 0.67 eV, respectively, and hence large lattice relaxations. The as-grown and neutron-irradiated samples all exhibit the persistent photoconductivity effect commonly seen in GaN that may be attributed to DX centers. The concentration of the DX centers increases significantly with neutron dosage and is helpful in sustaining sample conductivity at low temperatures, thus making possible DLTS measurements on N1 an N2 in the radiation-induced deep-donor defect compensated material which otherwise are prevented by carrier freeze-out

Studies of a full-scale mechanical prototype line for the ANTARES neutrino telescope and tests of a prototype instrument for deep-sea acoustic measurements

NARCIS (Netherlands)

Ageron, M.; Kooijman, P.

2007-01-01

A full-scale mechanical prototype line was deployed to a depth of 2500 m to test the leak tightness of the electronics containers and the pressure-resistant properties of an electromechanical cable under evaluation for use in the ANTARES deep-sea neutrino telescope. During a month-long immersion

Searching for the 3.5 keV Line in the Deep Fields with Chandra: The 10 Ms Observations

Science.gov (United States)

Cappelluti, Nico; Bulbul, Esra; Foster, Adam; Natarajan, Priyamvada; Urry, Megan C.; Bautz, Mark W.; Civano, Francesca; Miller, Eric; Smith, Randall K.

2018-02-01

We report a systematic search for an emission line around 3.5 keV in the spectrum of the cosmic X-ray background using a total of ∼10 Ms Chandra observations toward the COSMOS Legacy and Extended Chandra Deep Field South survey fields. We find marginal evidence of a feature at an energy of ∼3.51 keV with a significance of 2.5–3σ, depending on the choice of statistical treatment. The line intensity is best fit at (8.8 ± 2.9) × 10‑7 ph cm‑2 s‑1 when using a simple Δχ 2 or {10.2}-0.4+0.2× {10}-7 ph cm‑2 s‑1 when Markov chain Monte Carlo is used. Based on our knowledge of Chandra and the reported detection of the line by other instruments, an instrumental origin for the line remains unlikely. We cannot, however, rule out a statistical fluctuation, and in that case our results provide a 3σ upper limit at 1.85 × 10‑6 ph cm‑2 s‑1. We discuss the interpretation of this observed line in terms of the iron line background, S XVI charge exchange, as well as potentially being from sterile neutrino decay. We note that our detection is consistent with previous measurements of this line toward the Galactic center and can be modeled as the result of sterile neutrino decay from the Milky Way for the dark matter distribution modeled as a Navarro–Frenk–White profile. For this case, we estimate a mass m ν ∼ 7.01 keV and a mixing angle sin2(2θ) = (0.83–2.75) × 10‑10. These derived values are in agreement with independent estimates from galaxy clusters, the Galactic center, and M31.

Simulation of the influence of line edge roughness on the performance of deep ultraviolet wire grid polarizers

Science.gov (United States)

Siefke, Thomas; Rojas Hurtado, Carol B.; Dickmann, Johannes; Heusinger, Martin; Kroker, Stefanie

2017-06-01

Controlling the polarization of light is crucial in numerous applications such as spectroscopy, ellipsometry, photo lithography or industrial vision. Polarization control can be realized by wire grid polarizers (WGPs), which are large aspect ratio, zero order gratings. These elements provide an anisotropic transmittance depending on the polarization direction of the incident light. WGPs' high attractiveness originates from their large free aperture, while simultaneously being extremely thin. Furthermore, these elements can be easily integrated into other nano-optical devices. Recently, such elements were successfully developed for applications down to the deep ultra violet spectral range. However, at shorter wavelengths the influence of roughness of the structures poses a severe limitation on the feasible optical performance. To tackle this problem, we numerically simulated the impact of line edge roughness on the polarization properties of WPGs. Therefore, we generated edge position data of rough grating lines by means of the Thorsos method and calculated the resulting optical response by finite difference time domain method. With this procedure the influence of standard deviation, correlation length, Hurst exponents and wavelength was investigated. We find that for standard deviations of 2.5 nm and 5.0 nm the polarization contrast is reduced by a factor of 3 and 7, respectively. The polarization contrast shows a minimum for intermediate correlation lengths, while virtually no impact of the Hurst exponent is observed. This is explained by several mechanisms occurring for different ratios between the spatial frequency of the roughness and the frequency of incident light. Our theoretical findings correlate well with experimental results we retrieved with measured roughness parameters of fabricated elements.

Deep Learning-Based Banknote Fitness Classification Using the Reflection Images by a Visible-Light One-Dimensional Line Image Sensor

Directory of Open Access Journals (Sweden)

Tuyen Danh Pham

2018-02-01

Full Text Available In automatic paper currency sorting, fitness classification is a technique that assesses the quality of banknotes to determine whether a banknote is suitable for recirculation or should be replaced. Studies on using visible-light reflection images of banknotes for evaluating their usability have been reported. However, most of them were conducted under the assumption that the denomination and input direction of the banknote are predetermined. In other words, a pre-classification of the type of input banknote is required. To address this problem, we proposed a deep learning-based fitness-classification method that recognizes the fitness level of a banknote regardless of the denomination and input direction of the banknote to the system, using the reflection images of banknotes by visible-light one-dimensional line image sensor and a convolutional neural network (CNN. Experimental results on the banknote image databases of the Korean won (KRW and the Indian rupee (INR with three fitness levels, and the Unites States dollar (USD with two fitness levels, showed that our method gives better classification accuracy than other methods.

Deep Learning-Based Banknote Fitness Classification Using the Reflection Images by a Visible-Light One-Dimensional Line Image Sensor.

Science.gov (United States)

Pham, Tuyen Danh; Nguyen, Dat Tien; Kim, Wan; Park, Sung Ho; Park, Kang Ryoung

2018-02-06

In automatic paper currency sorting, fitness classification is a technique that assesses the quality of banknotes to determine whether a banknote is suitable for recirculation or should be replaced. Studies on using visible-light reflection images of banknotes for evaluating their usability have been reported. However, most of them were conducted under the assumption that the denomination and input direction of the banknote are predetermined. In other words, a pre-classification of the type of input banknote is required. To address this problem, we proposed a deep learning-based fitness-classification method that recognizes the fitness level of a banknote regardless of the denomination and input direction of the banknote to the system, using the reflection images of banknotes by visible-light one-dimensional line image sensor and a convolutional neural network (CNN). Experimental results on the banknote image databases of the Korean won (KRW) and the Indian rupee (INR) with three fitness levels, and the Unites States dollar (USD) with two fitness levels, showed that our method gives better classification accuracy than other methods.

Chicken lines divergently selected for antibody responses to sheep red blood cells show line-specific differences in sensitivity to immunomodulation by diet. Part I: Humoral parameters.

Science.gov (United States)

Adriaansen-Tennekes, R; de Vries Reilingh, G; Nieuwland, M G B; Parmentier, H K; Savelkoul, H F J

2009-09-01

Individual differences in nutrient sensitivity have been suggested to be related with differences in stress sensitivity. Here we used layer hens divergently selected for high and low specific antibody responses to SRBC (i.e., low line hens and high line hens), reflecting a genetically based differential immune competence. The parental line of these hens was randomly bred as the control line and was used as well. Recently, we showed that these selection lines differ in their stress reactivity; the low line birds show a higher hypothalamic-pituitary-adrenal (HPA) axis reactivity. To examine maternal effects and neonatal nutritional exposure on nutrient sensitivity, we studied 2 subsequent generations. This also created the opportunity to examine egg production in these birds. The 3 lines were fed 2 different nutritionally complete layer feeds for a period of 22 wk in the first generation. The second generation was fed from hatch with the experimental diets. At several time intervals, parameters reflecting humoral immunity were determined such as specific antibody to Newcastle disease and infectious bursal disease vaccines; levels of natural antibodies binding lipopolysaccharide, lipoteichoic acid, and keyhole limpet hemocyanin; and classical and alternative complement activity. The most pronounced dietary-induced effects were found in the low line birds of the first generation: specific antibody titers to Newcastle disease vaccine were significantly elevated by 1 of the 2 diets. In the second generation, significant differences were found in lipoteichoic acid natural antibodies of the control and low line hens. At the end of the observation period of egg parameters, a significant difference in egg weight was found in birds of the high line. Our results suggest that nutritional differences have immunomodulatory effects on innate and adaptive humoral immune parameters in birds with high HPA axis reactivity and affect egg production in birds with low HPA axis reactivity.

deepTools2: a next generation web server for deep-sequencing data analysis.

Science.gov (United States)

Ramírez, Fidel; Ryan, Devon P; Grüning, Björn; Bhardwaj, Vivek; Kilpert, Fabian; Richter, Andreas S; Heyne, Steffen; Dündar, Friederike; Manke, Thomas

2016-07-08

We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

Science.gov (United States)

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

A DEEP CHANDRA ACIS STUDY OF NGC 4151. III. THE LINE EMISSION AND SPECTRAL ANALYSIS OF THE IONIZATION CONE

International Nuclear Information System (INIS)

Wang, Junfeng; Fabbiano, Giuseppina; Elvis, Martin; Risaliti, Guido; Karovska, Margarita; Zezas, Andreas; Mundell, Carole G.; Dumas, Gaelle; Schinnerer, Eva

2011-01-01

This paper is the third in a series in which we present deep Chandra ACIS-S imaging spectroscopy of the Seyfert 1 galaxy NGC 4151, devoted to study its complex circumnuclear X-ray emission. Emission features in the soft X-ray spectrum of the bright extended emission (L 0.3-2 k eV ∼ 10 40 erg s –1 ) at r > 130 pc (2'') are consistent with blended brighter O VII, O VIII, and Ne IX lines seen in the Chandra HETGS and XMM-Newton RGS spectra below 2 keV. We construct emission line images of these features and find good morphological correlations with the narrow-line region clouds mapped in [O III] λ5007. Self-consistent photoionization models provide good descriptions of the spectra of the large-scale emission, as well as resolved structures, supporting the dominant role of nuclear photoionization, although displacement of optical and X-ray features implies a more complex medium. Collisionally ionized emission is estimated to be ∼ ☉ yr –1 at 130 pc and the kinematic power of the ionized outflow is 1.7 × 10 41 erg s –1 , approximately 0.3% of the bolometric luminosity of the active nucleus in NGC 4151.

Deep Seawater Intrusion Enhanced by Geothermal Through Deep Faults in Xinzhou Geothermal Field in Guangdong, China

Science.gov (United States)

Lu, G.; Ou, H.; Hu, B. X.; Wang, X.

2017-12-01

This study investigates abnormal sea water intrusion from deep depth, riding an inland-ward deep groundwater flow, which is enhanced by deep faults and geothermal processes. The study site Xinzhou geothermal field is 20 km from the coast line. It is in southern China's Guangdong coast, a part of China's long coastal geothermal belt. The geothermal water is salty, having fueled an speculation that it was ancient sea water retained. However, the perpetual "pumping" of the self-flowing outflow of geothermal waters might alter the deep underground flow to favor large-scale or long distant sea water intrusion. We studied geochemical characteristics of the geothermal water and found it as a mixture of the sea water with rain water or pore water, with no indication of dilution involved. And we conducted numerical studies of the buoyancy-driven geothermal flow in the deep ground and find that deep down in thousand meters there is favorable hydraulic gradient favoring inland-ward groundwater flow, allowing seawater intrude inland for an unusually long tens of kilometers in a granitic groundwater flow system. This work formed the first in understanding geo-environment for deep ground water flow.

Setup for in situ deep level transient spectroscopy of semiconductors during swift heavy ion irradiation.

Science.gov (United States)

Kumar, Sandeep; Kumar, Sugam; Katharria, Y S; Safvan, C P; Kanjilal, D

2008-05-01

A computerized system for in situ deep level characterization during irradiation in semiconductors has been set up and tested in the beam line for materials science studies of the 15 MV Pelletron accelerator at the Inter-University Accelerator Centre, New Delhi. This is a new facility for in situ irradiation-induced deep level studies, available in the beam line of an accelerator laboratory. It is based on the well-known deep level transient spectroscopy (DLTS) technique. High versatility for data manipulation is achieved through multifunction data acquisition card and LABVIEW. In situ DLTS studies of deep levels produced by impact of 100 MeV Si ions on Aun-Si(100) Schottky barrier diode are presented to illustrate performance of the automated DLTS facility in the beam line.

Many Teenagers Can't Distinguish Harassment Lines, Research Shows

Science.gov (United States)

Sparks, Sarah D.

2011-01-01

A national survey finds that, when it comes to sexual harassment in school, many students do not know where to draw the line. Based on the first nationally representative survey in a decade of students in grades 7-12, the study conducted by the American Association of University Women (AAUW), found that 48 percent of nearly 2,000 students surveyed…

Polymer Drilling Fluid with Micron-Grade Cenosphere for Deep Coal Seam

Directory of Open Access Journals (Sweden)

Peng Xu

2015-01-01

Full Text Available Traditional shallow coal seam uses clean water, solid-free system, and foam system as drilling fluid, while they are not suitable for deep coal seam drilling due to mismatching density, insufficient bearing capacity, and poor reservoir protection effect. According to the existing problems of drilling fluid, micron-grade cenosphere with high bearing capacity and ultralow true density is selected as density regulator; it, together with polymer “XC + CMC” and some other auxiliary agents, is jointly used to build micron-grade polymer drilling fluid with cenosphere which is suitable for deep coal seam. Basic performance test shows that the drilling fluid has good rheological property, low filtration loss, good density adjustability, shear thinning, and thixotropy; besides, drilling fluid flow is in line with the power law rheological model. Compared with traditional drilling fluid, dispersion stability basically does not change within 26 h; settlement stability evaluated with two methods only shows a small amount of change; permeability recovery rate evaluated with Qinshui Basin deep coal seam core exceeds 80%. Polymer drilling fluid with cenosphere provides a new thought to solve the problem of drilling fluid density and pressure for deep coal seam drilling and also effectively improves the performance of reservoir protection ability.

Deep Incremental Boosting

OpenAIRE

Mosca, Alan; Magoulas, George D

2017-01-01

This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member. We show a set of experiments that outlines some preliminary results on some common Deep Learning datasets and discuss the potential improvements Deep In...

Deep Learning For Sequential Pattern Recognition

OpenAIRE

Safari, Pooyan

2013-01-01

Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM) In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with ...

Associations between deepness of response and clinical outcomes among Japanese patients with metastatic colorectal cancer treated with second-line FOLFIRI plus cetuximab

Directory of Open Access Journals (Sweden)

Osumi H

2015-08-01

Full Text Available Hiroki Osumi, Satoshi Matsusaka, Mitsukuni Suenaga, Eiji Shinozaki, Nobuyuki Mizunuma Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan Background: In the FIRE-3 trial, overall survival (OS was significantly longer in patients treated with FOLFIRI plus cetuximab (C-mab than in those treated with FOLFIRI plus bevacizumab (Bev, but progression-free survival (PFS was not significantly different. This may be associated with the deepness of response (DpR in patients treated with FOLFIRI plus C-mab. We aimed to evaluate the relationship between clinical outcome and DpR in metastatic colorectal cancer (mCRC patients treated with second-line FOLFIRI plus C-mab.Methods: A total of 112 patients with histopathologically confirmed mCRC treated with second-line FOLFIRI in combination with C-mab (N=42 or Bev (N=70 were retrospectively enrolled between October 2008 and June 2013. The relationship between DpR and clinical outcome in patients treated with FOLFIRI plus C-mab or Bev was determined.Results: Forty-two patients treated with FOLFIRI plus C-mab had a mean DpR of 6.1% (interquartile range: -13.7%, 20.8% and a minimum DpR of -62.7%. On the other hand, 70 patients treated with FOLFIRI plus Bev had a mean DpR of 0% (interquartile range: -16%, 10% and a minimum DpR of -111%. DpR ≥30% was associated with significantly longer OS and PFS when compared with DpR ≤30% in patients given FOLFIRI plus C-mab. DpR (≥30% was independently associated with prolongation of OS and PFS. In patients treated with FOLFIRI plus C-mab, there was a moderate positive correlation between DpR and clinical outcomes (OS: r=0.51, P<0.001; PFS: r=0.54, P<0.001.Conclusion: FOLFIRI plus C-mab yielded a stronger correlation between DpR and clinical outcomes. These results indicate the potential of DpR as a new measure of efficacy in mCRC patients treated with second-line chemotherapy plus C-mab. Keywords: deepness of

Deep Belief Nets for Topic Modeling

DEFF Research Database (Denmark)

Maaløe, Lars; Arngren, Morten; Winther, Ole

2015-01-01

-formative. In this paper we describe large-scale content based collaborative filtering for digital publishing. To solve the digital publishing recommender problem we compare two approaches: latent Dirichlet allocation (LDA) and deep be-lief nets (DBN) that both find low-dimensional latent representations for documents....... Efficient retrieval can be carried out in the latent representation. We work both on public benchmarks and digital media content provided by Issuu, an on-line publishing platform. This article also comes with a newly developed deep belief nets toolbox for topic modeling tailored towards performance...

A transgenic mouse line for molecular genetic analysis of excitatory glutamatergic neurons

DEFF Research Database (Denmark)

Borgius, Lotta; Restrepo, C. Ernesto; Leao, Richardson N.

2010-01-01

Excitatory glutamatergic neurons are part of most of the neuronal circuits in the mammalian nervous system. We have used BAC-technology to generate a BAC-Vglut2::Cre mouse line where Cre expression is driven by the vesicular glutamate transporter 2 (Vglut2) promotor. This BAC-Vglut2::Cre mouse line...... showed specific expression of Cre in Vglut2 positive cells in the spinal cord with no ectopic expression in GABAergic or glycinergic neurons. This mouse line also showed specific Cre expression in Vglut2 positive structures in the brain such as thalamus, hypothalamus, superior colliculi, inferior...... colliculi and deep cerebellar nuclei together with nuclei in the midbrain and hindbrain. Cre-mediated recombination was restricted to Cre expressing cells in the spinal cord and brain and occurred as early as E 12.5. Known Vglut2 positive neurons showed normal electrophysiological properties in the BAC...

Installation of deep water sub-sea equipment

Energy Technology Data Exchange (ETDEWEB)

Pollack, Jack; Demian, Nabil [SBM-IMODCO Inc., Houston, TX (UNited States)

2004-07-01

Offshore oil developments are being planned in water depths exceeding 2000 m. Lowering and positioning large, heavy sub sea hardware, using conventional methods, presents new technical challenges in these ultra deep waters. In 3000 m a safe lift using conventional steel cables will require more capacity to support the cable self weight than the static payload. Adding dynamic loads caused by the motions of the surface vessel can quickly cause the safe capacity of the wire to be exceeded. Synthetic ropes now exist to greatly reduce the lowering line weight. The lower stiffness of these synthetic ropes aggravate the dynamic line tensions due to vessel motions and relatively little is known about the interaction of these ropes on the winches and sheaves required for pay-out and haul-in of these lines under dynamic load. Usage of conventional winches would damage the synthetic rope and risk the hardware being deployed. Reliable and economic installation systems that can operate from existing installation vessels are considered vital for ultra deep-water oil development. The paper describes a Deep Water Sub-Sea Hardware Deployment system consisting of a buoy with variable, pressure-balanced buoyancy, which is used to offset most of the payload weight as it is lowered. The buoyant capacity is controlled by air pumped into the tank from the surface vessel through a reinforced hose. The buoy and payload motion are isolated from the deployment line surface dynamics using a simple passive heave compensator mounted between the buoy and the bottom of the deployment rope. The system components, functionality and dynamic behavior are presented in the paper. (author)

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

Science.gov (United States)

Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

2018-02-01

DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg .

Conquered from the deep sea? A new deep-sea isopod species from the Antarctic shelf shows pattern of recent colonization.

Directory of Open Access Journals (Sweden)

Torben Riehl

Full Text Available The Amundsen Sea, Antarctica, is amongst the most rapidly changing environments of the world. Its benthic inhabitants are barely known and the BIOPEARL 2 project was one of the first to biologically explore this region. Collected during this expedition, Macrostylis roaldi sp. nov. is described as the first isopod discovered on the Amundsen-Sea shelf. Amongst many characteristic features, the most obvious characters unique for M. roaldi are the rather short pleotelson and short operculum as well as the trapezoid shape of the pleotelson in adult males. We used DNA barcodes (COI and additional mitochondrial markers (12S, 16S to reciprocally illuminate morphological results and nucleotide variability. In contrast to many other deep-sea isopods, this species is common and shows a wide distribution. Its range spreads from Pine Island Bay at inner shelf right to the shelf break and across 1,000 m bathymetrically. Its gene pool is homogenized across space and depth. This is indicative for a genetic bottleneck or a recent colonization history. Our results suggest further that migratory or dispersal capabilities of some species of brooding macrobenthos have been underestimated. This might be relevant for the species' potential to cope with effects of climate change. To determine where this species could have survived the last glacial period, alternative refuge possibilities are discussed.

pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

Science.gov (United States)

Zhou, Xie-Xuan; Zeng, Wen-Feng; Chi, Hao; Luo, Chunjie; Liu, Chao; Zhan, Jianfeng; He, Si-Min; Zhang, Zhifei

2017-12-05

In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network-based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electron-transfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with >0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q, or even I = L), which were very difficult to distinguish using traditional search engines.

Deep rooting conferred by DEEPER ROOTING 1 enhances rice yield in paddy fields.

Science.gov (United States)

Arai-Sanoh, Yumiko; Takai, Toshiyuki; Yoshinaga, Satoshi; Nakano, Hiroshi; Kojima, Mikiko; Sakakibara, Hitoshi; Kondo, Motohiko; Uga, Yusaku

2014-07-03

To clarify the effect of deep rooting on grain yield in rice (Oryza sativa L.) in an irrigated paddy field with or without fertilizer, we used the shallow-rooting IR64 and the deep-rooting Dro1-NIL (a near-isogenic line homozygous for the Kinandang Patong allele of DEEPER ROOTING 1 (DRO1) in the IR64 genetic background). Although total root length was similar in both lines, more roots were distributed within the lower soil layer of the paddy field in Dro1-NIL than in IR64, irrespective of fertilizer treatment. At maturity, Dro1-NIL showed approximately 10% higher grain yield than IR64, irrespective of fertilizer treatment. Higher grain yield of Dro1-NIL was mainly due to the increased 1000-kernel weight and increased percentage of ripened grains, which resulted in a higher harvest index. After heading, the uptake of nitrogen from soil and leaf nitrogen concentration were higher in Dro1-NIL than in IR64. At the mid-grain-filling stage, Dro1-NIL maintained higher cytokinin fluxes from roots to shoots than IR64. These results suggest that deep rooting by DRO1 enhances nitrogen uptake and cytokinin fluxes at late stages, resulting in better grain filling in Dro1-NIL in a paddy field in this study.

Discorhabdin P, a new enzyme inhibitor from a deep-water Caribbean sponge of the genus Batzella.

Science.gov (United States)

Gunasekera, S P; McCarthy, P J; Longley, R E; Pomponi, S A; Wright, A E; Lobkovsky, E; Clardy, J

1999-01-01

Discorhabdin P (1), a new discorhabdin analogue, has been isolated from a deep-water marine sponge of the genus Batzella. Discorhabdin P (1) inhibited the phosphatase activity of calcineurin and the peptidase activity of CPP32. It also showed in vitro cytotoxicity against P-388 and A-549 cell lines. The isolation and structure elucidation of discorhabdin P (1) are described.

Dro1, a major QTL involved in deep rooting of rice under upland field conditions.

Science.gov (United States)

Uga, Yusaku; Okuno, Kazutoshi; Yano, Masahiro

2011-05-01

Developing a deep root system is an important strategy for avoiding drought stress in rice. Using the 'basket' method, the ratio of deep rooting (RDR; the proportion of total roots that elongated through the basket bottom) was calculated to evaluate deep rooting. A new major quantitative trait locus (QTL) controlling RDR was detected on chromosome 9 by using 117 recombinant inbred lines (RILs) derived from a cross between the lowland cultivar IR64, with shallow rooting, and the upland cultivar Kinandang Patong (KP), with deep rooting. This QTL explained 66.6% of the total phenotypic variance in RDR in the RILs. A BC(2)F(3) line homozygous for the KP allele of the QTL had an RDR of 40.4%, compared with 2.6% for the homozygous IR64 allele. Fine mapping of this QTL was undertaken using eight BC(2)F(3) recombinant lines. The RDR QTL Dro1 (Deeper rooting 1) was mapped between the markers RM24393 and RM7424, which delimit a 608.4 kb interval in the reference cultivar Nipponbare. To clarify the influence of Dro1 in an upland field, the root distribution in different soil layers was quantified by means of core sampling. A line homozygous for the KP allele of Dro1 (Dro1-KP) and IR64 did not differ in root dry weight in the shallow soil layers (0-25 cm), but root dry weight of Dro1-KP in deep soil layers (25-50 cm) was significantly greater than that of IR64, suggesting that Dro1 plays a crucial role in increased deep rooting under upland field conditions.

Sesquiterpene-derived metabolites from the deep water marine sponge Poecillastra sollasi.

Science.gov (United States)

Killday, K B; Longley, R; McCarthy, P J; Pomponi, S A; Wright, A E; Neale, R F; Sills, M A

1993-04-01

Six sesquiterpene-derived compounds, 1-6, which we call sollasins a-f, have been isolated from a deep water specimen of the sponge Poecillastra sollasi. The structures were elucidated by comparison of spectral data to related metabolites and confirmed using spectroscopic methods. The compounds inhibit the growth of the pathogenic fungi Candida albicans and Cryptococcus neoformans and the P-388 and A-549 tumor cell lines. Compounds 3 and 4 show weak inhibition of binding of [125I] angiotensin II to rat aorta smooth muscle cell membranes.

Discorhabdins S, T, and U, new cytotoxic pyrroloiminoquinones from a deep-water Caribbean sponge of the genus Batzella.

Science.gov (United States)

Gunasekera, Sarath P; Zuleta, Ignacio A; Longley, Ross E; Wright, Amy E; Pomponi, Shirley A

2003-12-01

Discorhabdins S, T, and U (1-3), three new discorhabdin analogues, have been isolated from a deep-water marine sponge of the genus Batzella. These discorhabdin analogues showed in vitro cytotoxicity against PANC-1, P-388, and A-549 cell lines. The isolation and structure elucidation of discorhabdins S, T, and U are described.

Exploring frontiers of the deep biosphere through scientific ocean drilling

Science.gov (United States)

Inagaki, F.; D'Hondt, S.; Hinrichs, K. U.

2015-12-01

Since the first deep biosphere-dedicated Ocean Drilling Program (ODP) Leg 201 using the US drill ship JOIDES Resolution in 2002, scientific ocean drilling has offered unique opportunities to expand our knowledge of the nature and extent of the deep biosphere. The latest estimate of the global subseafloor microbial biomass is ~1029cells, accounting for 4 Gt of carbon and ~1% of the Earth's total living biomass. The subseafloor microbial communities are evolutionarily diverse and their metabolic rates are extraordinarily slow. Nevertheless, accumulating activity most likely plays a significant role in elemental cycles over geological time. In 2010, during Integrated Ocean Drilling Program (IODP) Expedition 329, the JOIDES Resolutionexplored the deep biosphere in the open-ocean South Pacific Gyre—the largest oligotrophic province on our planet. During Expedition 329, relatively high concentrations of dissolved oxygen and significantly low biomass of microbial populations were observed in the entire sediment column, indicating that (i) there is no limit to life in open-ocean sediment and (ii) a significant amount of oxygen reaches through the sediment to the upper oceanic crust. This "deep aerobic biosphere" inhabits the sediment throughout up to ~37 percent of the world's oceans. The remaining ~63 percent of the oceans is comprised of higher productivity areas that contain the "deep anaerobic biosphere". In 2012, during IODP Expedition 337, the Japanese drill ship Chikyu explored coal-bearing sediments down to 2,466 meters below the seafloor off the Shimokita Peninsula, Japan. Geochemical and microbiological analyses consistently showed the occurrence of methane-producing communities associated with the coal beds. Cell concentrations in deep sediments were notably lower than those expected from the global regression line, implying that the bottom of the deep biosphere is approached in these beds. Taxonomic composition of the deep coal-bearing communities profoundly

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.

Science.gov (United States)

Katzman, Jared L; Shaham, Uri; Cloninger, Alexander; Bates, Jonathan; Jiang, Tingting; Kluger, Yuval

2018-02-26

Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.

Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

Directory of Open Access Journals (Sweden)

Seung Seog Han

Full Text Available Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively, 125 images from Hallym University (C dataset, and 939 images from Seoul National University (D dataset. The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98, (82.7 / 96.7 / 0.95, (92.3 / 79.3 / 0.93, (87.7 / 69.3 / 0.82 for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01 higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

Science.gov (United States)

Han, Seung Seog; Park, Gyeong Hun; Lim, Woohyung; Kim, Myoung Shin; Na, Jung Im; Park, Ilwoo; Chang, Sung Eun

2018-01-01

Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

Coherence effects in deep inelastic scattering

International Nuclear Information System (INIS)

Andersson, B.; Gustafson, G.; Loennblad, L.; Pettersson, U.

1988-09-01

We present a framework for deep inelastic scattering, with bound state properties in accordance with a QCD force field acting like a vortex line in a colour superconducting vacuum, which implies some simple coherence effects. Within this scheme one may describe the results of present energies very well, but one obtains an appreciable depletion of gluon radiation in the HERA energy regime. (authors)

Motivational priming and processing interrupt: startle reflex modulation during shallow and deep processing of emotional words.

Science.gov (United States)

Herbert, Cornelia; Kissler, Johanna

2010-05-01

Valence-driven modulation of the startle reflex, that is larger eyeblinks during viewing of unpleasant pictures and inhibited blinks while viewing pleasant pictures, is well documented. The current study investigated, whether this motivational priming pattern also occurs during processing of unpleasant and pleasant words, and to what extent it is influenced by shallow vs. deep encoding of verbal stimuli. Emotional and neutral adjectives were presented for 5s, and the acoustically elicited startle eyeblink response was measured while subjects memorized the words by means of shallow or deep processing strategies. Results showed blink potentiation to unpleasant and blink inhibition to pleasant adjectives in subjects using shallow encoding strategies. In subjects using deep-encoding strategies, blinks were larger for pleasant than unpleasant or neutral adjectives. In line with this, free recall of pleasant words was also better in subjects who engaged in deep processing. The results suggest that motivational priming holds as long as processing is perceptual. However, during deep processing the startle reflex appears to represent a measure of "processing interrupt", facilitating blinks to those stimuli that are more deeply encoded. Copyright 2010 Elsevier B.V. All rights reserved.

DeepPy: Pythonic deep learning

DEFF Research Database (Denmark)

Larsen, Anders Boesen Lindbo

This technical report introduces DeepPy – a deep learning framework built on top of NumPy with GPU acceleration. DeepPy bridges the gap between highperformance neural networks and the ease of development from Python/NumPy. Users with a background in scientific computing in Python will quickly...... be able to understand and change the DeepPy codebase as it is mainly implemented using high-level NumPy primitives. Moreover, DeepPy supports complex network architectures by letting the user compose mathematical expressions as directed graphs. The latest version is available at http...

Moral differences in deep continuous palliative sedation and euthanasia.

Science.gov (United States)

Juth, Niklas; Lindblad, Anna; Lynöe, Niels; Sjöstrand, Manne; Helgesson, Gert

2013-06-01

In palliative care there is much debate about which end of life treatment strategies are legitimate and which are not. Some writers argue that there is an important moral dividing-line between palliative sedation and euthanasia, making the first acceptable and the latter not. We have questioned this. In a recent article, Lars Johan Materstvedt has argued that we are wrong on two accounts: first, that we fail to account properly for the moral difference between continuous deep palliative sedation at the end of life and euthanasia, and, second, that we fail to account properly for the difference between permanent loss of consciousness and death. Regarding the first objection, we argue that Materstvedt misses the point: we agree that there is a difference in terms of intentions between continuous deep palliative sedation and euthanasia, but we question whether this conceptual difference makes up for a moral difference. Materstvedt fails to show that it does. Regarding the second objection, we argue that if nothing else is at stake than the value of the patient's life, permanent unconsciousness and death are morally indifferent.

Rational solitons in deep nonlinear optical Bragg grating

NARCIS (Netherlands)

Alatas, H.; Iskandar, A.A.; Tjia, M.O.; Valkering, T.P.

2006-01-01

We have examined the rational solitons in the Generalized Coupled Mode model for a deep nonlinear Bragg grating. These solitons are the degenerate forms of the ordinary solitons and appear at the transition lines in the parameter plane. A simple formulation is presented for the investigation of the

Development of high-strength heavy-wall sour-service seamless line pipe for deep water by applying inline heat treatment

Energy Technology Data Exchange (ETDEWEB)

Arai, Y.; Kondo, K.; Hamada, M.; Hisamune, N.; Murao, N.; Murase, T.; Osako, H. [Sumitomo Metal Industries Ltd., Tokyo (Japan)

2004-07-01

This paper provided details of a new high-strength heavy-wall sour service seamless line pipe developed for use in deep water applications. Pig iron was processed in a blast furnace and refined. Molten steel was degassed to reduce impurities and poured into a continuous caster with a round mold. Billets were then heated in a walking-beam furnace and then pierced to form a hollow shell. The shell was then rolled to a specific thickness in a compact mandrel mill and rolled to a specified outer diameter by an extracting sizer. A heating furnace was used to improve the uniformity of the pipes. The heated pipes were then moved to a cooling zone, then rotated quickly while a high-pressured jet flow was injected inside the pipe at the same time as a slit laminar flow was applied to the outside of the pipe. Higher strength was achieved by using the high performance quenching device. It was noted that while pipes manufactured using the inline heat treatment process were able to achieve higher strengths, toughness was reduced. Metallurgical tests were conducted to improve the toughness value of the seamless pipe. Both the microstructure and the fracture surface of test specimens were examined using scanning electron microscopy. Results of the tests showed that lowering sulphur (S) and titanium (Ti) content improved the toughness properties of the pipes. It was concluded that control of microalloys is important to secure improved toughness for pipes manufactured using inline heat treatments. 5 tabs., 12 figs.

Towards deep learning with segregated dendrites.

Science.gov (United States)

Guerguiev, Jordan; Lillicrap, Timothy P; Richards, Blake A

2017-12-05

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

DEEP: a general computational framework for predicting enhancers

KAUST Repository

Kleftogiannis, Dimitrios A.

2014-11-05

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer\\'s properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

DEEP: a general computational framework for predicting enhancers

KAUST Repository

Kleftogiannis, Dimitrios A.; Kalnis, Panos; Bajic, Vladimir B.

2014-01-01

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Science.gov (United States)

Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

2018-04-01

Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

Spectral properties of X-ray selected narrow emission line galaxies

Science.gov (United States)

Romero-Colmenero, E.

1998-03-01

This thesis reports a study of the X-ray and optical properties of two samples of X-ray selected Narrow Emission Line Galaxies (NELGs), and their comparison with the properties of broad line Active Galactic Nuclei (AGN). One sample (18 NELGs) is drawn from the ROSAT International X-ray Optical Survey (RIXOS), the other (19 NELGs and 33 AGN) from the ROSAT UK Deep Survey. ROSAT multi-channel X-ray spectra have been extracted and fitted with power-law, bremsstrahlung and black body models for the brighter RIXOS sources. In most cases, power-law and bremsstrahlung models provide the best results. The average spectral energy index, alpha, of the RIXOS NELGs is 0.96 +/- 0.07, similar to that of AGN (alpha~1). For the fainter RIXOS NELGs, as well as for all the UK Deep Survey sources, counts in three spectral bands have been extracted and fitted with a power-law model, assuming the Galactic value for N_H. The brighter RIXOS sources demonstrated that the results obtained by these two different extraction and fitting procedures provide consistent results. Two average X-ray spectra, one for the NELGs and another for the AGN, were created from the UK Deep Survey sources. The power-law slope of the average NELG is alpha = 0.45 +/- 0.09, whilst that of the AGN is alpha = 0.96 +/- 0.03. ROSAT X-ray surveys have shown that the fractional surface density of NELGs increases with respect to AGN at faint fluxes (case for NELGs to be major contributors to the XRB at the fainter fluxes. The analysis of optical spectroscopy, obtained on La Palma and Hawaii, shows that NELGs form a very heterogeneous group, made up of a mixture of Seyfert 2, LINER and HII-region like galaxies. Seyfert 2 galaxies are found to possess in general the steepest X-ray slopes. Ways to explain this in the context of the unified model of AGN are discussed. The FWHM of some emission lines (Halpha, Hbeta, [NII]) in the NELGs appears to increase with steepening X-ray spectral slope. In the case of the Balmer lines

Sensory processing of deep tissue nociception in the rat spinal cord and thalamic ventrobasal complex.

Science.gov (United States)

Sikandar, Shafaq; West, Steven J; McMahon, Stephen B; Bennett, David L; Dickenson, Anthony H

2017-07-01

Sensory processing of deep somatic tissue constitutes an important component of the nociceptive system, yet associated central processing pathways remain poorly understood. Here, we provide a novel electrophysiological characterization and immunohistochemical analysis of neural activation in the lateral spinal nucleus (LSN). These neurons show evoked activity to deep, but not cutaneous, stimulation. The evoked responses of neurons in the LSN can be sensitized to somatosensory stimulation following intramuscular hypertonic saline, an acute model of muscle pain, suggesting this is an important spinal relay site for the processing of deep tissue nociceptive inputs. Neurons of the thalamic ventrobasal complex (VBC) mediate both cutaneous and deep tissue sensory processing, but in contrast to the lateral spinal nucleus our electrophysiological studies do not suggest the existence of a subgroup of cells that selectively process deep tissue inputs. The sensitization of polymodal and thermospecific VBC neurons to mechanical somatosensory stimulation following acute muscle stimulation with hypertonic saline suggests differential roles of thalamic subpopulations in mediating cutaneous and deep tissue nociception in pathological states. Overall, our studies at both the spinal (lateral spinal nucleus) and supraspinal (thalamic ventrobasal complex) levels suggest a convergence of cutaneous and deep somatosensory inputs onto spinothalamic pathways, which are unmasked by activation of muscle nociceptive afferents to produce consequent phenotypic alterations in spinal and thalamic neural coding of somatosensory stimulation. A better understanding of the sensory pathways involved in deep tissue nociception, as well as the degree of labeled line and convergent pathways for cutaneous and deep somatosensory inputs, is fundamental to developing targeted analgesic therapies for deep pain syndromes. © 2017 University College London. Physiological Reports published by Wiley Periodicals

Deep processing activates the medial temporal lobe in young but not in old adults.

Science.gov (United States)

Daselaar, Sander M; Veltman, Dick J; Rombouts, Serge A R B; Raaijmakers, Jeroen G W; Jonker, Cees

2003-11-01

Age-related impairments in episodic memory have been related to a deficiency in semantic processing, based on the finding that elderly adults typically benefit less than young adults from deep, semantic as opposed to shallow, nonsemantic processing of study items. In the present study, we tested the hypothesis that elderly adults are not able to perform certain cognitive operations under deep processing conditions. We further hypothesised that this inability does not involve regions commonly associated with lexical/semantic retrieval processes, but rather involves a dysfunction of the medial temporal lobe (MTL) memory system. To this end, we used functional MRI on rather extensive groups of young and elderly adults to compare brain activity patterns obtained during a deep (living/nonliving) and a shallow (uppercase/lowercase) classification task. Common activity in relation to semantic classification was observed in regions that have been previously related to semantic retrieval, including mainly left-lateralised activity in the inferior prefrontal, middle temporal, and middle frontal/anterior cingulate gyrus. Although the young adults showed more activity in some of these areas, the finding of mainly overlapping activation patterns during semantic classification supports the idea that lexical/semantic retrieval processes are still intact in elderly adults. This received further support by the finding that both groups showed similar behavioural performances as well on the deep and shallow classification tasks. Importantly, though, the young revealed significantly more activity than the elderly adults in the left anterior hippocampus during deep relative to shallow classification. This finding is in line with the idea that age-related impairments in episodic encoding are, at least partly, due to an under-recruitment of the medial temporal lobe memory system.

The variable cyclotron line of GX 301-2

Energy Technology Data Exchange (ETDEWEB)

Kreykenbohm, I.; Wilms, J.; Coburn, W.; Kuster, M.; Rothschild, R.E.; Heindl, W.A.; Kretschmar, P.; Staubert, R

2004-06-01

We present a 200 ksec observation of the High Mass X-ray Binary GX 301-2 taken in 2000 November with the Rossi X-ray Timing Explorer during the pre-periastron flare and the actual periastron passage of the neutron star. To model the spectrum we use a power law with the Fermi Dirac cutoff and a cyclotron line at higher energies plus either a reflection component or a heavily absorbed partial covering component. Although completely different, both models describe the data equally well. Phase resolved spectra show that the energy and the depth of the cyclotron resonant scattering feature vary strongly with pulse phase: It is deepest in the fall of the main pulse, the rise of the secondary pulse, and the pulse minimum in-between with {tau}{sub C}{approx}0.3. In the other phase bins the line is much less deep with {tau}{sub C}{approx}0.1. The energy of the line correlates strongly with its depth and varies by 25 % from 30.1 keV in the fall of the secondary pulse to 37.9 keV in the fall of the main pulse.

Deep learning for plasma tomography using the bolometer system at JET

Energy Technology Data Exchange (ETDEWEB)

Matos, Francisco A. [Instituto Superior Técnico (IST), University of Lisbon (Portugal); Ferreira, Diogo R., E-mail: diogo.ferreira@tecnico.ulisboa.pt [Instituto Superior Técnico (IST), University of Lisbon (Portugal); Carvalho, Pedro J. [Instituto de Plasmas e Fusão Nuclear (IPFN), IST, University of Lisbon (Portugal)

2017-01-15

Highlights: • Plasma tomography is able to reconstruct the plasma profile from radiation measurements along several lines of sight. • The reconstruction can be performed with neural networks, but previous work focused on learning a parametric model. • Deep learning can be used to reconstruct the full 2D plasma profile with the same resolution as existing tomograms. • We introduce a deep neural network to generate an image from 1D projection data based on a series of up-convolutions. • After training on JET data, the network provides accurate reconstructions with an average pixel error as low as 2%. - Abstract: Deep learning is having a profound impact in many fields, especially those that involve some form of image processing. Deep neural networks excel in turning an input image into a set of high-level features. On the other hand, tomography deals with the inverse problem of recreating an image from a number of projections. In plasma diagnostics, tomography aims at reconstructing the cross-section of the plasma from radiation measurements. This reconstruction can be computed with neural networks. However, previous attempts have focused on learning a parametric model of the plasma profile. In this work, we use a deep neural network to produce a full, pixel-by-pixel reconstruction of the plasma profile. For this purpose, we use the overview bolometer system at JET, and we introduce an up-convolutional network that has been trained and tested on a large set of sample tomograms. We show that this network is able to reproduce existing reconstructions with a high level of accuracy, as measured by several metrics.

Deep learning for plasma tomography using the bolometer system at JET

International Nuclear Information System (INIS)

Matos, Francisco A.; Ferreira, Diogo R.; Carvalho, Pedro J.

2017-01-01

Highlights: • Plasma tomography is able to reconstruct the plasma profile from radiation measurements along several lines of sight. • The reconstruction can be performed with neural networks, but previous work focused on learning a parametric model. • Deep learning can be used to reconstruct the full 2D plasma profile with the same resolution as existing tomograms. • We introduce a deep neural network to generate an image from 1D projection data based on a series of up-convolutions. • After training on JET data, the network provides accurate reconstructions with an average pixel error as low as 2%. - Abstract: Deep learning is having a profound impact in many fields, especially those that involve some form of image processing. Deep neural networks excel in turning an input image into a set of high-level features. On the other hand, tomography deals with the inverse problem of recreating an image from a number of projections. In plasma diagnostics, tomography aims at reconstructing the cross-section of the plasma from radiation measurements. This reconstruction can be computed with neural networks. However, previous attempts have focused on learning a parametric model of the plasma profile. In this work, we use a deep neural network to produce a full, pixel-by-pixel reconstruction of the plasma profile. For this purpose, we use the overview bolometer system at JET, and we introduce an up-convolutional network that has been trained and tested on a large set of sample tomograms. We show that this network is able to reproduce existing reconstructions with a high level of accuracy, as measured by several metrics.

A Deep Chandra ACIS Study of NGC 4151. III. The Line Emission and Spectral Analysis of the Ionization Cone

Science.gov (United States)

Wang, Junfeng; Fabbiano, Giuseppina; Elvis, Martin; Risaliti, Guido; Karovska, Margarita; Zezas, Andreas; Mundell, Carole G.; Dumas, Gaelle; Schinnerer, Eva

2011-11-01

This paper is the third in a series in which we present deep Chandra ACIS-S imaging spectroscopy of the Seyfert 1 galaxy NGC 4151, devoted to study its complex circumnuclear X-ray emission. Emission features in the soft X-ray spectrum of the bright extended emission (L 0.3-2 keV ~ 1040 erg s-1) at r > 130 pc (2'') are consistent with blended brighter O VII, O VIII, and Ne IX lines seen in the Chandra HETGS and XMM-Newton RGS spectra below 2 keV. We construct emission line images of these features and find good morphological correlations with the narrow-line region clouds mapped in [O III] λ5007. Self-consistent photoionization models provide good descriptions of the spectra of the large-scale emission, as well as resolved structures, supporting the dominant role of nuclear photoionization, although displacement of optical and X-ray features implies a more complex medium. Collisionally ionized emission is estimated to be lsim12% of the extended emission. Presence of both low- and high-ionization spectral components and extended emission in the X-ray image perpendicular to the bicone indicates leakage of nuclear ionization, likely filtered through warm absorbers, instead of being blocked by a continuous obscuring torus. The ratios of [O III]/soft X-ray flux are approximately constant (~15) for the 1.5 kpc radius spanned by these measurements, indicating similar relative contributions from the low- and high-ionization gas phases at different radial distances from the nucleus. If the [O III] and X-ray emission arise from a single photoionized medium, this further implies an outflow with a wind-like density profile. Using spatially resolved X-ray features, we estimate that the mass outflow rate in NGC 4151 is ~2 M ⊙ yr-1 at 130 pc and the kinematic power of the ionized outflow is 1.7 × 1041 erg s-1, approximately 0.3% of the bolometric luminosity of the active nucleus in NGC 4151.

Stability of deep features across CT scanners and field of view using a physical phantom

Science.gov (United States)

Paul, Rahul; Shafiq-ul-Hassan, Muhammad; Moros, Eduardo G.; Gillies, Robert J.; Hall, Lawrence O.; Goldgof, Dmitry B.

2018-02-01

Radiomics is the process of analyzing radiological images by extracting quantitative features for monitoring and diagnosis of various cancers. Analyzing images acquired from different medical centers is confounded by many choices in acquisition, reconstruction parameters and differences among device manufacturers. Consequently, scanning the same patient or phantom using various acquisition/reconstruction parameters as well as different scanners may result in different feature values. To further evaluate this issue, in this study, CT images from a physical radiomic phantom were used. Recent studies showed that some quantitative features were dependent on voxel size and that this dependency could be reduced or removed by the appropriate normalization factor. Deep features extracted from a convolutional neural network, may also provide additional features for image analysis. Using a transfer learning approach, we obtained deep features from three convolutional neural networks pre-trained on color camera images. An we examination of the dependency of deep features on image pixel size was done. We found that some deep features were pixel size dependent, and to remove this dependency we proposed two effective normalization approaches. For analyzing the effects of normalization, a threshold has been used based on the calculated standard deviation and average distance from a best fit horizontal line among the features' underlying pixel size before and after normalization. The inter and intra scanner dependency of deep features has also been evaluated.

Indolo[3,2-a]carbazoles from a deep-water sponge of the genus Asteropus.

Science.gov (United States)

Russell, Floyd; Harmody, Dedra; McCarthy, Peter J; Pomponi, Shirley A; Wright, Amy E

2013-10-25

Two new indolo[3,2-a]carbazoles (1, 2) were isolated from a deep-water collection of a sponge of the genus Asteropus. The structures of 1 and 2 were determined through the analysis of spectroscopic data including mass spectrometry and 2D-NMR. Compound 1 showed minimum inhibitory concentrations of 25 μg/mL against the fungal pathogen Candida albicans and 50 μg/mL against methicillin-resistant Staphylococcus aureus (MRSA). Compounds 1 and 2 showed no cytotoxicity against the PANC1 human pancreatic carcinoma and NCI/ADR-RES ovarian adenocarcinoma cell lines at our standard test concentration of 5 μg/mL.

Auxiliary Deep Generative Models

DEFF Research Database (Denmark)

Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

2016-01-01

Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave...... the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge...... faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST (0.96%), SVHN (16.61%) and NORB (9.40%) datasets....

Hot, deep origin of petroleum: deep basin evidence and application

Science.gov (United States)

Price, Leigh C.

1978-01-01

Use of the model of a hot deep origin of oil places rigid constraints on the migration and entrapment of crude oil. Specifically, oil originating from depth migrates vertically up faults and is emplaced in traps at shallower depths. Review of petroleum-producing basins worldwide shows oil occurrence in these basins conforms to the restraints of and therefore supports the hypothesis. Most of the world's oil is found in the very deepest sedimentary basins, and production over or adjacent to the deep basin is cut by or directly updip from faults dipping into the basin deep. Generally the greater the fault throw the greater the reserves. Fault-block highs next to deep sedimentary troughs are the best target areas by the present concept. Traps along major basin-forming faults are quite prospective. The structural style of a basin governs the distribution, types, and amounts of hydrocarbons expected and hence the exploration strategy. Production in delta depocenters (Niger) is in structures cut by or updip from major growth faults, and structures not associated with such faults are barren. Production in block fault basins is on horsts next to deep sedimentary troughs (Sirte, North Sea). In basins whose sediment thickness, structure and geologic history are known to a moderate degree, the main oil occurrences can be specifically predicted by analysis of fault systems and possible hydrocarbon migration routes. Use of the concept permits the identification of significant targets which have either been downgraded or ignored in the past, such as production in or just updip from thrust belts, stratigraphic traps over the deep basin associated with major faulting, production over the basin deep, and regional stratigraphic trapping updip from established production along major fault zones.

Deep-seated sarcomas of the penis

Directory of Open Access Journals (Sweden)

Alberto A. Antunes

2005-06-01

Full Text Available Mesenchymal neoplasias represent 5% of tumors affecting the penis. Due to the rarity of such tumors, there is no agreement concerning the best method for staging and managing these patients. Sarcomas of the penis can be classified as deep-seated if they derive from the structures forming the spongy body and the cavernous bodies. Superficial lesions are usually low-grade and show a small tendency towards distant metastasis. In contrast, deep-seated lesions usually show behavior that is more aggressive and have poorer prognosis. The authors report 3 cases of deep-seated primary sarcomas of the penis and review the literature on this rare and aggressive neoplasia.

Effects of fortified lysine on the amino acid profile and sensory qualities of deep-fried and dried noodles.

Science.gov (United States)

Polpuech, C; Chavasit, V; Srichakwal, P; Paniangvait, P

2011-08-01

Lysine fortification of wheat flour has been used toward reducing protein energy malnutrition in developing countries. The feasibility of fortifying instant noodles with lysine was evaluated based on sensory qualities and the residual lysine content. Fifty grams of deep-fried and dried instant noodles were fortified with 0.23 and 0.21 g lysine, respectively. The production temperatures used for deep-frying were 165-175 degrees C and for drying, 80-105 degrees C; these are the temperatures used in the industrial production of both kinds of noodles. Lysine fortification was then performed at the local factories using the commercial production lines and packaging for both types of instant noodles. Both fortified and unfortified deep-fried and dried instant noodles were stored at 50 degrees C under fluorescent light for 2 and 4 months, respectively. The fortified products were tested for residual lysine content and sensory qualities as compared with unfortified noodles. The results show fortified products from the tested processing temperatures were all accepted. After storage, significant losses of lysine were not found in both types of noodles analysed. The lysine-fortified noodles had amino acid scores of 102% and 122%, respectively. After 2 months, the sensory quality of fortified deep-fried noodles was still acceptable; however, the dried noodles turned to an unacceptable dark colour. This study shows that it is feasible to fortify deep-fried instant noodles with lysine, though lysine fortification exhibited an undesirable colour in the dried instant noodles after storage.

DeepSimulator: a deep simulator for Nanopore sequencing

KAUST Repository

Li, Yu

2017-12-23

Motivation: Oxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals. Results: Here we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection. Availability: The software can be accessed freely at: https://github.com/lykaust15/DeepSimulator.

Deep Learning in Neuroradiology.

Science.gov (United States)

Zaharchuk, G; Gong, E; Wintermark, M; Rubin, D; Langlotz, C P

2018-02-01

Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method. © 2018 by American Journal of Neuroradiology.

Analysis and seismic tests of composite shear walls with CFST columns and steel plate deep beams

Science.gov (United States)

Dong, Hongying; Cao, Wanlin; Wu, Haipeng; Zhang, Jianwei; Xu, Fangfang

2013-12-01

A composite shear wall concept based on concrete filled steel tube (CFST) columns and steel plate (SP) deep beams is proposed and examined in this study. The new wall is composed of three different energy dissipation elements: CFST columns; SP deep beams; and reinforced concrete (RC) strips. The RC strips are intended to allow the core structural elements — the CFST columns and SP deep beams — to work as a single structure to consume energy. Six specimens of different configurations were tested under cyclic loading. The resulting data are analyzed herein. In addition, numerical simulations of the stress and damage processes for each specimen were carried out, and simulations were completed for a range of location and span-height ratio variations for the SP beams. The simulations show good agreement with the test results. The core structure exhibits a ductile yielding mechanism characteristic of strong column-weak beam structures, hysteretic curves are plump and the composite shear wall exhibits several seismic defense lines. The deformation of the shear wall specimens with encased CFST column and SP deep beam design appears to be closer to that of entire shear walls. Establishing optimal design parameters for the configuration of SP deep beams is pivotal to the best seismic behavior of the wall. The new composite shear wall is therefore suitable for use in the seismic design of building structures.

A simple method for migrating narrow aperture, noisy seismic reflection data and application to Project INDEPTH (International Deep Profiling of Tibet and the Himalaya) deep seismic profiles

Science.gov (United States)

Alsdorf, Doug

1997-08-01

Migration of deep seismic data is often hindered by a narrow recording aperture (line length by record length) and a low signal-to-noise ratio. The severity of typical migration artifacts (e.g., lateral smearing of discontinuous reflections into synforms, "smiles") increases with travel time such that interpreters of deep seismic data have often substituted migrated line drawings for the actual sections. As part of Project INDEPTH (International Deep Profiling of Tibet and the Himalaya), a new migration method was developed to address both the noise and migration issues. The method works in the time-space domain and uses the simple, constant velocity, straight ray path to perform the migration. First, only amplitudes within a given range are retained for migration, thus avoiding high-amplitude noise bursts and low-amplitude background noise. Then, the local dip of a reflection is found by automatically fitting a straight line to the highest amplitudes within a small window (several time samples by several traces) and calculating the dip of the line using a constant velocity. Finally, using this dip, the method migrates a selected amplitude value. The dips, lateral positions, and depths of the migrated events compare very well with output from more conventional algorithms (e.g.,fk-Stolt, finite difference, etc.). The advantages of the new method include fewer artifacts, fast computer run times, low memory use and the ability to migrate long profiles and travel times (e.g., 50 s). The output of the method is a grid of migrated amplitudes (not wavelets) or dip values which is particularly effective for making small figures, such as those needed for publication. The principal disadvantage is the use of a constant migration velocity.

Extreme Longevity in Proteinaceous Deep-Sea Corals

Energy Technology Data Exchange (ETDEWEB)

Roark, E B; Guilderson, T P; Dunbar, R B; Fallon, S J; Mucciarone, D A

2009-02-09

Deep-sea corals are found on hard substrates on seamounts and continental margins world-wide at depths of 300 to {approx}3000 meters. Deep-sea coral communities are hotspots of deep ocean biomass and biodiversity, providing critical habitat for fish and invertebrates. Newly applied radiocarbon age date from the deep water proteinaceous corals Gerardia sp. and Leiopathes glaberrima show that radial growth rates are as low as 4 to 35 {micro}m yr{sup -1} and that individual colony longevities are on the order of thousands of years. The management and conservation of deep sea coral communities is challenged by their commercial harvest for the jewelry trade and damage caused by deep water fishing practices. In light of their unusual longevity, a better understanding of deep sea coral ecology and their interrelationships with associated benthic communities is needed to inform coherent international conservation strategies for these important deep-sea ecosystems.

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

Science.gov (United States)

Sadeghi, Zahra

2016-09-01

In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

GEANT4 simulation diagram showing the architecture of the ATLAS test line: the detectors are positioned to receive the beam from the SPS. A muon particle which enters the magnet and crosses all detectors is shown (blue line).

CERN Multimedia

2004-01-01

GEANT4 simulation diagram showing the architecture of the ATLAS test line: the detectors are positioned to receive the beam from the SPS. A muon particle which enters the magnet and crosses all detectors is shown (blue line).

A novel cell line derived from pleomorphic adenoma expresses MMP2, MMP9, TIMP1, TIMP2, and shows numeric chromosomal anomalies.

Directory of Open Access Journals (Sweden)

Aline Semblano Carreira Falcão

Full Text Available Pleomorphic adenoma is the most common salivary gland neoplasm, and it can be locally invasive, despite its slow growth. This study aimed to establish a novel cell line (AP-1 derived from a human pleomorphic adenoma sample to better understand local invasiveness of this tumor. AP-1 cell line was characterized by cell growth analysis, expression of epithelial and myoepithelial markers by immunofluorescence, electron microscopy, 3D cell culture assays, cytogenetic features and transcriptomic study. Expression of matrix metalloproteinases (MMPs and their tissue inhibitors (TIMPs was also analyzed by immunofluorescence and zymography. Furthermore, epithelial and myoepithelial markers, MMPs and TIMPs were studied in the tumor that originated the cell line. AP-1 cells showed neoplastic epithelial and myoepithelial markers, such as cytokeratins, vimentin, S100 protein and smooth-muscle actin. These molecules were also found in vivo, in the tumor that originated the cell line. MMPs and TIMPs were observed in vivo and in AP-1 cells. Growth curve showed that AP-1 exhibited a doubling time of 3.342 days. AP-1 cells grown inside Matrigel recapitulated tumor architecture. Different numerical and structural chromosomal anomalies were visualized in cytogenetic analysis. Transcriptomic analysis addressed expression of 7 target genes (VIM, TIMP2, MMP2, MMP9, TIMP1, ACTA2 e PLAG1. Results were compared to transcriptomic profile of non-neoplastic salivary gland cells (HSG. Only MMP9 was not expressed in both libraries, and VIM was expressed solely in AP-1 library. The major difference regarding gene expression level between AP-1 and HSG samples occurred for MMP2. This gene was 184 times more expressed in AP-1 cells. Our findings suggest that AP-1 cell line could be a useful model for further studies on pleomorphic adenoma biology.

Stable architectures for deep neural networks

Science.gov (United States)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

Deep groundwater circulation and associated methane leakage in the northern Canadian Rocky Mountains

International Nuclear Information System (INIS)

Grasby, S.E.; Ferguson, G.; Brady, A.; Sharp, C.; Dunfield, P.; McMechan, M.

2016-01-01

Concern over potential impact of shale gas development on shallow groundwater systems requires greater understanding of crustal scale fluid movement. We examined natural deeply circulating groundwater systems in northeastern British Columbia adjacent to a region of shale gas development, in order to elucidate origin of waters, depths of circulation, and controls on fluid flow. These systems are expressed as thermal springs that occur in the deformed sedimentary rocks of the Liard Basin. Stable isotope data from these springs show that they originate as meteoric water. Although there are no thermal anomalies in the region, outlet temperatures range from 30 to 56 °C, reflecting depth of circulation. Based on aqueous geothermometry and geothermal gradients, circulation depths up to 3.8 km are estimated, demonstrating connection of deep groundwater systems to the surface. Springs are also characterised by leakage of thermogenic gas from deep strata that is partly attenuated by methanotrophic microbial communities in the spring waters. Springs are restricted to anomalous structural features, cross cutting faults, and crests of fault-cored anticlines. On a regional scale they are aligned with the major tectonic features of the Liard Line and Larsen Fault. This suggests that while connection of surface to deep reservoirs is possible, it is rare and restricted to highly deformed geologic units that produce permeable pathways from depth through otherwise thick intervening shale units. Results allow a better understanding of potential for communication between deep shale gas units and shallow aquifer systems. - Highlights: • Deep groundwater systems were studied near active shale gas development. • Natural fracture systems allow circulation of meteoric water to depths of 3.8 km. • Deep circulation systems occur, but are rare in even highly deformed rocks.

Deep millimeter spectroscopy observations toward NGC 1068

Science.gov (United States)

Qiu, Jianjie; Wang, Junzhi; Shi, Yong; Zhang, Jiangshui; Fang, Min; Li, Fei

2018-05-01

Aims: We aim for a better understanding of gas properties in the circum-nuclear disk (CND) region of the nearby gas-rich Seyfert 2 galaxy NGC 1068. We focus on line identification and the basic physical parameters estimation of molecular gas in the CND region. Methods: We used the IRAM 30 m telescope to conduct deep millimeter spectroscopy observations toward the center of NGC 1068. Results: Thirty-two lines were detected in this galaxy, 15 lines of wich were detected for the first time. With a sensitivity better by about a factor of 4 than observations in the literature for this source at 3 mm band, we detected several weak lines for the first time in this source, such as lines from CH3CCH, CH3OCH3, and HC18O+. Column densities of these molecules were estimated based on line emissions. Some marginal detections in the literature, such as HN13C (1-0), were confirmed. CH3OCH3 was detected for the first time in external galaxies. Lines from several carbon chain molecules and shock-related molecules were also detected in this source. The reduced spectrum (FITS file) is only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/613/A3

Efficient collective swimming by harnessing vortices through deep reinforcement learning.

Science.gov (United States)

Verma, Siddhartha; Novati, Guido; Koumoutsakos, Petros

2018-06-05

Fish in schooling formations navigate complex flow fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behavior has been associated with evolutionary advantages including energy savings, yet the underlying physical mechanisms remain unknown. We show that fish can improve their sustained propulsive efficiency by placing themselves in appropriate locations in the wake of other swimmers and intercepting judiciously their shed vortices. This swimming strategy leads to collective energy savings and is revealed through a combination of high-fidelity flow simulations with a deep reinforcement learning (RL) algorithm. The RL algorithm relies on a policy defined by deep, recurrent neural nets, with long-short-term memory cells, that are essential for capturing the unsteadiness of the two-way interactions between the fish and the vortical flow field. Surprisingly, we find that swimming in-line with a leader is not associated with energetic benefits for the follower. Instead, "smart swimmer(s)" place themselves at off-center positions, with respect to the axis of the leader(s) and deform their body to synchronize with the momentum of the oncoming vortices, thus enhancing their swimming efficiency at no cost to the leader(s). The results confirm that fish may harvest energy deposited in vortices and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep RL can produce navigation algorithms for complex unsteady and vortical flow fields, with promising implications for energy savings in autonomous robotic swarms.

Deep Carbon Observatory investigates Carbon from Crust to Core: An Academic Record of the History of Deep Carbon Science

Science.gov (United States)

Mitton, S. A.

2017-12-01

Carbon plays an unparalleled role in our lives: as the element of life, as the basis of most of society's energy, as the backbone of most new materials, and as the central focus in efforts to understand Earth's variable and uncertain climate. Yet in spite of carbon's importance, scientists remain largely ignorant of the physical, chemical, and biological behavior of many of Earth's carbon-bearing systems. The Deep Carbon Observatory (DCO) is a global research program to transform our understanding of carbon in Earth. At its heart, DCO is a community of scientists, from biologists to physicists, geoscientists to chemists, and many others whose work crosses these disciplinary lines, forging a new, integrative field of deep carbon science. As a historian of science, I specialise in the history of planetary science and astronomy since 1900. This is directed toward understanding of the history of the steps on the road to discovering the internal dynamics of our planet. Within a framework that describes the historical background to the new field of Earth System Science, I present the first history of deep carbon science. This project will identifies the key discoveries of deep carbon science. It will assess the impact of new knowledge on geochemistry, geodynamics, and geobiology. The project will lead to publication, in book form in 2019, of an illuminating narrative that will highlight the engaging human stories of many remarkable scientists and natural philosophers from whom we have learned about the complexity of Earth's internal world. On this journey of discovery we will encounter not just the pioneering researchers of deep carbon science, but also their institutions, their instrumental inventiveness, and their passion for exploration. The book is organised thematically around the four communities of the Deep Carbon Observatory: Deep Life, Extreme Physics and Chemistry, Reservoirs and Fluxes, and Deep Energy. The presentation has a gallery and list of Deep Carbon

CHEERS Results on Mrk 573: A Study of Deep Chandra Observations

Science.gov (United States)

Paggi, Alessandro; Wang, Junfeng; Fabbiano, Giuseppina; Elvis, Martin; Karovska, Margarita

2012-09-01

We present results on Mrk 573 obtained as part of the CHandra survey of Extended Emission-line Regions in nearby Seyfert galaxies (CHEERS). Previous studies showed that this source features a biconical emission in the soft X-ray band closely related to the narrow-line region as mapped by the [O III] emission line and the radio emission, though on a smaller scale; we investigate the properties of soft X-ray emission from this source with new deep Chandra observations. Making use of the subpixel resolution of the Chandra/ACIS image and point-spread function deconvolution, we resolve and study substructures in each ionizing cone. The two cone spectra are fitted with a photoionization model, showing a mildly photoionized phase diffused over the bicone. Thermal collisional gas at about ~1.1 keV and ~0.8 keV appears to be located between the nucleus and the "knots" resolved in radio observations, and between the "arcs" resolved in the optical images, respectively; this can be interpreted in terms of shock interaction with the host galactic plane. The nucleus shows a significant flux decrease across the observations indicating variability of the active galactic nucleus (AGN), with the nuclear region featuring a higher ionization parameter with respect to the bicone region. The long exposure allows us to find extended emission up to ~7 kpc from the nucleus along the bicone axis. Significant emission is also detected in the direction perpendicular to the ionizing cones, disagreeing with the fully obscuring torus prescribed in the AGN unified model and suggesting instead the presence of a clumpy structure.

Deep Echo State Network (DeepESN): A Brief Survey

OpenAIRE

Gallicchio, Claudio; Micheli, Alessio

2017-01-01

The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced deep Echo State Network (deepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of deepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions ...

Simulation of deep one- and two-dimensional redshift surveys

International Nuclear Information System (INIS)

Park, Changbom; Gott, J.R. III

1991-01-01

We show that slice or pencil-beam redshift surveys of galaxies can be simulated in a box with non-equal sides. This method saves a lot of computer time and memory while providing essentially the same results as from whole-cube simulations. A 2457.6-h -1 Mpc-long rod (out to a redshift z = 0.58 in two opposite directions) is simulated using the standard biased Cold Dark Matter model as an example to mimic the recent deep pencil-beam surveys by Broadhurst et al. The structures (spikes) we see in these simulated samples occur when the narrow pencil-beam pierces walls, filaments and clusters appearing randomly along the line-of-sight. We have applied a statistical test for goodness of fit to a periodic lattice to the observations and the simulations. (author)

Effects of hypnosis and level of processing on repeated recall of line drawings.

Science.gov (United States)

McKelvie, S J; Pullara, M

1988-07-01

Moderately susceptible subjects (N = 30) initially judged 30 line drawings of objects for pleasantness (deep processing) and 30 line drawings for visual complexity (shallow processing), after which they were given two immediate recall tests. Following a 48-hr delay, subjects were allocated randomly to hypnosis, simulation, or neutral control conditions and were tested four more times. Subjects produced more correct and incorrect responses over the six trials and gave a higher number of correct responses for deep items than for shallow items. Over the last four trials, hypnosis had no general facilitative effect relative to the other two treatments, but the effect of depth was strongest for hypnotized subjects, who recalled more deep items than did the controls. Finally, both hypnotized and simulating subjects rated their recall as more involuntary and their experimental treatment as more helpful than did the controls. Caution is urged in the forensic use of hypnosis as a retrieval device.

Deep Learning and Its Applications in Biomedicine.

Science.gov (United States)

Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

2018-02-01

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

Repository tunnel construction in deep clay formations

International Nuclear Information System (INIS)

Clarke, B.G.; Mair, R.J.; Taylor, R.N.

1992-01-01

One of the objects of the Hades project at Mol, Belgium has been to evaluate the feasibility of construction of a deep repository in the Boom clay formation at depth of approximately 225 metres. The main objective of the present project was to analyse and interpret the detailed geotechnical measurements made around the Hades trial shaft and tunnel excavations and evaluate the safety of radioactive waste disposal in a repository facility in deep clay formations. Plasticity calculations and finite element analyses were used which gave results consistent with the in-situ measurements. It was shown that effective stress analysis could successfully predict the observed field behaviour. Correct modelling of the small-strain stiffness of the Boom clay was essential if reasonable predictions of the pore pressure response due to construction are to be made. The calculations undertaken indicated that, even in the long term, the pressures on the test drift tunnel lining are likely to be significantly lower than the overburden pressure. Larger long-term tunnel lining pressures are predicted for impermeable linings. A series of laboratory stress path tests was undertaken to determine the strength and stiffness characteristics of the Boom clay. The tests were conducted at appropriate effective stress levels on high-quality samples retrieved during construction of the test drift. The apparatus developed for the testing is described and the results discussed. The development of a self boring retracting pressure-meter is described. This novel in-situ testing device was specifically designed to determine from direct measurements the convergence/confinement curve relevant to tunnelling in clay formations. 44 refs., 60 figs., 3 tabs

Impact of open-ocean convection on particle fluxes and sediment dynamics in the deep margin of the Gulf of Lions

Directory of Open Access Journals (Sweden)

M. Stabholz

2013-02-01

Full Text Available The deep outer margin of the Gulf of Lions and the adjacent basin, in the western Mediterranean Sea, are regularly impacted by open-ocean convection, a major hydrodynamic event responsible for the ventilation of the deep water in the western Mediterranean Basin. However, the impact of open-ocean convection on the flux and transport of particulate matter remains poorly understood. The variability of water mass properties (i.e., temperature and salinity, currents, and particle fluxes were monitored between September 2007 and April 2009 at five instrumented mooring lines deployed between 2050 and 2350-m depth in the deepest continental margin and adjacent basin. Four of the lines followed a NW–SE transect, while the fifth one was located on a sediment wave field to the west. The results of the main, central line SC2350 ("LION" located at 42°02.5′ N, 4°41′ E, at 2350-m depth, show that open-ocean convection reached mid-water depth (≈ 1000-m depth during winter 2007–2008, and reached the seabed (≈ 2350-m depth during winter 2008–2009. Horizontal currents were unusually strong with speeds up to 39 cm s⁻¹ during winter 2008–2009. The measurements at all 5 different locations indicate that mid-depth and near-bottom currents and particle fluxes gave relatively consistent values of similar magnitude across the study area except during winter 2008–2009, when near-bottom fluxes abruptly increased by one to two orders of magnitude. Particulate organic carbon contents, which generally vary between 3 and 5%, were abnormally low (≤ 1% during winter 2008–2009 and approached those observed in surface sediments (≈ 0.6%. Turbidity profiles made in the region demonstrated the existence of a bottom nepheloid layer, several hundred meters thick, and related to the resuspension of bottom sediments. These observations support the view that open-ocean deep convection events in the Gulf of Lions can cause significant remobilization

Peripherally inserted central catheters and upper extremity deep vein thrombosis

International Nuclear Information System (INIS)

Ong, B.; Gibbs, H.; Catchpole, I.; Hetherington, R.; Harper, J.

2006-01-01

The purpose of the study was to determine the incidence and risk factors for venous thrombosis in patients with a peripherally inserted central catheter (PICC). A retrospective study of all upper extremity venous duplex scans was carried out in the Vascular Medicine department from year 2000 to 2002 inclusive. A chart review of positive scans was undertaken to identify possible thrombotic risk factors. Of 317 upper extremity venous duplex scans carried out, 115, or 32%, were positive for upper extremity deep vein thrombosis. Three main risk factors were identified - presence of a central line, malignancy and administration of chemotherapy. PICC were the most common central line present. Symptomatic thrombosis occurred in 7% of PICC inserted for chemotherapy compared with 1% of PICC inserted for other reasons. Ten per cent of the patients receiving chemotherapy through a PICC developed a thrombosis. The post-thrombotic syndrome was infrequent following upper extremity deep vein thrombosis. Patients receiving chemotherapy through a PICC are at increased risk of thrombosis. There may be a role for prophylactic low-dose anticoagulation in these high-risk patients

Secobatzellines A and B, two new enzyme inhibitors from a deep-water Caribbean sponge of the genus Batzella.

Science.gov (United States)

Gunasekera, S P; McCarthy, P J; Longley, R E; Pomponi, S A; Wright, A E

1999-08-01

Secobatzelline A (1), a new batzelline natural analogue, and secobatzelline B (2), a likely artifact formed during the isolation procedure, have been isolated from a deep-water marine sponge of the genus Batzella. Secobatzellines A and B inhibited the phosphatase activity of calcineurin, and secobatzelline A inhibited the peptidase activity of CPP32. Both compounds showed in vitro cytotoxicity against P-388 and A-549 cell lines. The isolation and structure elucidation of secobatzellines A (1) and B (2) are described.

Deep Unfolding for Topic Models.

Science.gov (United States)

Chien, Jen-Tzung; Lee, Chao-Hsi

2018-02-01

Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.

PEARS Emission Line Galaxies

Science.gov (United States)

Pirzkal, Nor; Rothberg, Barry; Ly, Chun; Rhoads, James E.; Malhotra, Sangeeta; Grogin, Norman A.; Dahlen, Tomas; Meurer, Gerhardt R.; Walsh, Jeremy; Hathi, Nimish P.;

Why & When Deep Learning Works: Looking Inside Deep Learnings

OpenAIRE

Ronen, Ronny

2017-01-01

The Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI) has been heavily supporting Machine Learning and Deep Learning research from its foundation in 2012. We have asked six leading ICRI-CI Deep Learning researchers to address the challenge of "Why & When Deep Learning works", with the goal of looking inside Deep Learning, providing insights on how deep networks function, and uncovering key observations on their expressiveness, limitations, and potential. The outp...

Deep Brain Stimulation for Parkinson's Disease

Science.gov (United States)

... about the BRAIN initiative, see www.nih.gov/science/brain . Show More Show Less Search Disorders SEARCH SEARCH Definition Treatment Prognosis Clinical Trials Organizations Publications Definition Deep ...

Seismic characteristics of central Brazil crust and upper mantle: A deep seismic refraction study

Science.gov (United States)

Soares, J.E.; Berrocal, J.; Fuck, R.A.; Mooney, W.D.; Ventura, D.B.R.

2006-01-01

A two-dimensional model of the Brazilian central crust and upper mantle was obtained from the traveltime interpretation of deep seismic refraction data from the Porangatu and Cavalcante lines, each approximately 300 km long. When the lines were deployed, they overlapped by 50 km, forming an E-W transect approximately 530 km long across the Tocantins Province and western Sa??o Francisco Craton. The Tocantins Province formed during the Neoproterozoic when the Sa??o Francisco, the Paranapanema, and the Amazon cratons collided, following the subduction of the former Goia??s ocean basin. Average crustal VP and VP/VS ratios, Moho topography, and lateral discontinuities within crustal layers suggest that the crust beneath central Brazil can be associated with major geological domains recognized at the surface. The Moho is an irregular interface, between 36 and 44 km deep, that shows evidences of first-order tectonic structures. The 8.05 and 8.23 km s-1 P wave velocities identify the upper mantle beneath the Porangatu and Cavalcante lines, respectively. The observed seismic features allow for the identification of (1) the crust has largely felsic composition in the studied region, (2) the absence of the mafic-ultramafic root beneath the Goia??s magmatic arc, and (3) block tectonics in the foreland fold-and-thrust belt of the northern Brasi??lia Belt during the Neoproterozoic. Seismic data also suggested that the Bouguer gravimetric discontinuities are mainly compensated by differences in mass distribution within the lithospheric mantle. Finally, the Goia??s-Tocantins seismic belt can be interpreted as a natural seismic alignment related to the Neoproterozoic mantle domain. Copyright 2006 by the American Geophysical Union.

A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

OpenAIRE

Wang, Haohan; Raj, Bhiksha

2015-01-01

This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On t...

The deep-sea hub of the ANTARES neutrino telescope

Energy Technology Data Exchange (ETDEWEB)

Anghinolfi, M. [INFN Sezione di Genova, Via Dodecaneso 33, I-16146 Genova (Italy); Calzas, A. [Centre de Physique des Particules de Marseille (CNRS/IN2P3), Universite de la Mediterranee, 13288 Marseille (France); Dinkespiler, B. [Centre de Physique des Particules de Marseille (CNRS/IN2P3), Universite de la Mediterranee, 13288 Marseille (France); Cuneo, S. [INFN Laboratori Nazionali del Sud, Via S. Sofia 44, I-95123 Catania (Italy); Favard, S. [Centre de Physique des Particules de Marseille (CNRS/IN2P3), Universite de la Mediterranee, 13288 Marseille (France); Hallewell, G. [Centre de Physique des Particules de Marseille (CNRS/IN2P3), Universite de la Mediterranee, 13288 Marseille (France)]. E-mail: gregh@cppm.in2p3.fr; Jaquet, M. [Centre de Physique des Particules de Marseille (CNRS/IN2P3), Universite de la Mediterranee, 13288 Marseille (France); Musumeci, M. [INFN Laboratori Nazionali del Sud, Via S. Sofia 44, I-95123 Catania (Italy); Papaleo, R. [INFN Laboratori Nazionali del Sud, Via S. Sofia 44, I-95123 Catania (Italy); Raia, G. [INFN Laboratori Nazionali del Sud, Via S. Sofia 44, I-95123 Catania (Italy); Valdy, P. [IFREMER - Institut francais de recherche pour l' exploitation de la mer, Centre de La Seyne, 83500 La Seyne sur mer (France); Vernin, P. [DSM-DAPNIA, CEA SACLAY, 91191 Gif sur Yvette Cedex (France)

2006-11-15

The ANTARES neutrino telescope, currently under construction at 2500 m depth off the French Mediterranean coast, will contain 12 detection lines, powered and read out through a deep-sea junction box (JB) hub. Electrical energy from the shore station is distributed through a transformer with multiple secondary windings and a plugboard with 16 deep sea-mateable electro-optic connectors. Connections are made to the JB outputs using manned or remotely operated submersible vehicles. The triply redundant power management and slow control system is based on two identical AC-powered systems, communicating with the shore through 160 Mb/s fibre G-links and a third battery-powered system using a slower link. We describe the power and slow control systems of the underwater hub.

The deep-sea hub of the ANTARES neutrino telescope

International Nuclear Information System (INIS)

Anghinolfi, M.; Calzas, A.; Dinkespiler, B.; Cuneo, S.; Favard, S.; Hallewell, G.; Jaquet, M.; Musumeci, M.; Papaleo, R.; Raia, G.; Valdy, P.; Vernin, P.

2006-01-01

The ANTARES neutrino telescope, currently under construction at 2500 m depth off the French Mediterranean coast, will contain 12 detection lines, powered and read out through a deep-sea junction box (JB) hub. Electrical energy from the shore station is distributed through a transformer with multiple secondary windings and a plugboard with 16 deep sea-mateable electro-optic connectors. Connections are made to the JB outputs using manned or remotely operated submersible vehicles. The triply redundant power management and slow control system is based on two identical AC-powered systems, communicating with the shore through 160 Mb/s fibre G-links and a third battery-powered system using a slower link. We describe the power and slow control systems of the underwater hub

Cytotoxic macrolides from a new species of the deep-water marine sponge Leiodermatium.

Science.gov (United States)

Sandler, Joel S; Colin, Patrick L; Kelly, Michelle; Fenical, William

2006-09-15

Chemical investigation of a new species of the deep-water marine sponge Leiodermatium, collected by manned submersible at a depth of 740 feet in Palau, resulted in the isolation of two cytotoxic macrolides, leiodolides A (1) and B (2). The leiodolides represent the first members of a new class of 19-membered ring macrolides, incorporating several unique functional groups including a conjugated oxazole ring, a bromine substituent, and an alpha-hydroxy-alpha-methyl carboxylic acid side-chain terminus. The structures of these new metabolites were established by spectroscopic analysis, chemical modification, and degradation. The relative and absolute stereochemistries at most chiral centers were assigned on detailed interpretation of spectroscopic data, coupled with chemical degradation and application of the modified Mosher ester method. Leiodolide A showed significant cytotoxicity (average GI(50) = 2.0 microM) in the National Cancer Institute's 60 cell line panel with enhanced activity against HL-60 leukemia and OVCAR-3 ovarian cancer cell lines.

Encoding-related brain activity during deep processing of verbal materials: a PET study.

Science.gov (United States)

Fujii, Toshikatsu; Okuda, Jiro; Tsukiura, Takashi; Ohtake, Hiroya; Suzuki, Maki; Kawashima, Ryuta; Itoh, Masatoshi; Fukuda, Hiroshi; Yamadori, Atsushi

2002-12-01

The recent advent of neuroimaging techniques provides an opportunity to examine brain regions related to a specific memory process such as episodic memory encoding. There is, however, a possibility that areas active during an assumed episodic memory encoding task, compared with a control task, involve not only areas directly relevant to episodic memory encoding processes but also areas associated with other cognitive processes for on-line information. We used positron emission tomography (PET) to differentiate these two kinds of regions. Normal volunteers were engaged in deep (semantic) or shallow (phonological) processing of new or repeated words during PET. Results showed that deep processing, compared with shallow processing, resulted in significantly better recognition performance and that this effect was associated with activation of various brain areas. Further analyses revealed that there were regions directly relevant to episodic memory encoding in the anterior part of the parahippocampal gyrus, inferior frontal gyrus, supramarginal gyrus, anterior cingulate gyrus, and medial frontal lobe in the left hemisphere. Our results demonstrated that several regions, including the medial temporal lobe, play a role in episodic memory encoding.

Coupled interactions of organized deep convection over the tropical western pacific

Energy Technology Data Exchange (ETDEWEB)

Hong, X.; Raman, S. [North Carolina State Univ., Raleigh, NC (United States)

1996-04-01

The relationship between sea surface temperature (SST) and deep convection is complex. In general, deep convection occurs more frequently and with more intensity as SSTs become higher. This theory assumes that the atmospheric stability is sufficiently reduced to allow the onset of moist convection. However, the amount and intensity of convection observed tends to decrease with increasing SST because very warm SSTs. A reason for such decrease is the enhancements to surface fluxes of heat and moisture out of the ocean surface because of the vertical overturning associated with deep convection. Early studies used the radiative-convective models of the atmosphere to examine the role of the convective exchange of heat and moisture in maintaining the vertical temperature profile. In this paper we use a Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to simulate a squall line over a tropical ocean global atmosphere/coupled ocean atmosphere response experiment (TOGA/COARE) area and to investigate how the ocean cooling mechanisms associated with organized deep convection act to limit tropical SSTs.

Project installation the large equipment in line system in Brazil. Gas export line valve P-40 FPSO-MLS. Field Marlim Sul, Campos Basin, Brazil; Operacao de instalacao do maior equipamento no sistema in line ja realizado no Brasil. Valvula do gasoduto P-40 X FPSO-MLS. Campo de Marlim Sul, Bacia de Campos, Brasil

Energy Technology Data Exchange (ETDEWEB)

Marques, Marcos Antonio Rodrigues; Fernandes, Paulo Tavares [PETROBRAS, Campos dos Goytacases, RJ (Brazil). Exploracao e Producao

2005-07-01

This work will approach the current level of development of the installation of connected underwater equipment to flexible lines in the underwater engineering operations in Campos' Basin. The project will show studies, analysis and simulations (through software developed by PETROBRAS) about the installation of the largest equipment laid in the 'in-line' system (connected to flexible lines) in Brazil - and one of the largest of the world: the ESDV (Emergency Shut Down Valve) of the gas pipeline P-40 x FPSO-MLS, in the South Marlim field, in Campos' Basin. This ESDV, of about 18.000 kg, 4 m height and 6,5 m length, has the purpose of assuring the safety conditions on the facilities, interrupting the gas flow exported for P-40 in case of emergency situations. Its installation opened a new alternative in releasing underwater equipment, using the ships that install the flexible lines. This operation occurred in June, 2004, and required the use of a second vessel for support and monitoring of the ESDV laying. The ESDV was installed at 400 m from FPSO-MLS, in a water depth of 1.137 m. This method shall be used broadly by the company in the implantation of the new units of Campos' Basin, and the upcoming studies must consider the gradual increase of the water depth in the new projects. This work will focus the technological development in this area, and one of its purposes is to foresee the future difficulties that can appear in the implantation of the production systems in deep and ultra-deep waters. (author)

Dynamic design method for deep hard rock tunnels and its application

Directory of Open Access Journals (Sweden)

Xia-Ting Feng

2016-08-01

Full Text Available Numerous deep underground projects have been designed and constructed in China, which are beyond the current specifications in terms of scale and construction difficulty. The severe failure problems induced by high in situ stress, such as rockburst, spalling, damage of deep surrounding rocks, and time-dependent damage, were observed during construction of these projects. To address these problems, the dynamic design method for deep hard rock tunnels is proposed based on the disintegration process of surrounding rocks using associated dynamic control theories and technologies. Seven steps are basically employed: (i determination of design objective, (ii characteristics of site, rock mass and project, and identification of constraint conditions, (iii selection or development of global design strategy, (iv determination of modeling method and software, (v preliminary design, (vi comprehensive integrated method and dynamic feedback analysis, and (vii final design. This dynamic method was applied to the construction of the headrace tunnels at Jinping II hydropower station. The key technical issues encountered during the construction of deep hard rock tunnels, such as in situ stress distribution along the tunnels, mechanical properties and constitutive model of deep hard rocks, determination of mechanical parameters of surrounding rocks, stability evaluation of surrounding rocks, and optimization design of rock support and lining, have been adequately addressed. The proposed method and its application can provide guidance for deep underground projects characterized with similar geological conditions.

The genetic basis of pectoralis major myopathies in modern broiler chicken lines.

Science.gov (United States)

Bailey, Richard A; Watson, Kellie A; Bilgili, S F; Avendano, Santiago

2015-12-01

This is the first report providing estimates of the genetic basis of breast muscle myopathies (BMM) and their relationship with growth and yield in broiler chickens. In addition, this paper addresses the hypothesis that genetic selection for increase breast yield has contributed to the onset of BMM. Data were analyzed from ongoing recording of BMM within the Aviagen breeding program. This study focused on three BMM: deep pectoral myopathy (DPM; binary trait), white striping (WS; 4 categories) and wooden breast (WB; 3 categories). Data from two purebred commercial broiler lines (A and B) were utilized providing greater than 40,000 meat quality records per line. The difference in selection history between these two lines has resulted in contrasting breast yield (BY): 29% for Line A and 21% for Line B. Data were analyzed to estimate genetic parameters using a multivariate animal model including six traits: body weight (BW), processing body weight (PW), BY, DPM, WB, and WS, in addition to the appropriate fixed effects and permanent environmental effect of the dam. Results indicate similar patterns of heritability and genetic correlations for the two lines. Heritabilities (h2) of BW, PW and BY ranged from 0.271-0.418; for DPM and WB h2white striping of breast muscle and more than 90% of the variance of the incidence of wooden breast and deep pectoral myopathy in broiler chickens. © The Author 2015. Published by Oxford University Press on behalf of Poultry Science Association.

A replacement LH2 recirculation line before installation in Discovery

Science.gov (United States)

1999-01-01

A spare four-inch diameter LH2 recirculation line (shown in photo) will be used to replace a damaged LH2 line in the orbiter Discovery. The line recirculates hydrogen from the Shuttle main engines back to the external tank during prelaunch engine conditioning. Workers noted a dent in the line during routine aft compartment inspections Tuesday, Dec. 7. The dent measures 12 inches long and about =-inch deep. Managers expect the replacement work to take about 3 days, followed by system retests and final aft compartment close-outs. Preliminary assessments reflect a launch date of Space Shuttle Discovery on mission STS-103 no earlier than Dec. 16. STS-103 is the third servicing mission for the Hubble Space Telescope.

[Deep vein thrombosis prophylaxis.

Science.gov (United States)

Sandoval-Chagoya, Gloria Alejandra; Laniado-Laborín, Rafael

2013-01-01

Background: despite the proven effectiveness of preventive therapy for deep vein thrombosis, a significant proportion of patients at risk for thromboembolism do not receive prophylaxis during hospitalization. Our objective was to determine the adherence to thrombosis prophylaxis guidelines in a general hospital as a quality control strategy. Methods: a random audit of clinical charts was conducted at the Tijuana General Hospital, Baja California, Mexico, to determine the degree of adherence to deep vein thrombosis prophylaxis guidelines. The instrument used was the Caprini's checklist for thrombosis risk assessment in adult patients. Results: the sample included 300 patient charts; 182 (60.7 %) were surgical patients and 118 were medical patients. Forty six patients (15.3 %) received deep vein thrombosis pharmacologic prophylaxis; 27.1 % of medical patients received deep vein thrombosis prophylaxis versus 8.3 % of surgical patients (p < 0.0001). Conclusions: our results show that adherence to DVT prophylaxis at our hospital is extremely low. Only 15.3 % of our patients at risk received treatment, and even patients with very high risk received treatment in less than 25 % of the cases. We have implemented strategies to increase compliance with clinical guidelines.

Contemporary deep recurrent learning for recognition

Science.gov (United States)

Iftekharuddin, K. M.; Alam, M.; Vidyaratne, L.

2017-05-01

Large-scale feed-forward neural networks have seen intense application in many computer vision problems. However, these networks can get hefty and computationally intensive with increasing complexity of the task. Our work, for the first time in literature, introduces a Cellular Simultaneous Recurrent Network (CSRN) based hierarchical neural network for object detection. CSRN has shown to be more effective to solving complex tasks such as maze traversal and image processing when compared to generic feed forward networks. While deep neural networks (DNN) have exhibited excellent performance in object detection and recognition, such hierarchical structure has largely been absent in neural networks with recurrency. Further, our work introduces deep hierarchy in SRN for object recognition. The simultaneous recurrency results in an unfolding effect of the SRN through time, potentially enabling the design of an arbitrarily deep network. This paper shows experiments using face, facial expression and character recognition tasks using novel deep recurrent model and compares recognition performance with that of generic deep feed forward model. Finally, we demonstrate the flexibility of incorporating our proposed deep SRN based recognition framework in a humanoid robotic platform called NAO.

DEWS (DEep White matter hyperintensity Segmentation framework): A fully automated pipeline for detecting small deep white matter hyperintensities in migraineurs.

Science.gov (United States)

Park, Bo-Yong; Lee, Mi Ji; Lee, Seung-Hak; Cha, Jihoon; Chung, Chin-Sang; Kim, Sung Tae; Park, Hyunjin

2018-01-01

Migraineurs show an increased load of white matter hyperintensities (WMHs) and more rapid deep WMH progression. Previous methods for WMH segmentation have limited efficacy to detect small deep WMHs. We developed a new fully automated detection pipeline, DEWS (DEep White matter hyperintensity Segmentation framework), for small and superficially-located deep WMHs. A total of 148 non-elderly subjects with migraine were included in this study. The pipeline consists of three components: 1) white matter (WM) extraction, 2) WMH detection, and 3) false positive reduction. In WM extraction, we adjusted the WM mask to re-assign misclassified WMHs back to WM using many sequential low-level image processing steps. In WMH detection, the potential WMH clusters were detected using an intensity based threshold and region growing approach. For false positive reduction, the detected WMH clusters were classified into final WMHs and non-WMHs using the random forest (RF) classifier. Size, texture, and multi-scale deep features were used to train the RF classifier. DEWS successfully detected small deep WMHs with a high positive predictive value (PPV) of 0.98 and true positive rate (TPR) of 0.70 in the training and test sets. Similar performance of PPV (0.96) and TPR (0.68) was attained in the validation set. DEWS showed a superior performance in comparison with other methods. Our proposed pipeline is freely available online to help the research community in quantifying deep WMHs in non-elderly adults.

Vertical Line Nodes in the Superconducting Gap Structure of Sr_{2}RuO_{4}

Directory of Open Access Journals (Sweden)

E. Hassinger

2017-03-01

Full Text Available There is strong experimental evidence that the superconductor Sr_{2}RuO_{4} has a chiral p-wave order parameter. This symmetry does not require that the associated gap has nodes, yet specific heat, ultrasound, and thermal conductivity measurements indicate the presence of nodes in the superconducting gap structure of Sr_{2}RuO_{4}. Theoretical scenarios have been proposed to account for the existence of deep minima or accidental nodes (minima tuned to zero or below by material parameters within a p-wave state. Other scenarios propose chiral d-wave and f-wave states, with horizontal and vertical line nodes, respectively. To elucidate the nodal structure of the gap, it is essential to know whether the lines of nodes (or minima are vertical (parallel to the tetragonal c axis or horizontal (perpendicular to the c axis. Here, we report thermal conductivity measurements on single crystals of Sr_{2}RuO_{4} down to 50 mK for currents parallel and perpendicular to the c axis. We find that there is substantial quasiparticle transport in the T=0 limit for both current directions. A magnetic field H immediately excites quasiparticles with velocities both in the basal plane and in the c direction. Our data down to T_{c}/30 and down to H_{c2}/100 show no evidence that the nodes are in fact deep minima. Relative to the normal state, the thermal conductivity of the superconducting state is found to be very similar for the two current directions, from H=0 to H=H_{c2}. These findings show that the gap structure of Sr_{2}RuO_{4} consists of vertical line nodes. This rules out a chiral d-wave state. Given that the c-axis dispersion (warping of the Fermi surface in Sr_{2}RuO_{4} varies strongly from sheet to sheet, the small a-c anisotropy suggests that the line nodes are present on all three sheets of the Fermi surface. If imposed by symmetry, vertical line nodes would be inconsistent with a p-wave order parameter for Sr_{2}RuO_{4}. To reconcile the gap structure

Slow shock characteristics as a function of distance from the X-line in the magnetotail

International Nuclear Information System (INIS)

Lee, L.C.; Lin, Y.; Shi, Y.; Tsurutani, B.T.

1989-01-01

Both particle and MHD simulations are performed to study the characteristics of slow shocks in the magnetotail. The particle simulations indicate that switch-off shocks exhibit large amplitude rotational wave trains, while magnetotail slow shocks with an intermediate Mach number M An c congruent 0.98 do not display such rotational wave trains. The MHD simulations show that the spontaneous reconnection process in the near-earth plasma sheet leads to the formation of a pair of slow shocks tailward of the reconnection line (X-line). The properties of slow shocks are found to vary as a function of the distance from X-line due to the fomation of plasmoid. Slow shocks in most regions of the magnetotail are found to be non-switch-off shocks with M An <0.98. The present results are used to discuss the lack of large amplitude rotational wave trains at slow shocks in the deep magnetotail. copyright American Geophysical Union 1989

Prevalence and evolution of low frequency HIV drug resistance mutations detected by ultra deep sequencing in patients experiencing first line antiretroviral therapy failure.

Science.gov (United States)

Vandenhende, Marie-Anne; Bellecave, Pantxika; Recordon-Pinson, Patricia; Reigadas, Sandrine; Bidet, Yannick; Bruyand, Mathias; Bonnet, Fabrice; Lazaro, Estibaliz; Neau, Didier; Fleury, Hervé; Dabis, François; Morlat, Philippe; Masquelier, Bernard

2014-01-01

Clinical relevance of low-frequency HIV-1 variants carrying drug resistance associated mutations (DRMs) is still unclear. We aimed to study the prevalence of low-frequency DRMs, detected by Ultra-Deep Sequencing (UDS) before antiretroviral therapy (ART) and at virological failure (VF), in HIV-1 infected patients experiencing VF on first-line ART. Twenty-nine ART-naive patients followed up in the ANRS-CO3 Aquitaine Cohort, having initiated ART between 2000 and 2009 and experiencing VF (2 plasma viral loads (VL) >500 copies/ml or one VL >1000 copies/ml) were included. Reverse transcriptase and protease DRMs were identified using Sanger sequencing (SS) and UDS at baseline (before ART initiation) and VF. Additional low-frequency variants with PI-, NNRTI- and NRTI-DRMs were found by UDS at baseline and VF, significantly increasing the number of detected DRMs by 1.35 fold (plow-frequency DRMs modified ARV susceptibility predictions to the prescribed treatment for 1 patient at baseline, in whom low-frequency DRM was found at high frequency at VF, and 6 patients at VF. DRMs found at VF were rarely detected as low-frequency DRMs prior to treatment. The rare low-frequency NNRTI- and NRTI-DRMs detected at baseline that correlated with the prescribed treatment were most often found at high-frequency at VF. Low frequency DRMs detected before ART initiation and at VF in patients experiencing VF on first-line ART can increase the overall burden of resistance to PI, NRTI and NNRTI.

Using the Properties of Broad Absorption Line Quasars to Illuminate Quasar Structure

Science.gov (United States)

Yong, Suk Yee; King, Anthea L.; Webster, Rachel L.; Bate, Nicholas F.; O'Dowd, Matthew J.; Labrie, Kathleen

2018-06-01

A key to understanding quasar unification paradigms is the emission properties of broad absorption line quasars (BALQs). The fact that only a small fraction of quasar spectra exhibit deep absorption troughs blueward of the broad permitted emission lines provides a crucial clue to the structure of quasar emitting regions. To learn whether it is possible to discriminate between the BALQ and non-BALQ populations given the observed spectral properties of a quasar, we employ two approaches: one based on statistical methods and the other supervised machine learning classification, applied to quasar samples from the Sloan Digital Sky Survey. The features explored include continuum and emission line properties, in particular the absolute magnitude, redshift, spectral index, line width, asymmetry, strength, and relative velocity offsets of high-ionisation C IV λ1549 and low-ionisation Mg II λ2798 lines. We consider a complete population of quasars, and assume that the statistical distributions of properties represent all angles where the quasar is viewed without obscuration. The distributions of the BALQ and non-BALQ sample properties show few significant differences. None of the observed continuum and emission line features are capable of differentiating between the two samples. Most published narrow disk-wind models are inconsistent with these observations, and an alternative disk-wind model is proposed. The key feature of the proposed model is a disk-wind filling a wide opening angle with multiple radial streams of dense clumps.

The deep sea Acoustic Detection system AMADEUS

International Nuclear Information System (INIS)

Naumann, Christopher Lindsay

2008-01-01

As a part of the ANTARES neutrino telescope, the AMADEUS (ANTARES Modules for Acoustic Detection Under the Sea) system is an array of acoustical sensors designed to investigate the possibilities of acoustic detection of ultra-high energy neutrinos in the deep sea. The complete system will comprise a total of 36 acoustic sensors in six clusters on two of the ANTARES detector lines. With an inter-sensor spacing of about one metre inside the clusters and between 15 and 340 metres between the different clusters, it will cover a wide range of distances as will as provide a considerable lever arm for point source triangulation. Three of these clusters have already been deployed in 2007 and have been in operation since, currently yielding around 2GB of acoustic data per day. The remaining three clusters are scheduled to be deployed in May 2008 together with the final ANTARES detector line. Apart from proving the feasibility of operating an acoustic detection system in the deep sea, the main aim of this project is an in-depth survey of both the acoustic properties of the sea water and the acoustic background present at the detector site. It will also serve as a platform for the development and refinement of triggering, filtering and reconstruction algorithms for acoustic particle detection. In this presentation, a description of the acoustic sensor and read-out system is given, together with examples for the reconstruction and evaluation of the acoustic data.

Resistivity tomography using line electrode; Sendenryugen wo tsukatta hiteiko tomography

Energy Technology Data Exchange (ETDEWEB)

Sugimoto, Y [Dia Consultants Company, Tokyo (Japan)

1996-10-01

Resistivity tomography (RT) using line electrode was studied. Although line electrode is available even for RT, in casing line electrode, only one kind of electrode data is obtained. The calculation method of potential and sensitivity distributions based on line electrode is not yet established. Since various data in various measurement arrangements are required for analysis of RT, the new measurement method was devised which measures resistivities while successively changing the tip depth of line electrode. Until now, although potential has been calculated under the assumption that outflow current per unit length of line electrode is uniform, this assumption is incorrect. The new potential distribution calculation method was thus proposed. Sensitivity distribution calculation for inverse analysis is also described. RT using line electrode could precisely obtain deep information which couldn`t be obtained only by measurement along the surface measuring line. Although RT is poorer in accuracy than the previous point electrode method, it will be probably improved by 3-electrode arrangement. RT is also useful in the case difficult to apply point electrode method. 3 refs., 10 figs.

Deep iCrawl: An Intelligent Vision-Based Deep Web Crawler

OpenAIRE

R.Anita; V.Ganga Bharani; N.Nityanandam; Pradeep Kumar Sahoo

2011-01-01

The explosive growth of World Wide Web has posed a challenging problem in extracting relevant data. Traditional web crawlers focus only on the surface web while the deep web keeps expanding behind the scene. Deep web pages are created dynamically as a result of queries posed to specific web databases. The structure of the deep web pages makes it impossible for traditional web crawlers to access deep web contents. This paper, Deep iCrawl, gives a novel and vision-based app...

Tuft (caveolated) cells in two human colon carcinoma cell lines.

Science.gov (United States)

Barkla, D H; Whitehead, R H; Foster, H; Tutton, P J

1988-09-01

The presence of an unusual cell type in two human colon carcinoma cell lines is reported. The cells show the same morphology as "tuft" (caveolated) cells present in normal gastrointestinal epithelium. Tuft cells were seen in cell line LIM 1863 growing in vitro and in human colon carcinoma cell line LIM 2210 growing as subcutaneous solid tumour xenografts in nude mice. Characteristic morphologic features of tuft cells included a wide base, narrow apex and a tuft of long microvilli projecting from the apical surface. The microvilli are attached by a core of long microfilaments passing deep into the apical cytoplasm. Between the microvilli are parallel arrays of vesicles (caveoli) containing flocculent material. Two different but not mutually exclusive explanations for the presence of tuft cells are proposed. The first explanation is that tuft cells came from the resected tumour and have survived by mitotic division during subsequent passages. The second explanation suggests that tuft cells are the progeny of undifferentiated tumour cells. Descriptions of tuft cells in colon carcinomas are uncommon and possible reasons for this are presented. The morphology of tuft cells is consistent with that of a highly differentiated cell specialised for absorption, and these new models provide an opportunity to further investigate the structure and function of tuft cells.

Dinosaur incubation periods directly determined from growth-line counts in embryonic teeth show reptilian-grade development.

Science.gov (United States)

Erickson, Gregory M; Zelenitsky, Darla K; Kay, David Ian; Norell, Mark A

2017-01-17

Birds stand out from other egg-laying amniotes by producing relatively small numbers of large eggs with very short incubation periods (average 11-85 d). This aspect promotes high survivorship by limiting exposure to predation and environmental perturbation, allows for larger more fit young, and facilitates rapid attainment of adult size. Birds are living dinosaurs; their rapid development has been considered to reflect the primitive dinosaurian condition. Here, nonavian dinosaurian incubation periods in both small and large ornithischian taxa are empirically determined through growth-line counts in embryonic teeth. Our results show unexpectedly slow incubation (2.8 and 5.8 mo) like those of outgroup reptiles. Developmental and physiological constraints would have rendered tooth formation and incubation inherently slow in other dinosaur lineages and basal birds. The capacity to determine incubation periods in extinct egg-laying amniotes has implications for dinosaurian embryology, life history strategies, and survivorship across the Cretaceous-Paleogene mass extinction event.

The final optical identification content of the Einstein deep x-ray field in Pavo.

Science.gov (United States)

Danziger, J. I.; Gilmozzi, R.

1997-07-01

The optical identification of all sources revealed in the final analysis of the Einstein deep field observations in Pavo has been completed to the viable limits accessible to spectroscopy. This work combined with previously published data results in the identification of 16 AGN's with the real possibility of 3 further such identifications, while a further 2 probably are spurious. Another AGN is identified in an IPC exposure just outside the boundary of the four HRI exposures. One elliptical galaxy (or cluster) and one dMe star complete the tally. In a log N-log S plot the point represented by these 16-19 AGN's falls precisely on the extension of the line defined by the EMSS data, and somewhat below the line defined by the more recent deep field ROSAT data. It extends to fainter sensitivities than the previously published work from the Einstein observations of the same field. It is consistent with the more recently published data for Pavo obtained with ROSAT even though this latter reaches a slightly fainter sensitivity. This identification work therefore sets a firm lower limit to the AGN content of the X-ray identifications in Pavo. By virtue of having selected in this survey intrinsically fainter-than-average AGN's it has been possible to show, by combination with data for higher luminosity quasars, that a correlation exists between the luminosities and (B-V) colours extending over a luminosity range of 6 magnitudes. This sequence coincides with the sequence obtained by plotting data for all AGN's in the same redshift range taken from the Veron and Veron catalogue. It is argued that the magnitude of this effect cannot be explained by the translation of various strong emission lines through the band-passes of the relevant filters. It may be explained by the influence of host galaxies.

VERY STRONG EMISSION-LINE GALAXIES IN THE WFC3 INFRARED SPECTROSCOPIC PARALLEL SURVEY AND IMPLICATIONS FOR HIGH-REDSHIFT GALAXIES

Energy Technology Data Exchange (ETDEWEB)

Atek, H.; Colbert, J.; Shim, H. [Spitzer Science Center, Caltech, Pasadena, CA 91125 (United States); Siana, B.; Bridge, C. [Department of Astronomy, Caltech, Pasadena, CA 91125 (United States); Scarlata, C. [Department of Astronomy, University of Minnesota-Twin Cities, Minneapolis, MN 55455 (United States); Malkan, M.; Ross, N. R. [Department of Physics and Astronomy, University of California, Los Angeles, CA (United States); McCarthy, P.; Dressler, A.; Hathi, N. P. [Observatories of the Carnegie Institution for Science, Pasadena, CA 91101 (United States); Teplitz, H. [Infrared Processing and Analysis Center, Caltech, Pasadena, CA 91125 (United States); Henry, A.; Martin, C. [Department of Physics, University of California, Santa Barbara, CA 93106 (United States); Bunker, A. J. [Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH (United Kingdom); Fosbury, R. A. E. [Space Telescope-European Coordinating Facility, Garching bei Muenchen (Germany)

2011-12-20

The WFC3 Infrared Spectroscopic Parallel Survey uses the Hubble Space Telescope (HST) infrared grism capabilities to obtain slitless spectra of thousands of galaxies over a wide redshift range including the peak of star formation history of the universe. We select a population of very strong emission-line galaxies with rest-frame equivalent widths (EWs) higher than 200 A. A total of 176 objects are found over the redshift range 0.35 < z < 2.3 in the 180 arcmin{sup 2} area that we have analyzed so far. This population consists of young and low-mass starbursts with high specific star formation rates (sSFR). After spectroscopic follow-up of one of these galaxies with Keck/Low Resolution Imaging Spectrometer, we report the detection at z = 0.7 of an extremely metal-poor galaxy with 12 + log(O/H) =7.47 {+-} 0.11. After estimating the active galactic nucleus fraction in the sample, we show that the high-EW galaxies have higher sSFR than normal star-forming galaxies at any redshift. We find that the nebular emission lines can substantially affect the total broadband flux density with a median brightening of 0.3 mag, with some examples of line contamination producing brightening of up to 1 mag. We show that the presence of strong emission lines in low-z galaxies can mimic the color-selection criteria used in the z {approx} 8 dropout surveys. In order to effectively remove low-redshift interlopers, deep optical imaging is needed, at least 1 mag deeper than the bands in which the objects are detected. Without deep optical data, most of the interlopers cannot be ruled out in the wide shallow HST imaging surveys. Finally, we empirically demonstrate that strong nebular lines can lead to an overestimation of the mass and the age of galaxies derived from fitting of their spectral energy distribution (SED). Without removing emission lines, the age and the stellar mass estimates are overestimated by a factor of 2 on average and up to a factor of 10 for the high-EW galaxies

Chicken lines divergently selected for antibody responses to sheep red blood cells show line-specific differences in sensitivity to immunomodulation by diet. Part I: Humoral parameters

NARCIS (Netherlands)

Adriaansen-Tennekes, R.; Vries Reilingh, de G.; Nieuwland, M.G.B.; Parmentier, H.K.; Savelkoul, H.F.J.

2009-01-01

Individual differences in nutrient sensitivity have been suggested to be related with differences in stress sensitivity. Here we used layer hens divergently selected for high and low specific antibody responses to SRBC (i.e., low line hens and high line hens), reflecting a genetically based

Water transparency measurements in the deep Ionian Sea

CERN Document Server

Anassontzis, E G; Belias, A; Fotiou, A; Grammatikakis, G; Kontogiannis, H; Koske, P; Koutsoukos, S; Lykoussis, V; Markopoulos, E; Psallidas, A; Resvanis, L K; Siotis, I; Stavrakakis, S; Stavropoulos, G; Zhukov, V A

2010-01-01

A long optical base line spectrophotometer designed to measure light transmission in deep sea waters is described. The variable optical path length allows measurements without the need for absolute or external calibration. The spectrophotometer uses eight groups of uncollimated light sources emitting in the range 370–530 nm and was deployed at various depths at two locations in the Ionian Sea that are candidate sites for a future underwater neutrino telescope. Light transmission spectra at the two locations are presented and compared.

Real-time expert systems and deep knowledge models

International Nuclear Information System (INIS)

Felkel, L.

1990-01-01

To guide operators in normal and disturbed plant conditions expert systems are feasible. These, however, must be on-line and real-time systems. The knowledge contained in such a system cannot be represented in a 'classical' role-based manner. The paper describes problems and solutions with regard to process reference models as these are important in order to provide so-called deep-knowledge for the operators. The system described is being implemented and is meant to support both diagnosis and prediction

Off-line testing of multifunctional surfaces for metal forming applications

DEFF Research Database (Denmark)

Godi, A.; Grønbæk, J.; De Chiffre, L.

2015-01-01

In this paper, Bending-Under-Tension, an off-line test method simulating deep-drawing, is chosen for investigating the effectiveness of multifunctional (MUFU) surfaces in metal forming operations. Four different MUFU surfaces, characterized by a plateau bearing area and grooves for lubricant...... retention, are manufactured, together with two polished references. During the tests, surface texture is the only variable. The results show how MUFU surfaces perform better than the polished references, which produce severe galling, while MUFU surfaces with low bearing area display no clear evidence...... of galling. Metal-to-metal contact occurs anyway, but the strip material is pulverized and deposited onto the tool instead of cold-welding to it. The pockets create a discontinuity on the texture hindering pick-up propagation....

Full-fledged temporal processing: bridging the gap between deep linguistic processing and temporal extraction

Directory of Open Access Journals (Sweden)

Francisco Costa

2013-07-01

Full Text Available The full-fledged processing of temporal information presents specific challenges. These difficulties largely stem from the fact that the temporal meaning conveyed by grammatical means interacts with many extra-linguistic factors (world knowledge, causality, calendar systems, reasoning. This article proposes a novel approach to this problem, based on a hybrid strategy that explores the complementarity of the symbolic and probabilistic methods. A specialized temporal extraction system is combined with a deep linguistic processing grammar. The temporal extraction system extracts eventualities, times and dates mentioned in text, and also temporal relations between them, in line with the tasks of the recent TempEval challenges; and uses machine learning techniques to draw from different sources of information (grammatical and extra-grammatical even if it is not explicitly known how these combine to produce the final temporal meaning being expressed. In turn, the deep computational grammar delivers richer truth-conditional meaning representations of input sentences, which include a principled representation of temporal information, on which higher level tasks, including reasoning, can be based. These deep semantic representations are extended and improved according to the output of the aforementioned temporal extraction module. The prototype implemented shows performance results that increase the quality of the temporal meaning representations and are better than the performance of each of the two components in isolation.

Isolation, Characterization, and Bioactivity Evaluation of 3-((6-Methylpyrazin-2-ylmethyl-1H-indole, a New Alkaloid from a Deep-Sea-Derived Actinomycete Serinicoccus profundi sp. nov.

Directory of Open Access Journals (Sweden)

Yong-Hong Liu

2012-12-01

Full Text Available One new alkaloid, 3-((6-methylpyrazin-2-ylmethyl-1H-indole (1 was obtained from the deep-sea actinomycete Serinicoccus profundi sp. nov., along with five known compounds (2–6. Their structures were determined on the basis of detailed analysis of the 1D and 2D NMR as well as MS data. The new indole alkaloid displayed weak antimicrobial activity against Staphylococcus aureus ATCC 25923 with an MIC value of 96 μg/mL. It showed no cytotoxicity on a normal human liver cell line (BEL7402 and a human liver tumor cell line (HL-7702.

3-loop heavy flavor corrections to DIS with two massive fermion lines

International Nuclear Information System (INIS)

Ablinger, J.; Schneider, C.; Klein, S.

2011-06-01

We report on recent results obtained for the massive operator matrix elements which contribute to the massive Wilson coefficients in deep-inelastic scattering for Q 2 >> m i 2 in case of sub-processes with two fermion lines and different mass assignment. (orig.)

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

Science.gov (United States)

Wachinger, Christian; Reuter, Martin; Klein, Tassilo

2018-04-15

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Re-visiting the notion of Deep Incarnation in light of 1 Corinthians 15:28 and emergence theory

OpenAIRE

Bentley, Wessel

2016-01-01

Niels Hendrik Gregersen's 'Deep Incarnation' is opening up possibilities for engagement between science and theology. Recent discoveries, like that of Homo naledi, raise questions about how inclusive a Christian doctrine of Incarnation is. Is Jesus only God incarnate for Homo sapien sapiens, or is the incarnation inclusive of preceding hominid species as well? Does the incarnation stretch beyond the hominid line? This chapter engages Gregersen's understanding of Deep Incarnation in light of 1...

THE SPECTRAL ENERGY DISTRIBUTIONS OF z ∼ 8 GALAXIES FROM THE IRAC ULTRA DEEP FIELDS: EMISSION LINES, STELLAR MASSES, AND SPECIFIC STAR FORMATION RATES AT 650 MYR

Energy Technology Data Exchange (ETDEWEB)

Labbé, I.; Bouwens, R. J.; Franx, M. [Leiden Observatory, Leiden University, NL-2300 RA Leiden (Netherlands); Oesch, P. A.; Illingworth, G. D.; Magee, D.; González, V. [UCO/Lick Observatory, University of California, Santa Cruz, CA 95064 (United States); Carollo, C. M. [Institute for Astronomy, ETH Zurich, 8092 Zurich (Switzerland); Trenti, M. [Kavli Institute for Cosmology and Institute of Astronomy, University of Cambridge, Cambridge (United Kingdom); Van Dokkum, P. G. [Department of Astronomy, Yale University, New Haven, CT 06520 (United States); Stiavelli, M. [Space Telescope Science Institute, Baltimore, MD 21218 (United States)

2013-11-10

Using new ultradeep Spitzer/InfraRed Array Camera (IRAC) photometry from the IRAC Ultra Deep Field program, we investigate the stellar populations of a sample of 63 Y-dropout galaxy candidates at z ∼ 8, only 650 Myr after the big bang. The sources are selected from HST/ACS+WFC3/IR data over the Hubble Ultra Deep Field (HUDF), two HUDF parallel fields, and wide area data over the CANDELS/GOODS-South. The new Spitzer/IRAC data increase the coverage in [3.6] and [4.5] to ∼120h over the HUDF reaching depths of ∼28 (AB,1σ). The improved depth and inclusion of brighter candidates result in direct ≥3σ InfraRed Array Camera (IRAC) detections of 20/63 sources, of which 11/63 are detected at ≥5σ. The average [3.6]-[4.5] colors of IRAC detected galaxies at z ∼ 8 are markedly redder than those at z ∼ 7, observed only 130 Myr later. The simplest explanation is that we witness strong rest-frame optical emission lines (in particular [O III] λλ4959, 5007 + Hβ) moving through the IRAC bandpasses with redshift. Assuming that the average rest-frame spectrum is the same at both z ∼ 7 and z ∼ 8 we estimate a rest-frame equivalent width of contributing 0.56{sup +0.16}{sub -0.11} mag to the [4.5] filter at z ∼ 8. The corresponding W{sub Hα}=430{sup +160}{sub -110} Å implies an average specific star formation rate of sSFR=11{sub -5}{sup +11} Gyr{sup –1} and a stellar population age of 100{sub -50}{sup +100} Myr. Correcting the spectral energy distribution for the contribution of emission lines lowers the average best-fit stellar masses and mass-to-light ratios by ∼3 ×, decreasing the integrated stellar mass density to ρ{sup *}(z=8,M{sub UV}<-18)=0.6{sup +0.4}{sub -0.3}×10{sup 6} M{sub sun} Mpc{sup –3}.

Similarity law for Widom lines and coexistence lines.

Science.gov (United States)

Banuti, D T; Raju, M; Ihme, M

2017-05-01

The coexistence line of a fluid separates liquid and gaseous states at subcritical pressures, ending at the critical point. Only recently, it became clear that the supercritical state space can likewise be divided into regions with liquidlike and gaslike properties, separated by an extension to the coexistence line. This crossover line is commonly referred to as the Widom line, and is characterized by large changes in density or enthalpy, manifesting as maxima in the thermodynamic response functions. Thus, a reliable representation of the coexistence line and the Widom line is important for sub- and supercritical applications that depend on an accurate prediction of fluid properties. While it is known for subcritical pressures that nondimensionalization with the respective species critical pressures p_{cr} and temperatures T_{cr} only collapses coexistence line data for simple fluids, this approach is used for Widom lines of all fluids. However, we show here that the Widom line does not adhere to the corresponding states principle, but instead to the extended corresponding states principle. We resolve this problem in two steps. First, we propose a Widom line functional based on the Clapeyron equation and derive an analytical, species specific expression for the only parameter from the Soave-Redlich-Kwong equation of state. This parameter is a function of the acentric factor ω and compares well with experimental data. Second, we introduce the scaled reduced pressure p_{r}^{*} to replace the previously used reduced pressure p_{r}=p/p_{cr}. We show that p_{r}^{*} is a function of the acentric factor only and can thus be readily determined from fluid property tables. It collapses both subcritical coexistence line and supercritical Widom line data over a wide range of species with acentric factors ranging from -0.38 (helium) to 0.34 (water), including alkanes up to n-hexane. By using p_{r}^{*}, the extended corresponding states principle can be applied within

Pathways to deep decarbonization - 2015 report

International Nuclear Information System (INIS)

Ribera, Teresa; Colombier, Michel; Waisman, Henri; Bataille, Chris; Pierfederici, Roberta; Sachs, Jeffrey; Schmidt-Traub, Guido; Williams, Jim; Segafredo, Laura; Hamburg Coplan, Jill; Pharabod, Ivan; Oury, Christian

2015-12-01

In September 2015, the Deep Decarbonization Pathways Project published the Executive Summary of the Pathways to Deep Decarbonization: 2015 Synthesis Report. The full 2015 Synthesis Report was launched in Paris on December 3, 2015, at a technical workshop with the Mitigation Action Plans and Scenarios (MAPS) program. The Deep Decarbonization Pathways Project (DDPP) is a collaborative initiative to understand and show how individual countries can transition to a low-carbon economy and how the world can meet the internationally agreed target of limiting the increase in global mean surface temperature to less than 2 degrees Celsius (deg. C). Achieving the 2 deg. C limit will require that global net emissions of greenhouse gases (GHG) approach zero by the second half of the century. In turn, this will require a profound transformation of energy systems by mid-century through steep declines in carbon intensity in all sectors of the economy, a transition we call 'deep decarbonization'

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Science.gov (United States)

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-11

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Abrupt stop of deep water turnover with lake warming: Drastic consequences for algal primary producers.

Science.gov (United States)

Yankova, Yana; Neuenschwander, Stefan; Köster, Oliver; Posch, Thomas

2017-10-23

After strong fertilization in the 20 th century, many deep lakes in Central Europe are again nutrient poor due to long-lasting restoration (re-oligotrophication). In line with reduced phosphorus and nitrogen loadings, total organismic productivity decreased and lakes have now historically low nutrient and biomass concentrations. This caused speculations that restoration was overdone and intended fertilizations are needed to ensure ecological functionality. Here we show that recent re-oligotrophication processes indeed accelerated, however caused by lake warming. Rising air temperatures strengthen thermal stabilization of water columns which prevents thorough turnover (holomixis). Reduced mixis impedes down-welling of oxygen rich epilimnetic (surface) and up-welling of phosphorus and nitrogen rich hypolimnetic (deep) water. However, nutrient inputs are essential for algal spring blooms acting as boost for annual food web successions. We show that repeated lack (since 1977) and complete stop (since 2013) of holomixis caused drastic epilimnetic phosphorus depletions and an absence of phytoplankton spring blooms in Lake Zurich (Switzerland). By simulating holomixis in experiments, we could induce significant vernal algal blooms, confirming that there would be sufficient hypolimnetic phosphorus which presently accumulates due to reduced export. Thus, intended fertilizations are highly questionable, as hypolimnetic nutrients will become available during future natural or artificial turnovers.

Deep learning

CERN Document Server

Goodfellow, Ian; Courville, Aaron

2016-01-01

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language proces...

Boosting compound-protein interaction prediction by deep learning.

Science.gov (United States)

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

Dragmacidin G, a Bioactive Bis-Indole Alkaloid from a Deep-Water Sponge of the Genus Spongosorites.

Science.gov (United States)

Wright, Amy E; Killday, K Brian; Chakrabarti, Debopam; Guzmán, Esther A; Harmody, Dedra; McCarthy, Peter J; Pitts, Tara; Pomponi, Shirley A; Reed, John K; Roberts, Bracken F; Rodrigues Felix, Carolina; Rohde, Kyle H

2017-01-11

A deep-water sponge of the genus Spongosorites has yielded a bis-indole alkaloid which we have named dragmacidin G. Dragmacidin G was first reported by us in the patent literature and has recently been reported by Hitora et al. from a sponge of the genus Lipastrotheya . Dragmacidin G is the first in this series of compounds to have a pyrazine ring linking the two indole rings. It also has a rare N -(2-mercaptoethyl)-guanidine side chain. Dragmacidin G shows a broad spectrum of biological activity including inhibition of methicillin-resistant Staphylococcus aureus , Mycobacterium tuberculosis , Plasmodium falciparum, and a panel of pancreatic cancer cell lines.

Dragmacidin G, a Bioactive Bis-Indole Alkaloid from a Deep-Water Sponge of the Genus Spongosorites

Directory of Open Access Journals (Sweden)

Amy E. Wright

2017-01-01

Full Text Available A deep-water sponge of the genus Spongosorites has yielded a bis-indole alkaloid which we have named dragmacidin G. Dragmacidin G was first reported by us in the patent literature and has recently been reported by Hitora et al. from a sponge of the genus Lipastrotheya. Dragmacidin G is the first in this series of compounds to have a pyrazine ring linking the two indole rings. It also has a rare N-(2-mercaptoethyl-guanidine side chain. Dragmacidin G shows a broad spectrum of biological activity including inhibition of methicillin-resistant Staphylococcus aureus, Mycobacterium tuberculosis, Plasmodium falciparum, and a panel of pancreatic cancer cell lines.

Deep Learning in Flavour Tagging at the ATLAS experiment

CERN Document Server

Lanfermann, Marie Christine; The ATLAS collaboration

2017-01-01

A novel higher-level flavour tagging algorithm called DL1 has been developed using a neural network at the ATLAS experiment at the CERN Large Hadron Collider. We have investigated the potential of Deep Learning in flavour tagging using higher-level inputs from lower-level physics-motivated taggers. A systematic grid search over architectures and the training hyperparameter space is presented. In this novel neural network approach, the jet flavours are treated on an equal footing while training with multiple output nodes, which provides a highly flexible tagger. The DL1 studies presented show that the obtained neural network improves discrimination against both light-jets and c-jets, and also provides a novel c-tagging possibility. The performance for arbitrary background mixtures can be fine-tuned after the training by using iso-efficiency lines of constant signal efficiency, according to the to the needs of the physics analysis. The resulting DL1 tagger is described and a detailed set of performance plots pr...

Parallel Distributed Processing Theory in the Age of Deep Networks.

Science.gov (United States)

Bowers, Jeffrey S

2017-12-01

Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory. Copyright © 2017. Published by Elsevier Ltd.

A naturally derived gastric cancer cell line shows latency I Epstein-Barr virus infection closely resembling EBV-associated gastric cancer

International Nuclear Information System (INIS)

Oh, Sang Taek; Seo, Jung Seon; Moon, Uk Yeol; Kang, Kyeong Hee; Shin, Dong-Jik; Yoon, Sungjoo Kim; Kim, Woo Ho; Park, Jae-Gahb; Lee, Suk Kyeong

2004-01-01

In a process seeking out a good model cell line for Epstein-Barr virus (EBV)-associated gastric cancer, we found that one previously established gastric adenocarcinoma cell line is infected with type 1 EBV. This SNU-719 cell line from a Korean patient expressed cytokeratin without CD19 or CD21 expression. In SNU-719, EBNA1 and LMP2A were expressed, while LMP1 and EBNA2 were not. None of the tested lytic EBV proteins were detected in this cell line unless stimulated with phorbol ester. EBV infection was also shown in the original carcinoma tissue of SNU-719 cell line. Our results support the possibility of a CD21-independent EBV infection of gastric epithelial cells in vivo. As the latent EBV gene expression pattern of SNU-719 closely resembles that of the EBV-associated gastric cancer, this naturally derived cell line may serve as a valuable model system to clarify the precise role of EBV in gastric carcinogenesis

Deep underground reactor (passive heat removal of LWR with hard neutron energy spectrum)

Energy Technology Data Exchange (ETDEWEB)

Hiroshi, Takahashi [Brookhaven National Lab., Upton, NY (United States)

2001-07-01

To run a high conversion reactor with Pu-Th fueled tight fueled assembly which has a long burn-up of a fuel, the reactor should be sited deep underground. By putting the reactor deep underground heat can be removed passively not only during a steady-state run and also in an emergency case of loss of coolant and loss of on-site power; hence the safety of the reactor can be much improved. Also, the evacuation area around the reactor can be minimized, and the reactor placed near the consumer area. This approach reduces the cost of generating electricity by eliminating the container building and shortening transmission lines. (author)

Deep underground reactor (passive heat removal of LWR with hard neutron energy spectrum)

International Nuclear Information System (INIS)

Hiroshi, Takahashi

2001-01-01

To run a high conversion reactor with Pu-Th fueled tight fueled assembly which has a long burn-up of a fuel, the reactor should be sited deep underground. By putting the reactor deep underground heat can be removed passively not only during a steady-state run and also in an emergency case of loss of coolant and loss of on-site power; hence the safety of the reactor can be much improved. Also, the evacuation area around the reactor can be minimized, and the reactor placed near the consumer area. This approach reduces the cost of generating electricity by eliminating the container building and shortening transmission lines. (author)

Learning with hierarchical-deep models.

Science.gov (United States)

Salakhutdinov, Ruslan; Tenenbaum, Joshua B; Torralba, Antonio

2013-08-01

We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.

Deep learning in jet reconstruction at CMS

CERN Document Server

Stoye, Markus

2017-01-01

Deep learning has led to several breakthroughs outside the field of high energy physics, yet in jet reconstruction for the CMS experiment at the CERN LHC it has not been used so far. This report shows results of applying deep learning strategies to jet reconstruction at the stage of identifying the original parton association of the jet (jet tagging), which is crucial for physics analyses at the LHC experiments. We introduce a custom deep neural network architecture for jet tagging. We compare the performance of this novel method with the other established approaches at CMS and show that the proposed strategy provides a significant improvement. The strategy provides the first multi-class classifier, instead of the few binary classifiers that previously were used, and thus yields more information and in a more convenient way. The performance results obtained with simulation imply a significant improvement for a large number of important physics analysis at the CMS experiment.

Photographic but not line-drawn faces show early perceptual neural sensitivity to eye gaze direction

Directory of Open Access Journals (Sweden)

Alejandra eRossi

2015-04-01

Full Text Available Our brains readily decode facial movements and changes in social attention, reflected in earlier and larger N170 event-related potentials (ERPs to viewing gaze aversions vs. direct gaze in real faces (Puce et al. 2000. In contrast, gaze aversions in line-drawn faces do not produce these N170 differences (Rossi et al., 2014, suggesting that physical stimulus properties or experimental context may drive these effects. Here we investigated the role of stimulus-induced context on neurophysiological responses to dynamic gaze. Sixteen healthy adults viewed line-drawn and real faces, with dynamic eye aversion and direct gaze transitions, and control stimuli (scrambled arrays and checkerboards while continuous electroencephalographic (EEG activity was recorded. EEG data from 2 temporo-occipital clusters of 9 electrodes in each hemisphere where N170 activity is known to be maximal were selected for analysis. N170 peak amplitude and latency, and temporal dynamics from event-related spectral perturbations (ERSPs were measured in 16 healthy subjects. Real faces generated larger N170s for averted vs. direct gaze motion, however, N170s to real and direct gaze were as large as those to respective controls. N170 amplitude did not differ across line-drawn gaze changes. Overall, bilateral mean gamma power changes for faces relative to control stimuli occurred between 150-350 ms, potentially reflecting signal detection of facial motion.Our data indicate that experimental context does not drive N170 differences to viewed gaze changes. Low-level stimulus properties, such as the high sclera/iris contrast change in real eyes likely drive the N170 changes to viewed aversive movements.

A new cell line-based coculture model of the human air-blood barrier to evaluate the interaction with aerosolized drug carriers

OpenAIRE

Kletting, Stephanie

2016-01-01

Besides reducing animal testing, in vitro models allow for the pre-screening of new drug candidates in terms of safety and efficacy before they enter clinical trials. To date, models mimicking the deep lung show limitations such as cellular origin or lack of appropriate barrier properties. Therefore, the focus of this work was on the establishment of a robust and reproducible cell line-based coculture model that reflects the two major barrier structures present in the alveolar region, namely ...

A New Calculation Method of Dynamic Kill Fluid Density Variation during Deep Water Drilling

Directory of Open Access Journals (Sweden)

Honghai Fan

2017-01-01

Full Text Available There are plenty of uncertainties and enormous challenges in deep water drilling due to complicated shallow flow and deep strata of high temperature and pressure. This paper investigates density of dynamic kill fluid and optimum density during the kill operation process in which dynamic kill process can be divided into two stages, that is, dynamic stable stage and static stable stage. The dynamic kill fluid consists of a single liquid phase and different solid phases. In addition, liquid phase is a mixture of water and oil. Therefore, a new method in calculating the temperature and pressure field of deep water wellbore is proposed. The paper calculates the changing trend of kill fluid density under different temperature and pressure by means of superposition method, nonlinear regression, and segment processing technique. By employing the improved model of kill fluid density, deep water kill operation in a well is investigated. By comparison, the calculated density results are in line with the field data. The model proposed in this paper proves to be satisfactory in optimizing dynamic kill operations to ensure the safety in deep water.

Overview of deep learning in medical imaging.

Science.gov (United States)

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a

Rescuing Alu: recovery of new inserts shows LINE-1 preserves Alu activity through A-tail expansion.

Directory of Open Access Journals (Sweden)

Bradley J Wagstaff

Full Text Available Alu elements are trans-mobilized by the autonomous non-LTR retroelement, LINE-1 (L1. Alu-induced insertion mutagenesis contributes to about 0.1% human genetic disease and is responsible for the majority of the documented instances of human retroelement insertion-induced disease. Here we introduce a SINE recovery method that provides a complementary approach for comprehensive analysis of the impact and biological mechanisms of Alu retrotransposition. Using this approach, we recovered 226 de novo tagged Alu inserts in HeLa cells. Our analysis reveals that in human cells marked Alu inserts driven by either exogenously supplied full length L1 or ORF2 protein are indistinguishable. Four percent of de novo Alu inserts were associated with genomic deletions and rearrangements and lacked the hallmarks of retrotransposition. In contrast to L1 inserts, 5' truncations of Alu inserts are rare, as most of the recovered inserts (96.5% are full length. De novo Alus show a random pattern of insertion across chromosomes, but further characterization revealed an Alu insertion bias exists favoring insertion near other SINEs, highly conserved elements, with almost 60% landing within genes. De novo Alu inserts show no evidence of RNA editing. Priming for reverse transcription rarely occurred within the first 20 bp (most 5' of the A-tail. The A-tails of recovered inserts show significant expansion, with many at least doubling in length. Sequence manipulation of the construct led to the demonstration that the A-tail expansion likely occurs during insertion due to slippage by the L1 ORF2 protein. We postulate that the A-tail expansion directly impacts Alu evolution by reintroducing new active source elements to counteract the natural loss of active Alus and minimizing Alu extinction.

Deep absorption line studies of quiescent galaxies at z similar z ~ 2

DEFF Research Database (Denmark)

Toft, Sune; Gallazzi, Anna Rita; Zirm, Andrew Wasmuth

2012-01-01

the majority of its stars at z > 3 and currently has little or no ongoing star formation. We compile a sample of three other z similar to 2 quiescent galaxies with measured velocity dispersions, two of which are also post-starburst like. Their dynamical-mass-size relation is offset significantly less than...... the stellar-mass-size relation from the local early-type relations, which we attribute to a lower central dark matter fraction. Recent cosmological merger simulations agree qualitatively with the data, but cannot fully account for the evolution in the dark matter fraction. The z similar to 2 FP requires......We present dynamical and structural scaling relations of quiescent galaxies at z = 2, including the dynamical-mass-size relation and the first constraints on the fundamental plane (FP). The backbone of the analysis is a new, very deep Very Large Telescope/X-shooter spectrum of a massive, compact...

Automatic activation of word phonology from print in deep dyslexia.

Science.gov (United States)

Katz, R B; Lanzoni, S M

1992-11-01

The performance of deep dyslexics in oral reading and other tasks suggests that they are poor at activating the phonology of words and non-words from printed stimuli. As the tasks ordinarily used to test deep dyslexics require controlled processing, it is possible that the phonology of printed words can be better activated on an automatic basis. This study investigated this possibility by testing a deep dyslexic patient on a lexical decision task with pairs of stimuli presented simultaneously. In Experiment 1, which used content words as stimuli, the deep dyslexic, like normal subjects, showed faster reaction times on trials with rhyming, similarly spelled stimuli (e.g. bribe-tribe) than on control trials (consisting of non-rhyming, dissimilarly spelled words), but slower reaction times on trials with non-rhyming, similarly spelled stimuli (e.g. couch-touch). When the experiment was repeated using function words as stimuli, the patient no longer showed a phonological effect. Therefore, the phonological activation of printed content words by deep dyslexics may be better than would be expected on the basis of their oral reading performance.

Deep Super Learner: A Deep Ensemble for Classification Problems

OpenAIRE

Young, Steven; Abdou, Tamer; Bener, Ayse

2018-01-01

Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datase...

A distributed data base management system. [for Deep Space Network

Science.gov (United States)

Bryan, A. I.

1975-01-01

Major system design features of a distributed data management system for the NASA Deep Space Network (DSN) designed for continuous two-way deep space communications are described. The reasons for which the distributed data base utilizing third-generation minicomputers is selected as the optimum approach for the DSN are threefold: (1) with a distributed master data base, valid data is available in real-time to support DSN management activities at each location; (2) data base integrity is the responsibility of local management; and (3) the data acquisition/distribution and processing power of a third-generation computer enables the computer to function successfully as a data handler or as an on-line process controller. The concept of the distributed data base is discussed along with the software, data base integrity, and hardware used. The data analysis/update constraint is examined.

THE DEEP2 GALAXY REDSHIFT SURVEY: DESIGN, OBSERVATIONS, DATA REDUCTION, AND REDSHIFTS

International Nuclear Information System (INIS)

Newman, Jeffrey A.; Cooper, Michael C.; Davis, Marc; Faber, S. M.; Guhathakurta, Puragra; Koo, David C.; Phillips, Andrew C.; Conroy, Charlie; Harker, Justin J.; Lai, Kamson; Coil, Alison L.; Dutton, Aaron A.; Finkbeiner, Douglas P.; Gerke, Brian F.; Rosario, David J.; Weiner, Benjamin J.; Willmer, C. N. A.; Yan Renbin; Kassin, Susan A.; Konidaris, N. P.

2013-01-01

We describe the design and data analysis of the DEEP2 Galaxy Redshift Survey, the densest and largest high-precision redshift survey of galaxies at z ∼ 1 completed to date. The survey was designed to conduct a comprehensive census of massive galaxies, their properties, environments, and large-scale structure down to absolute magnitude M B = –20 at z ∼ 1 via ∼90 nights of observation on the Keck telescope. The survey covers an area of 2.8 deg 2 divided into four separate fields observed to a limiting apparent magnitude of R AB = 24.1. Objects with z ∼ 0.7 to be targeted ∼2.5 times more efficiently than in a purely magnitude-limited sample. Approximately 60% of eligible targets are chosen for spectroscopy, yielding nearly 53,000 spectra and more than 38,000 reliable redshift measurements. Most of the targets that fail to yield secure redshifts are blue objects that lie beyond z ∼ 1.45, where the [O II] 3727 Å doublet lies in the infrared. The DEIMOS 1200 line mm –1 grating used for the survey delivers high spectral resolution (R ∼ 6000), accurate and secure redshifts, and unique internal kinematic information. Extensive ancillary data are available in the DEEP2 fields, particularly in the Extended Groth Strip, which has evolved into one of the richest multiwavelength regions on the sky. This paper is intended as a handbook for users of the DEEP2 Data Release 4, which includes all DEEP2 spectra and redshifts, as well as for the DEEP2 DEIMOS data reduction pipelines. Extensive details are provided on object selection, mask design, biases in target selection and redshift measurements, the spec2d two-dimensional data-reduction pipeline, the spec1d automated redshift pipeline, and the zspec visual redshift verification process, along with examples of instrumental signatures or other artifacts that in some cases remain after data reduction. Redshift errors and catastrophic failure rates are assessed through more than 2000 objects with duplicate

DeepRT: deep learning for peptide retention time prediction in proteomics

OpenAIRE

Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

2017-01-01

Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

Cytoglobosins H and I, New Antiproliferative Cytochalasans from Deep-Sea-Derived Fungus Chaetomium globosum

Directory of Open Access Journals (Sweden)

Zhihan Zhang

2016-12-01

Full Text Available Cytoglobosins H (1 and I (2, together with seven known cytochalasan alkaloids (3–9, were isolated from the deep-sea-derived fungus Chaetomium globosum. The structures of new compounds 1 and 2 were elucidated by extensive 1D and 2D NMR and mass spectroscopic data. All the compounds were evaluated for their antiproliferative activities against MDA-MB-231 human breast cancer cells, LNCaP human prostate cancer cells, and B16F10 mouse melanoma cells. Compound 6 showed significant antiproliferative activity against LNCaP and B16F10 cell lines with IC50 values of 0.62 and 2.78 μM, respectively. Further testing confirmed that compound 6 inhibited the growth of LNCaP cells by inducing apoptosis.

Modeling of Antenna for Deep Target Hydrocarbon Exploration

Directory of Open Access Journals (Sweden)

Nadeem Nasir

2017-11-01

Full Text Available Nowadays control source electromagnetic method is used for offshore hydrocarbon exploration. Hydrocarbon detection in sea bed logging (SBL is a very challenging task for deep target hydrocarbon reservoir. Response of electromagnetic (EM field from marine environment is very low and it is very difficult to predict deep target reservoir below 2km from the sea floor. This work premise deals with modeling of new antenna for deep water deep target hydrocarbon exploration. Conventional and new EM antennas at 0.125Hz frequency are used in modeling for the detection of deep target hydrocarbon  reservoir.  The  proposed  area  of  the  seabed model   (40km ´ 40km   was   simulated   by using CST (computer simulation technology EM studio based on Finite Integration Method (FIM. Electromagnetic field components were compared at 500m target depth and it was concluded that Ex and Hz components shows better resistivity contrast. Comparison of conventional and new antenna for different target  depths  was  done in  our  proposed  model.  From  the results, it was observed that conventional antenna at 0.125Hz shows 70% ,86% resistivity contrast at target depth of 1000m where   as   new   antenna   showed   329%, 355%   resistivity contrast at the same target depth for Ex and Hz field respectively.  It  was  also  investigated  that  at  frequency of0.125Hz, new antenna gave 46% better delineation of hydrocarbon at 4000m target depth. This is due to focusing of electromagnetic waves by using new antenna. New antenna design gave 125% more extra depth than straight antenna for deep target hydrocarbon detection. Numerical modeling for straight  and  new antenna  was also done to know general equation for electromagnetic field behavior with target depth. From this numerical model it was speculated that this new antenna can detect up to 4.5 km target depth. This new EM antenna may open new frontiers for oil and gas

Survey of Cold Water Lines in Protoplanetary Disks : Indications of Systematic Volatile Depletion

NARCIS (Netherlands)

Du, F.; Bergin, E.A.; Hogerheijde, M.; van Dishoeck, E.F.; Blake, G.; Bruderer, S.; Cleeves, I.; Dominik, C.; Fedele, D.; Lis, D.C.; Melnick, G.; Neufeld, D.; Pearson, J.; Yıldız, U.

2017-01-01

We performed very deep searches for 2 ground-state water transitions in 13 protoplanetary disks with the HIFI instrument on board the Herschel Space Observatory, with integration times up to 12 hr per line. We also searched for, with shallower integrations, two other water transitions that sample

Parallel Distributed Processing theory in the age of deep networks

OpenAIRE

Bowers, Jeffrey

2017-01-01

Parallel Distributed Processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely, that all knowledge is coded in a distributed format, and cognition is mediated by non-symbolic computations. These claims have long been debated within cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks le...

Analyses of the deep borehole drilling status for a deep borehole disposal system

Energy Technology Data Exchange (ETDEWEB)

Lee, Jong Youl; Choi, Heui Joo; Lee, Min Soo; Kim, Geon Young; Kim, Kyung Su [KAERI, Daejeon (Korea, Republic of)

2016-05-15

The purpose of disposal for radioactive wastes is not only to isolate them from humans, but also to inhibit leakage of any radioactive materials into the accessible environment. Because of the extremely high level and long-time scale radioactivity of HLW(High-level radioactive waste), a mined deep geological disposal concept, the disposal depth is about 500 m below ground, is considered as the safest method to isolate the spent fuels or high-level radioactive waste from the human environment with the best available technology at present time. Therefore, as an alternative disposal concept, i.e., deep borehole disposal technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general status of deep drilling technologies was reviewed for deep borehole disposal of high level radioactive wastes. Based on the results of these review, very preliminary applicability of deep drilling technology for deep borehole disposal analyzed. In this paper, as one of key technologies of deep borehole disposal system, the general status of deep drilling technologies in oil industry, geothermal industry and geo scientific field was reviewed for deep borehole disposal of high level radioactive wastes. Based on the results of these review, the very preliminary applicability of deep drilling technology for deep borehole disposal such as relation between depth and diameter, drilling time and feasibility classification was analyzed.

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning with Deep Belief Networks.

Science.gov (United States)

Ying, Jun; Dutta, Joyita; Guo, Ning; Hu, Chenhui; Zhou, Dan; Sitek, Arkadiusz; Li, Quanzheng

2016-12-21

This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A threelayer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. 10,300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a 10-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We thus demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

Blue-wing enhancement of the chromospheric Mg II h and k lines in a solar flare

Science.gov (United States)

Tei, Akiko; Sakaue, Takahito; Okamoto, Takenori J.; Kawate, Tomoko; Heinzel, Petr; UeNo, Satoru; Asai, Ayumi; Ichimoto, Kiyoshi; Shibata, Kazunari

2018-05-01

We performed coordinated observations of AR 12205, which showed a C-class flare on 2014 November 11, with the Interface Region Imaging Spectrograph (IRIS) and the Domeless Solar Telescope (DST) at Hida Observatory. Using spectral data in the Si IV 1403 Å, C II 1335 Å, and Mg II h and k lines from IRIS and the Ca II K, Ca II 8542 Å, and Hα lines from DST, we investigated a moving flare kernel during the flare. In the Mg II h line, the leading edge of the flare kernel showed an intensity enhancement in the blue wing and a smaller intensity of the blue-side peak (h2v) than that of the red-side one (h2r). The blueshift lasted for 9-48 s with a typical speed of 10.1 ± 2.6 km s-1, which was followed by a high intensity and a large redshift with a speed of up to 51 km s-1 detected in the Mg II h line. The large redshift was a common property for all six lines, but the blueshift prior to it was found only in the Mg II lines. Cloud modeling of the Mg II h line suggests that the blue-wing enhancement with such a peak difference could have been caused by a chromospheric-temperature (cool) upflow. We discuss a scenario in which an upflow of cool plasma is lifted up by expanding hot plasma owing to the deep penetration of non-thermal electrons into the chromosphere. Furthermore, we found that the blueshift persisted without any subsequent redshift in the leading edge of the flare kernel during its decaying phase. The cause of such a long-lasting blueshift is also discussed.

Catwell and Sherdaps for deep-water production fields

Energy Technology Data Exchange (ETDEWEB)

Hopper, H.P.; Rey, R. [Cameron, 34 - Beziers (France)

2000-07-01

The names Catwell and SherDaps are derived from: - Catenary Well - Subsea Horizontal Extended Reach Drilling And Production System. Both systems use the technique of being able to drill a well in deep-water either through a platform catenary carrier pipe or a catenary drilling riser. They also offer, in addition, significant advantages when drilling into shallow reservoirs and the ability to enhance production using platform artificial lift systems or easily serviceable pumps either in the well or at the mud-line. Catwell is a platform system with surface wellheads/trees whereas SherDaps uses a group of subsea wellheads/trees/BOP's that are accessible from one permanent catenary drilling riser. Both systems allow drilling/completing and future well intervention from a central location that otherwise would have required several drilling centres (i.e. platforms or subsea) if the conventional approach was followed. It is envisaged that well targets close to a platform will use well conductors possibly with mud-line wellheads, then Catwell to reach the medium range well targets and SherDaps for long range wells. It is considered that this arrangement would allow a single surface drilling/ production centre to have access to well targets giving a foot print range of up to a 20 km diameter. The total Capex savings on a Deep-water Field Development could be in the region of $200 m on a $1 billion development. Opex will be lower with the ability from the drilling center to quickly access any problem well and rectify any faults, minimising lost production. (authors)

Plastic microfibre ingestion by deep-sea organisms

Science.gov (United States)

Taylor, M. L.; Gwinnett, C.; Robinson, L. F.; Woodall, L. C.

2016-09-01

Plastic waste is a distinctive indicator of the world-wide impact of anthropogenic activities. Both macro- and micro-plastics are found in the ocean, but as yet little is known about their ultimate fate and their impact on marine ecosystems. In this study we present the first evidence that microplastics are already becoming integrated into deep-water organisms. By examining organisms that live on the deep-sea floor we show that plastic microfibres are ingested and internalised by members of at least three major phyla with different feeding mechanisms. These results demonstrate that, despite its remote location, the deep sea and its fragile habitats are already being exposed to human waste to the extent that diverse organisms are ingesting microplastics.

Implications of the Deep Minimum for Slow Solar Wind Origin

Science.gov (United States)

Antiochos, S. K.; Mikic, Z.; Lionello, R.; Titov, V. S.; Linker, J. A.

2009-12-01

The origin of the slow solar wind has long been one of the most important problems in solar/heliospheric physics. Two observational constraints make this problem especially challenging. First, the slow wind has the composition of the closed-field corona, unlike the fast wind that originates on open field lines. Second, the slow wind has substantial angular extent, of order 30 degrees, which is much larger than the widths observed for streamer stalks or the widths expected theoretically for a dynamic heliospheric current sheet. We propose that the slow wind originates from an intricate network of narrow (possibly singular) open-field corridors that emanate from the polar coronal hole regions. Using topological arguments, we show that these corridors must be ubiquitous in the solar corona. The total solar eclipse in August 2008, near the lowest point of the Deep Minimum, affords an ideal opportunity to test this theory by using the ultra-high resolution Predictive Science's (PSI) eclipse model for the corona and wind. Analysis of the PSI eclipse model demonstrates that the extent and scales of the open-field corridors can account for both the angular width of the slow wind and its closed-field composition. We discuss the implications of our slow wind theory for the structure of the corona and heliosphere at the Deep Minimum and describe further observational and theoretical tests. This work has been supported by the NASA HTP, SR&T, and LWS programs.

Deep Space Telecommunications

Science.gov (United States)

Kuiper, T. B. H.; Resch, G. M.

2000-01-01

The increasing load on NASA's deep Space Network, the new capabilities for deep space missions inherent in a next-generation radio telescope, and the potential of new telescope technology for reducing construction and operation costs suggest a natural marriage between radio astronomy and deep space telecommunications in developing advanced radio telescope concepts.

Greedy Deep Dictionary Learning

OpenAIRE

Tariyal, Snigdha; Majumdar, Angshul; Singh, Richa; Vatsa, Mayank

2016-01-01

In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning t...

DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning.

Science.gov (United States)

Laksshman, Sundaram; Bhat, Rajendra Rana; Viswanath, Vivek; Li, Xiaolin

2017-09-01

Bipolar disorder, also known as manic depression, is a brain disorder that affects the brain structure of a patient. It results in extreme mood swings, severe states of depression, and overexcitement simultaneously. It is estimated that roughly 3% of the population of the United States (about 5.3 million adults) suffers from bipolar disorder. Recent research efforts like the Twin studies have demonstrated a high heritability factor for the disorder, making genomics a viable alternative for detecting and treating bipolar disorder, in addition to the conventional lengthy and costly postsymptom clinical diagnosis. Motivated by this study, leveraging several emerging deep learning algorithms, we design an end-to-end deep learning architecture (called DeepBipolar) to predict bipolar disorder based on limited genomic data. DeepBipolar adopts the Deep Convolutional Neural Network (DCNN) architecture that automatically extracts features from genotype information to predict the bipolar phenotype. We participated in the Critical Assessment of Genome Interpretation (CAGI) bipolar disorder challenge and DeepBipolar was considered the most successful by the independent assessor. In this work, we thoroughly evaluate the performance of DeepBipolar and analyze the type of signals we believe could have affected the classifier in distinguishing the case samples from the control set. © 2017 Wiley Periodicals, Inc.

Deep learning? What deep learning? | Fourie | South African ...

African Journals Online (AJOL)

In teaching generally over the past twenty years, there has been a move towards teaching methods that encourage deep, rather than surface approaches to learning. The reason for this being that students, who adopt a deep approach to learning are considered to have learning outcomes of a better quality and desirability ...

Mexico; 2013 Review Under the Flexible Credit Line Arrangement

OpenAIRE

International Monetary Fund

2013-01-01

This paper discusses Mexico’ Review Under the Flexible Credit Line (FCL) Arrangement. Significant progress has been made in advancing far-reaching structural reforms, signaling Mexico’s commitment to address deep-rooted impediments to growth. The economy slowed down in early 2013, but is expected to recover starting in the second half of the year. Mexico’s financial markets have functioned reasonably well through the recent global volatility, although with some currency depreciation and a ris...

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

Science.gov (United States)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

Life on the Number Line: Routes to Understanding Fraction Magnitude for Students With Difficulties Learning Mathematics.

Science.gov (United States)

Gersten, Russell; Schumacher, Robin F; Jordan, Nancy C

Magnitude understanding is critical for students to develop a deep understanding of fractions and more advanced mathematics curriculum. The research reports in this special issue underscore magnitude understanding for fractions and emphasize number lines as both an assessment and an instructional tool. In this commentary, we discuss how number lines broaden the concept of fractions for students who are tied to the more general part-whole representations of area models. We also discuss how number lines, compared to other representations, are a superior and more mathematically correct way to explain fraction concepts.

Computations in the deep vs superficial layers of the cerebral cortex.

Science.gov (United States)

Rolls, Edmund T; Mills, W Patrick C

2017-11-01

A fundamental question is how the cerebral neocortex operates functionally, computationally. The cerebral neocortex with its superficial and deep layers and highly developed recurrent collateral systems that provide a basis for memory-related processing might perform somewhat different computations in the superficial and deep layers. Here we take into account the quantitative connectivity within and between laminae. Using integrate-and-fire neuronal network simulations that incorporate this connectivity, we first show that attractor networks implemented in the deep layers that are activated by the superficial layers could be partly independent in that the deep layers might have a different time course, which might because of adaptation be more transient and useful for outputs from the neocortex. In contrast the superficial layers could implement more prolonged firing, useful for slow learning and for short-term memory. Second, we show that a different type of computation could in principle be performed in the superficial and deep layers, by showing that the superficial layers could operate as a discrete attractor network useful for categorisation and feeding information forward up a cortical hierarchy, whereas the deep layers could operate as a continuous attractor network useful for providing a spatially and temporally smooth output to output systems in the brain. A key advance is that we draw attention to the functions of the recurrent collateral connections between cortical pyramidal cells, often omitted in canonical models of the neocortex, and address principles of operation of the neocortex by which the superficial and deep layers might be specialized for different types of attractor-related memory functions implemented by the recurrent collaterals. Copyright © 2017 Elsevier Inc. All rights reserved.

Applying a punch with microridges in multistage deep drawing processes.

Science.gov (United States)

Lin, Bor-Tsuen; Yang, Cheng-Yu

2016-01-01

The developers of high aspect ratio components aim to minimize the processing stages in deep drawing processes. This study elucidates the application of microridge punches in multistage deep drawing processes. A microridge punch improves drawing performance, thereby reducing the number of stages required in deep forming processes. As an example, the original eight-stage deep forming process for a copper cylindrical cup with a high aspect ratio was analyzed by finite element simulation. Microridge punch designs were introduced in Stages 4 and 7 to replace the original punches. In addition, Stages 3 and 6 were eliminated. Finally, these changes were verified through experiments. The results showed that the microridge punches reduced the number of deep drawing stages yielding similar thickness difference percentages. Further, the numerical and experimental results demonstrated good consistency in the thickness distribution.

Proceedings of the 6th Computer Science On-line Conference 2017

CERN Document Server

Senkerik, Roman; Oplatkova, Zuzana; Prokopova, Zdenka; Silhavy, Petr

2017-01-01

This book presents new methods and approaches to real-world problems as well as exploratory research that describes novel artificial intelligence applications, including deep learning, neural networks and hybrid algorithms. This book constitutes the refereed proceedings of the Artificial Intelligence Trends in Intelligent Systems Section of the 6th Computer Science On-line Conference 2017 (CSOC 2017), held in April 2017. .

Continuum and Line Emission Simulation of Star-Forming Galaxies and Development of a New Sub-mm Inte

Science.gov (United States)

Lagache, Guilaine

2018-01-01

Nowadays, most of the constraints on the dusty star formation at high z comes from deep continuum surveys. We developed a new simulation of the dusty extragalactic sky with a realistic clustering. The comparison between single-dish and interferometric data showed that the clustering inside the beam of a single-dish instrument can seriously bias their measurements. Fortunately, these simulations also show that the beam of a >30-meter dish in the mm should not be affected by serious multiplicity effects. We will give predictions for important characteristics of future AtLAST surveys (as confusion limit, number of detections, properties of detected galaxies). These simulations can also include line emission to prepare a future sub-mm low-resolution spectroscopic survey at high z with AtLAST. Such a survey could be built on the legacy of the CONCERTO survey, that will map the fluctuations of the CII line intensity in the reionisation and post-reionisation epoch. A "super-CONCERTO" instrument on AtLAST would be a perfect first-light instrument to unveil the gigantic potential of this telescope.

Brevianamides and Mycophenolic Acid Derivatives from the Deep-Sea-Derived Fungus Penicillium brevicompactum DFFSCS025

Directory of Open Access Journals (Sweden)

Xinya Xu

2017-02-01

Full Text Available Four new compounds (1–4, including two brevianamides and two mycochromenic acid derivatives along with six known compounds were isolated from the deep-sea-derived fungus Penicillium brevicompactum DFFSCS025. Their structures were elucidated by spectroscopic analysis. Moreover, the absolute configurations of 1 and 2 were determined by quantum chemical calculations of the electronic circular dichroism (ECD spectra. Compound 9 showed moderate cytotoxicity against human colon cancer HCT116 cell line with IC50 value of 15.6 μM. In addition, 3 and 5 had significant antifouling activity against Bugula neritina larval settlement with EC50 values of 13.7 and 22.6 μM, respectively. The NMR data of 6, 8, and 9 were assigned for the first time.

The fabrication of silicon nanostructures by local gallium implantation and cryogenic deep reactive ion etching

International Nuclear Information System (INIS)

Chekurov, N; Grigoras, K; Franssila, S; Tittonen, I; Peltonen, A

2009-01-01

We show that gallium-ion-implanted silicon serves as an etch mask for fabrication of high aspect ratio nanostructures by cryogenic plasma etching (deep reactive ion etching). The speed of focused ion beam (FIB) patterning is greatly enhanced by the fact that only a thin approx. 30 nm surface layer needs to be modified to create a mask for the etching step. Etch selectivity between gallium-doped and undoped material is at least 1000:1, greatly decreasing the mask erosion problems. The resolution of the combined FIB-DRIE process is 20 lines μm -1 with the smallest masked feature size of 40 nm. The maximum achieved aspect ratio is 15:1 (e.g. 600 nm high pillars 40 nm in diameter).

FINITE ELEMENT ANALYSIS OF DEEP BEAM UNDER DIRECT AND INDIRECT LOAD

Directory of Open Access Journals (Sweden)

Haleem K. Hussain

2018-05-01

Full Text Available This research study the effect of exist of opening in web of deep beam loaded directly and indirectly and the behavior of reinforced concrete deep beams without with and without web reinforcement, the opening size and shear span ratio (a/d was constant. Nonlinear analysis using the finite element method with ANSYS software release 12.0 program was used to predict the ultimate load capacity and crack propagation for reinforced concrete deep beams with openings. The adopted beam models depend on experimental test program of reinforced concrete deep beam with and without openings and the finite element analysis result showed a good agreement with small amount of deference in ultimate beam capacity with (ANSYS analysis and it was completely efficient to simulate the behavior of reinforced concrete deep beams. The mid-span deflection at ultimate applied load and inclined cracked were highly compatible with experimental results. The model with opening in the shear span shows a reduction in the load-carrying capacity of beam and adding the vertical stirrup has improve the capacity of ultimate beam load.

Quantitative phase microscopy using deep neural networks

Science.gov (United States)

Li, Shuai; Sinha, Ayan; Lee, Justin; Barbastathis, George

2018-02-01

Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved.

Deep learning—Accelerating Next Generation Performance Analysis Systems?

Directory of Open Access Journals (Sweden)

Heike Brock

2018-02-01

Full Text Available Deep neural network architectures show superior performance in recognition and prediction tasks of the image, speech and natural language domains. The success of such multi-layered networks encourages their implementation in further application scenarios as the retrieval of relevant motion information for performance enhancement in sports. However, to date deep learning is only seldom applied to activity recognition problems of the human motion domain. Therefore, its use for sports data analysis might remain abstract to many practitioners. This paper provides a survey on recent works in the field of high-performance motion data and examines relevant technologies for subsequent deployment in real training systems. In particular, it discusses aspects of data acquisition, processing and network modeling. Analysis suggests the advantage of deep neural networks under difficult and noisy data conditions. However, further research is necessary to confirm the benefit of deep learning for next generation performance analysis systems.

Deep learning with Python

CERN Document Server

Chollet, Francois

2018-01-01

DESCRIPTION Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES • Practical code examples • In-depth introduction to Keras • Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGY Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. AUTHOR BIO Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural ...

Deep learning evaluation using deep linguistic processing

OpenAIRE

Kuhnle, Alexander; Copestake, Ann

2017-01-01

We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing 'deep' linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value ...

AN EFFICIENT METHOD FOR DEEP WEB CRAWLER BASED ON ACCURACY -A REVIEW

OpenAIRE

Pranali Zade1, Dr.S.W.Mohod2

2018-01-01

As deep web grows at a very fast pace, there has been increased interest in techniques that help efficiently locate deep-web interfaces. However, due to the large volume of web resources and the dynamic nature of deep web, achieving wide coverage and high efficiency is a challenging issue. We propose a three-stage framework, for efficient harvesting deep web interfaces. Project experimental results on a set of representative domains show the agility and accuracy of our proposed crawler framew...

Deep-Sea Microbes: Linking Biogeochemical Rates to -Omics Approaches

Science.gov (United States)

Herndl, G. J.; Sintes, E.; Bayer, B.; Bergauer, K.; Amano, C.; Hansman, R.; Garcia, J.; Reinthaler, T.

2016-02-01

Over the past decade substantial progress has been made in determining deep ocean microbial activity and resolving some of the enigmas in understanding the deep ocean carbon flux. Also, metagenomics approaches have shed light onto the dark ocean's microbes but linking -omics approaches to biogeochemical rate measurements are generally rare in microbial oceanography and even more so for the deep ocean. In this presentation, we will show by combining metagenomics, -proteomics and biogeochemical rate measurements on the bulk and single-cell level that deep-sea microbes exhibit characteristics of generalists with a large genome repertoire, versatile in utilizing substrate as revealed by metaproteomics. This is in striking contrast with the apparently rather uniform dissolved organic matter pool in the deep ocean. Combining the different -omics approaches with metabolic rate measurements, we will highlight some major inconsistencies and enigmas in our understanding of the carbon cycling and microbial food web structure in the dark ocean.

Multiscale deep features learning for land-use scene recognition

Science.gov (United States)

Yuan, Baohua; Li, Shijin; Li, Ning

2018-01-01

The features extracted from deep convolutional neural networks (CNNs) have shown their promise as generic descriptors for land-use scene recognition. However, most of the work directly adopts the deep features for the classification of remote sensing images, and does not encode the deep features for improving their discriminative power, which can affect the performance of deep feature representations. To address this issue, we propose an effective framework, LASC-CNN, obtained by locality-constrained affine subspace coding (LASC) pooling of a CNN filter bank. LASC-CNN obtains more discriminative deep features than directly extracted from CNNs. Furthermore, LASC-CNN builds on the top convolutional layers of CNNs, which can incorporate multiscale information and regions of arbitrary resolution and sizes. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

Long-term outcome of thalamic deep brain stimulation in two patients with Tourette syndrome.

Science.gov (United States)

Ackermans, Linda; Duits, Annelien; Temel, Yasin; Winogrodzka, Ania; Peeters, Frenk; Beuls, Emile A M; Visser-Vandewalle, Veerle

2010-10-01

Thalamic deep brain stimulation for intractable Tourette Syndrome was introduced in 1999 by Vandewalle et al. In this follow-up study, the authors report on the long-term (6 and 10 years) outcome in terms of tic reduction, cognition, mood and side effects of medial thalamic deep brain stimulation in two previously described Tourette patients. The authors compared the outcome of two patients at 6 and 10 years after surgery with their preoperative status and after 8 months and 5 years of treatment, respectively. Standardised video recordings were scored by three independent investigators. Both patients underwent (neuro)psychological assessment at all time points of follow-up. Tic improvement observed at 5 years in patient 1 (90.1%) was maintained at 10 years (92.6%). In patient 2, the tic improvement at 8 months (82%) was slightly decreased at 6 years (78%). During follow-up, case 1 revealed no changes in cognition, but case 2 showed a decrease in verbal fluency and learning which was in line with his subjective reports. Case 2 showed a slight decrease in depression, but overall psychopathology was still high at 6 years after surgery with an increase in anger and aggression together with difficulties in social adaptation. Besides temporary hardware-related complications, no distressing adverse effects were observed. Bilateral thalamic stimulation may provide sustained tic benefit after at least 6 years, but to maximise overall outcome, attention is needed for postoperative psychosocial adaptation, already prior to surgery.

Deep learning for studies of galaxy morphology

Science.gov (United States)

Tuccillo, D.; Huertas-Company, M.; Decencière, E.; Velasco-Forero, S.

2017-06-01

Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide parametric properties of Hubble Space Telescope like galaxies (half-light radii, Sérsic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We compare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.

Theileria parva antigens recognized by CD8+ T cells show varying degrees of diversity in buffalo-derived infected cell lines.

Science.gov (United States)

Sitt, Tatjana; Pelle, Roger; Chepkwony, Maurine; Morrison, W Ivan; Toye, Philip

2018-05-06

The extent of sequence diversity among the genes encoding 10 antigens (Tp1-10) known to be recognized by CD8+ T lymphocytes from cattle immune to Theileria parva was analysed. The sequences were derived from parasites in 23 buffalo-derived cell lines, three cattle-derived isolates and one cloned cell line obtained from a buffalo-derived stabilate. The results revealed substantial variation among the antigens through sequence diversity. The greatest nucleotide and amino acid diversity were observed in Tp1, Tp2 and Tp9. Tp5 and Tp7 showed the least amount of allelic diversity, and Tp5, Tp6 and Tp7 had the lowest levels of protein diversity. Tp6 was the most conserved protein; only a single non-synonymous substitution was found in all obtained sequences. The ratio of non-synonymous: synonymous substitutions varied from 0.84 (Tp1) to 0.04 (Tp6). Apart from Tp2 and Tp9, we observed no variation in the other defined CD8+ T cell epitopes (Tp4, 5, 7 and 8), indicating that epitope variation is not a universal feature of T. parva antigens. In addition to providing markers that can be used to examine the diversity in T. parva populations, the results highlight the potential for using conserved antigens to develop vaccines that provide broad protection against T. parva.

Technology of drill hole and well cleaning for in-situ leaching on the 16th section line ore body

International Nuclear Information System (INIS)

Li Xiqi; Liu Jianxiang; Wang Haifeng

2003-01-01

The specific feature of the 16th section line ore body of a deposit is deep water table and low confined aquifer. It causes the most trouble for well cleaning during completion and recovering the ore-bearing aquifer to natural state after it is worsened. It is a key point mining the ore body to search for a suitable way for well cleaning at the deep water table

Quantitative phenotyping via deep barcode sequencing.

Science.gov (United States)

Smith, Andrew M; Heisler, Lawrence E; Mellor, Joseph; Kaper, Fiona; Thompson, Michael J; Chee, Mark; Roth, Frederick P; Giaever, Guri; Nislow, Corey

2009-10-01

Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or "Bar-seq," outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that approximately 20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene-environment interactions on a genome-wide scale.

Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules.

Science.gov (United States)

Lusci, Alessandro; Pollastri, Gianluca; Baldi, Pierre

2013-07-22

Shallow machine learning methods have been applied to chemoinformatics problems with some success. As more data becomes available and more complex problems are tackled, deep machine learning methods may also become useful. Here, we present a brief overview of deep learning methods and show in particular how recursive neural network approaches can be applied to the problem of predicting molecular properties. However, molecules are typically described by undirected cyclic graphs, while recursive approaches typically use directed acyclic graphs. Thus, we develop methods to address this discrepancy, essentially by considering an ensemble of recursive neural networks associated with all possible vertex-centered acyclic orientations of the molecular graph. One advantage of this approach is that it relies only minimally on the identification of suitable molecular descriptors because suitable representations are learned automatically from the data. Several variants of this approach are applied to the problem of predicting aqueous solubility and tested on four benchmark data sets. Experimental results show that the performance of the deep learning methods matches or exceeds the performance of other state-of-the-art methods according to several evaluation metrics and expose the fundamental limitations arising from training sets that are too small or too noisy. A Web-based predictor, AquaSol, is available online through the ChemDB portal ( cdb.ics.uci.edu ) together with additional material.

Prediction of wax buildup in 24 inch cold, deep sea oil loading line

Energy Technology Data Exchange (ETDEWEB)

Asperger, R.G.; Sattler, R.E.; Tolonen, W.J.; Pitchford, A.C.

1981-10-01

When designing pipelines for cold environments, it is important to know how to predict potential problems due to wax deposition on the pipeline's inner surface. The goal of this work was to determine the rate of wax buildup and the maximum, equlibrium wax thickness for a North Sea field loading line. The experimental techniques and results used to evaluate the waxing potential of the crude oil (B) are described. Also, the theoretic model which was used for predicting the maximum wax deposit thickness in the crude oil (B) loading pipeline at controlled temperatures of 40 F (4.4 C) and 100 F (38 C), is illustrated. Included is a recommendation of a procedure for using hot oil at the end of a tanker loading period in order to dewax the crude oil (B) line. This technique would give maximum heating of the pipeline and should be followed by shutting the hot oil into the pipeline at the end of the loading cycle which will provide a hot oil soaking to help soften existing wax. 14 references.

Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings

KAUST Repository

Shaheen, Sara

2017-12-25

Convolutional sparse coding (CSC) plays an essential role in many computer vision applications ranging from image compression to deep learning. In this work, we spot the light on a new application where CSC can effectively serve, namely line drawing analysis. The process of drawing a line drawing can be approximated as the sparse spatial localization of a number of typical basic strokes, which in turn can be cast as a non-standard CSC model that considers the line drawing formation process from parametric curves. These curves are learned to optimize the fit between the model and a specific set of line drawings. Parametric representation of sketches is vital in enabling automatic sketch analysis, synthesis and manipulation. A couple of sketch manipulation examples are demonstrated in this work. Consequently, our novel method is expected to provide a reliable and automatic method for parametric sketch description. Through experiments, we empirically validate the convergence of our method to a feasible solution.

Lung Adenocarcinomas and Lung Cancer Cell Lines Show Association of MMP-1 Expression With STAT3 Activation

Directory of Open Access Journals (Sweden)

Alexander Schütz

2015-04-01

Full Text Available Signal transducer and activator of transcription 3 (STAT3 is constitutively activated in the majority of lung cancer. This study aims at defining connections between STAT3 function and the malignant properties of non–small cell lung carcinoma (NSCLC cells. To address possible mechanisms by which STAT3 influences invasiveness, the expression of matrix metalloproteinase-1 (MMP-1 was analyzed and correlated with the STAT3 activity status. Studies on both surgical biopsies and on lung cancer cell lines revealed a coincidence of STAT3 activation and strong expression of MMP-1. MMP-1 and tyrosine-phosphorylated activated STAT3 were found co-localized in cancer tissues, most pronounced in tumor fronts, and in particular in adenocarcinomas. STAT3 activity was constitutive, although to different degrees, in the lung cancer cell lines investigated. Three cell lines (BEN, KNS62, and A549 were identified in which STAT3 activitation was inducible by Interleukin-6 (IL-6. In A549 cells, STAT3 activity enhanced the level of MMP-1 mRNA and stimulated transcription from the MMP-1 promoter in IL-6–stimulated A549 cells. STAT3 specificity of this effect was confirmed by STAT3 knockdown through RNA interference. Our results link aberrant activity of STAT3 in lung cancer cells to malignant tumor progression through up-regulation of expression of invasiveness-associated MMPs.

Understanding deep roots and their functions in ecosystems: an advocacy for more unconventional research

Science.gov (United States)

Pierret, Alain; Maeght, Jean-Luc; Clément, Corentin; Montoroi, Jean-Pierre; Hartmann, Christian; Gonkhamdee, Santimaitree

2016-01-01

Background Deep roots are a common trait among a wide range of plant species and biomes, and are pivotal to the very existence of ecosystem services such as pedogenesis, groundwater and streamflow regulation, soil carbon sequestration and moisture content in the lower troposphere. Notwithstanding the growing realization of the functional significance of deep roots across disciplines such as soil science, agronomy, hydrology, ecophysiology or climatology, research efforts allocated to the study of deep roots remain incommensurate with those devoted to shallow roots. This is due in part to the fact that, despite technological advances, observing and measuring deep roots remains challenging. Scope Here, other reasons that explain why there are still so many fundamental unresolved questions related to deep roots are discussed. These include the fact that a number of hypotheses and models that are widely considered as verified and sufficiently robust are only partly supported by data. Evidence has accumulated that deep rooting could be a more widespread and important trait among plants than usually considered based on the share of biomass that it represents. Examples that indicate that plant roots have different structures and play different roles with respect to major biochemical cycles depending on their position within the soil profile are also examined and discussed. Conclusions Current knowledge gaps are identified and new lines of research for improving our understanding of the processes that drive deep root growth and functioning are proposed. This ultimately leads to a reflection on an alternative paradigm that could be used in the future as a unifying framework to describe and analyse deep rooting. Despite the many hurdles that pave the way to a practical understanding of deep rooting functions, it is anticipated that, in the relatively near future, increased knowledge about the deep rooting traits of a variety of plants and crops will have direct and tangible

Sedimentologic and volcanologic investigation of the deep tyrrhenian sea: preliminary result of cruise VST02

Directory of Open Access Journals (Sweden)

A. Bertagnini

2006-06-01

Full Text Available The VST02 cruise carried out in the summer of 2002 was focused at sedimentologic and volcanologic researches over selected areas of the deep portion of the Tyrrhenian sea. Chirp lines and seafloor samples were collected from the Calabrian slope surrounding Stromboli island, in the Marsili deep sea fan, in the Vavilov basin and in the Vavilov seamount. Submarine volcanic activity, both explosive and effusive, is occuring in the Stromboli edifice. Explosive submarine volcanism affects also the shallowest areas of the Vavilov seamount. Submarine carbonate lithification has been observed on the sediment-starved flanks of the Vavilov seamount. Acoustic transparent layers make up the recentmost infill of the Gortani basin, the easternmost portion of the Vavilov basin. Channels comprised of a variety of architectural elements and depositional lobes are the main elements of the Marsili deep-sea fan where, apparently, sedimentation occurs mainly through debris flow processes.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

Science.gov (United States)

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

Directory of Open Access Journals (Sweden)

Emre O. Neftci

2017-06-01

Full Text Available An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Distributed deep learning networks among institutions for medical imaging.

Science.gov (United States)

Chang, Ken; Balachandar, Niranjan; Lam, Carson; Yi, Darvin; Brown, James; Beers, Andrew; Rosen, Bruce; Rubin, Daniel L; Kalpathy-Cramer, Jayashree

2018-03-29

Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data. We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer. We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

Deep frying

NARCIS (Netherlands)

Koerten, van K.N.

2016-01-01

Deep frying is one of the most used methods in the food processing industry. Though practically any food can be fried, French fries are probably the most well-known deep fried products. The popularity of French fries stems from their unique taste and texture, a crispy outside with a mealy soft

Installation of borehole seismometer for earthquake characteristics in deep geological environment

Energy Technology Data Exchange (ETDEWEB)

Park, Dong Hee; Choi, Weon Hack; Cho, Sung Il; Chang, Chun Joong [KHNP CRI, Seoul (Korea, Republic of)

2014-10-15

Deep geological disposal is currently accepted as the most appropriate method for permanently removing spent nuclear fuel from the living sphere of humans. For implementation of deep geological disposal, we need to understand the geological changes that have taken place over the past 100,000 years, encompassing active faults, volcanic activity, elevation, ubsidence, which as yet have not been considered in assessing the site characteristics for general facilities, as well as to investigate and analyze the geological structures, fracture systems and seismic responses regarding deep geological environment about 500 meters or more underground. In regions with high seismic activity, such as Japan, the Western United States and Taiwan, borehole seismometers installed deep underground are used to monitor seismic activity during the course of seismic wave propagation at various depths and to study the stress changes due to earthquakes and analyze the connection to fault movements. Korea Hydro and Nuclear Power Co., Ltd. (KHNP) have installed the deep borehole earthquake observatory at depths of about 300 to 600 meters in order to study the seismic response characteristics in deep geological environment on June, 2014 in Andong area. This paper will show the status of deep borehole earthquake observatory and the results of background noise response characteristics of these deep borehole seismic data as a basic data analysis. We present here the status of deep borehole seismometer installation by KHNP. In order to basic data analysis for the borehole seismic observation data, this study shows the results of the orientation of seismometer and background noise characteristics by using a probability density function. Together with the ground motion data recorded by the borehole seismometers can be utilized as basic data for seismic response characteristics studies with regard to spent nuclear fuel disposal depth and as the input data for seismic hazard assessment that

Earthquake behavior at deep underground observed by three-dimensional array

International Nuclear Information System (INIS)

Komada, Hiroya; Sawada, Yoshihiro; Aoyama, Shigeo.

1989-01-01

The earthquake observation has been carried out using an eight point three-dimensional array between on-ground and the depth of about 400 m at Hosokura Mine in Miyagi prefecture, for the purpose of obtaining the basic datum on the characteristics of the seismic waves for the earthquake resistance design of the deep underground disposal facility of high level waste. The following results ware obtained. (1) The maximum accelerations at the underground are damped to about 60 % of those at on-ground horizontal and to about 70 % vertical. (2) Although the frequency characteristics of the seismic waves varies for each earthquake, the transfer characteristics of seismic waves from deep underground to on-ground is the same for each earthquake. (3) The horizontal dirrections of seismic wave incidence are similar to the directions from epicenters of each earthquake. The vertical directions of seismic wave incidence are in the range of about 3deg to 35deg from vertical line. (author)

Deep Neural Network-Based Chinese Semantic Role Labeling

Institute of Scientific and Technical Information of China (English)

ZHENG Xiaoqing; CHEN Jun; SHANG Guoqiang

2017-01-01

A recent trend in machine learning is to use deep architec-tures to discover multiple levels of features from data, which has achieved impressive results on various natural language processing (NLP) tasks. We propose a deep neural network-based solution to Chinese semantic role labeling (SRL) with its application on message analysis. The solution adopts a six-step strategy: text normalization, named entity recognition (NER), Chinese word segmentation and part-of-speech (POS) tagging, theme classification, SRL, and slot filling. For each step, a novel deep neural network - based model is designed and optimized, particularly for smart phone applications. Ex-periment results on all the NLP sub - tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost. The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requir-ing real-time response, highlighting the potential of the pro-posed solution for practical NLP systems.

Benchmarking Deep Learning Models on Large Healthcare Datasets.

Science.gov (United States)

Purushotham, Sanjay; Meng, Chuizheng; Che, Zhengping; Liu, Yan

2018-06-04

Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the 'raw' clinical time series data is used as input features to the models. Copyright © 2018 Elsevier Inc. All rights reserved.

Velocity and stress distributions of deep seismic zone under Izu-Bonin, Japan

Science.gov (United States)

Jiang, Guoming; Zhang, Guibin; Jia, Zhengyuan

2017-04-01

Deep earthquakes can provide the deep information of the Earth directly. We have collected the waveform data from 77 deep earthquakes with depth greater than 300 km under Izu-Bonin in Japan. To obtain the velocity structures of P- and S-wave, we have inversed the double-differences of travel times from deep event-pairs. These velocity anomalies can further yield the Poisson's ratio and the porosity. Our results show that the average P-wave velocity anomaly is lower 6%, however the S-wave anomaly is higher 2% than the iasp91 model. The corresponding Poisson's ratio and porosity anomaly are -24% and -4%, respectively, which suggest that the possibility of water in the deep seismic zone is very few and the porosity might be richer. To obtain the stress distribution, we have used the ISOLA method to analyse the non-double-couple components of moment tensors of 77 deep earthquakes. The focal mechanism results show that almost half of all earthquakes have larger double-couple (DC) components, but others have clear isotropic (ISO) or compensated linear vector dipole (CLVD) components. The non-double-couple components (ISO and CLVD) seem to represent the volume around a deep earthquake changes as it occurs, which could be explained the metastable olivine phase transition. All results indicate that the metastable olivine wedge (MOW) might exist in the Pacific slab under the Izu-Bonin region and the deep earthquakes might be induced by the phase change of metastable olivine.

Ploughing the deep sea floor.

Science.gov (United States)

Puig, Pere; Canals, Miquel; Company, Joan B; Martín, Jacobo; Amblas, David; Lastras, Galderic; Palanques, Albert

2012-09-13

Bottom trawling is a non-selective commercial fishing technique whereby heavy nets and gear are pulled along the sea floor. The direct impact of this technique on fish populations and benthic communities has received much attention, but trawling can also modify the physical properties of seafloor sediments, water–sediment chemical exchanges and sediment fluxes. Most of the studies addressing the physical disturbances of trawl gear on the seabed have been undertaken in coastal and shelf environments, however, where the capacity of trawling to modify the seafloor morphology coexists with high-energy natural processes driving sediment erosion, transport and deposition. Here we show that on upper continental slopes, the reworking of the deep sea floor by trawling gradually modifies the shape of the submarine landscape over large spatial scales. We found that trawling-induced sediment displacement and removal from fishing grounds causes the morphology of the deep sea floor to become smoother over time, reducing its original complexity as shown by high-resolution seafloor relief maps. Our results suggest that in recent decades, following the industrialization of fishing fleets, bottom trawling has become an important driver of deep seascape evolution. Given the global dimension of this type of fishery, we anticipate that the morphology of the upper continental slope in many parts of the world’s oceans could be altered by intensive bottom trawling, producing comparable effects on the deep sea floor to those generated by agricultural ploughing on land.

DeepPVP: phenotype-based prioritization of causative variants using deep learning

KAUST Repository

Boudellioua, Imene

2018-05-02

Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient\\'s phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.

Science.gov (United States)

Samadi, Arash; Lillicrap, Timothy P; Tweed, Douglas B

2017-03-01

Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation. Given these differences, it is unlikely that current deep learning algorithms can operate in the brain, but we that show these problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Our results also show that deep spiking networks learn much better if each neuron computes an intracellular teaching signal that reflects that cell's nonlinearity. With this mechanism, networks of spiking neurons show useful learning in synapses at least nine layers upstream from the output cells and perform well compared to other spiking networks in the literature on the MNIST digit recognition task.

Factors affecting the production of seeds in fully fertile tomatoes (Lycopersicon esculentum L. Mill. and those showing a tendency to parthenocarpy

Directory of Open Access Journals (Sweden)

Barbara Gabara

2014-01-01

Full Text Available Comparative studies on the development of the female gametophyte, pollination and fertilization in two lines of Lycopersicon esculentum, Kholodostoykye (Kh, fertile and A33 (with a tendency to parthenocarpy have revealed that seed production is affected by disturbances in embryo sac formation but mainly by its degeneration after anthesis, which is especially visible in line A33. Moreover, delayed development of some embryo sacs and incomplete pollination due to various stigma levels seem to be responsible for the diminution of seed number in line A33. Deep fluorescence of numerous pollen grains as well as whole pollen tubes in 83.3 per cent of A33 stigmas and only 24.1 per cent in the Kh line points to the heterogeneity of pollen. This could be one more reason for reduced fertility. The results of application of plant growth regulators (auxin, PCIB which affect seed production in tomato of line A33 remain inconclusive.

Evolutionary process of deep-sea bathymodiolus mussels.

Science.gov (United States)

Miyazaki, Jun-Ichi; de Oliveira Martins, Leonardo; Fujita, Yuko; Matsumoto, Hiroto; Fujiwara, Yoshihiro

2010-04-27

Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI) and NADH dehydrogenase subunit 4 (ND4) genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of symbiosis in that nutritional adaptation to the deep sea proceeded from extracellular

Communication: Diffusion constant in supercooled water as the Widom line is crossed in no man's land

Science.gov (United States)

Ni, Yicun; Hestand, Nicholas J.; Skinner, J. L.

2018-05-01

According to the liquid-liquid critical point (LLCP) hypothesis, there are two distinct phases of supercooled liquid water, namely, high-density liquid and low-density liquid, separated by a coexistence line that terminates in an LLCP. If the LLCP is real, it is located within No Man's Land (NML), the region of the metastable phase diagram that is difficult to access using conventional experimental techniques due to rapid homogeneous nucleation to the crystal. However, a recent ingenious experiment has enabled measurement of the diffusion constant deep inside NML. In the current communication, these recent measurements are compared, with good agreement, to the diffusion constant of E3B3 water, a classical water model that explicitly includes three-body interactions. The behavior of the diffusion constant as the system crosses the Widom line (the extension of the liquid-liquid coexistence line into the one-phase region) is analyzed to derive information about the presence and location of the LLCP. Calculations over a wide range of temperatures and pressures show that the new experimental measurements are consistent with an LLCP having a critical pressure of over 0.6 kbar.

Re-visiting the notion of Deep Incarnation in light of 1 Corinthians 15:28 and emergence theory

Directory of Open Access Journals (Sweden)

Wessel Bentley

2016-08-01

Full Text Available Niels Hendrik Gregersen’s ‘Deep Incarnation’ is opening up possibilities for engagementbetween science and theology. Recent discoveries, like that of Homo naledi, raise questions abouthow inclusive a Christian doctrine of Incarnation is. Is Jesus only God incarnate for Homo sapiensapiens, or is the incarnation inclusive of preceding hominid species as well? Does the incarnationstretch beyond the hominid line? This chapter engages Gregersen’s understanding of DeepIncarnation in light of 1 Corinthians 15:28 and emergence theory. It proposes that there is a directcorrelation between worldview and how we believe in the inclusive nature of divine incarnation.

Radon concentration distributions in shallow and deep groundwater around the Tachikawa fault zone.

Science.gov (United States)

Tsunomori, Fumiaki; Shimodate, Tomoya; Ide, Tomoki; Tanaka, Hidemi

2017-06-01

Groundwater radon concentrations around the Tachikawa fault zone were surveyed. The radon concentrations in shallow groundwater samples around the Tachikawa fault segment are comparable to previous studies. The characteristics of the radon concentrations on both sides of the segment are considered to have changed in response to the decrease in groundwater recharge caused by urbanization on the eastern side of the segment. The radon concentrations in deep groundwater samples collected around the Naguri and the Tachikawa fault segments are the same as those of shallow groundwater samples. However, the radon concentrations in deep groundwater samples collected from the bedrock beside the Naguri and Tachikawa fault segments are markedly higher than the radon concentrations expected from the geology on the Kanto plane. This disparity can be explained by the development of fracture zones spreading on both sides of the two segments. The radon concentration distribution for deep groundwater samples from the Naguri and the Tachikawa fault segments suggests that a fault exists even at the southern part of the Tachikawa fault line. Copyright © 2017 Elsevier Ltd. All rights reserved.

Potential Osteoporosis Recovery by Deep Sea Water through Bone Regeneration in SAMP8 Mice

Directory of Open Access Journals (Sweden)

Hen-Yu Liu

2013-01-01

Full Text Available The aim of this study is to examine the therapeutic potential of deep sea water (DSW on osteoporosis. Previously, we have established the ovariectomized senescence-accelerated mice (OVX-SAMP8 and demonstrated strong recovery of osteoporosis by stem cell and platelet-rich plasma (PRP. Deep sea water at hardness (HD 1000 showed significant increase in proliferation of osteoblastic cell (MC3T3 by MTT assay. For in vivo animal study, bone mineral density (BMD was strongly enhanced followed by the significantly increased trabecular numbers through micro-CT examination after a 4-month deep sea water treatment, and biochemistry analysis showed that serum alkaline phosphatase (ALP activity was decreased. For stage-specific osteogenesis, bone marrow-derived stromal cells (BMSCs were harvested and examined. Deep sea water-treated BMSCs showed stronger osteogenic differentiation such as BMP2, RUNX2, OPN, and OCN, and enhanced colony forming abilities, compared to the control group. Interestingly, most untreated OVX-SAMP8 mice died around 10 months; however, approximately 57% of DSW-treated groups lived up to 16.6 months, a life expectancy similar to the previously reported life expectancy for SAMR1 24 months. The results demonstrated the regenerative potentials of deep sea water on osteogenesis, showing that deep sea water could potentially be applied in osteoporosis therapy as a complementary and alternative medicine (CAM.

Genetic diversity of archaea in deep-sea hydrothermal vent environments.

Science.gov (United States)

Takai, K; Horikoshi, K

1999-08-01

Molecular phylogenetic analysis of naturally occurring archaeal communities in deep-sea hydrothermal vent environments was carried out by PCR-mediated small subunit rRNA gene (SSU rDNA) sequencing. As determined through partial sequencing of rDNA clones amplified with archaea-specific primers, the archaeal populations in deep-sea hydrothermal vent environments showed a great genetic diversity, and most members of these populations appeared to be uncultivated and unidentified organisms. In the phylogenetic analysis, a number of rDNA sequences obtained from deep-sea hydrothermal vents were placed in deep lineages of the crenarchaeotic phylum prior to the divergence of cultivated thermophilic members of the crenarchaeota or between thermophilic members of the euryarchaeota and members of the methanogen-halophile clade. Whole cell in situ hybridization analysis suggested that some microorganisms of novel phylotypes predicted by molecular phylogenetic analysis were likely present in deep-sea hydrothermal vent environments. These findings expand our view of the genetic diversity of archaea in deep-sea hydrothermal vent environments and of the phylogenetic organization of archaea.

Automated analysis of high-content microscopy data with deep learning.

Science.gov (United States)

Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

2017-04-18

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

Deep learning in bioinformatics.

Science.gov (United States)

Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

2017-09-01

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Application of AMT in detecting deep geological structures in Lejia district of Xiangshan uranium ore field

International Nuclear Information System (INIS)

Duan Shuxin; Liu Hu

2014-01-01

In recent years, exploration in Xiangshan uranium ore field shows that the intersection of faults and the interface of different rock formation and the basement is an important sign of deep ore- prospecting. In order to evaluate deep uranium resource in Lejia district, audio magnetotelluric method (AMT) was undertaken to carry out profile investigation. With that method, we discerned the interface of different rock formation and the basement successfully, and faults in the deep, which provides a good basis for the prediction of deep uranium resource. Drilling results show that AMT method has an obvious advantage in detecting deep geological structures in Xiangshan. (authors)

[Fasciocutaneous flap reliable by deep femoral artery perforator for the treatment of ischial pressure ulcers].

Science.gov (United States)

Gebert, L; Boucher, F; Lari, A; Braye, F; Mojallal, A; Ismaïl, M

2018-04-01

The surgical management of pressure ulcers in the paraplegic or quadriplegic population is marked by the high risk of recurrence in the long-term. In the current era of perforator flaps, newer reconstructive options are available for the management of pressure ulcers, decreasing the need to use the classically described muscular or musculocutaneous locoregional flaps. The coverage of ischial sores described in this article by a pedicled flap based on a deep femoral artery perforator, appears to be an effective first-line reconstructive option for the management of limited size pressure ulcers. A number of fifteen paraplegic or quadriplegic patients having at least one ischial bed sore with underlying osteomyelitis were included in this series. The approximate location of the deep femoral artery perforator was initially identified using the "The Atlas of the perforator arteries of the skin, the trunk and limbs", which was confirmed, with the use of a Doppler device. A fasciocutaneous transposition flap was elevated, with the pivot point based on the cutaneous bridge centered on the perforator, and then transposed to cover the area of tissue loss. The donor site was closed primarily. A total of fifteen patients were operated from November 2015 to November 2016. The series comprised of 16 first presentations of a stage 4 pressure ulcers associated with underlying osteomyelitis that were subsequently reconstructed by the pedicled deep femoral artery perforator flap. The healing rate and functional results were both satisfactory. Fasciocutaneous flap reliable by deep femoral artery perforator appears to have a promising role in the treatment of ischial pressure sores. It is an attractive option to spare the use of musculocutaneous flaps in the area. Thus this flap could be used as a first-line option to cover ischial pressure ulcers of limited size. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

An adaptive sampling scheme for deep-penetration calculation

International Nuclear Information System (INIS)

Wang, Ruihong; Ji, Zhicheng; Pei, Lucheng

2013-01-01

As we know, the deep-penetration problem has been one of the important and difficult problems in shielding calculation with Monte Carlo Method for several decades. In this paper, an adaptive Monte Carlo method under the emission point as a sampling station for shielding calculation is investigated. The numerical results show that the adaptive method may improve the efficiency of the calculation of shielding and might overcome the under-estimation problem easy to happen in deep-penetration calculation in some degree

LINE FUSION GENES: a database of LINE expression in human genes

Directory of Open Access Journals (Sweden)

Park Hong-Seog

2006-06-01

Full Text Available Abstract Background Long Interspersed Nuclear Elements (LINEs are the most abundant retrotransposons in humans. About 79% of human genes are estimated to contain at least one segment of LINE per transcription unit. Recent studies have shown that LINE elements can affect protein sequences, splicing patterns and expression of human genes. Description We have developed a database, LINE FUSION GENES, for elucidating LINE expression throughout the human gene database. We searched the 28,171 genes listed in the NCBI database for LINE elements and analyzed their structures and expression patterns. The results show that the mRNA sequences of 1,329 genes were affected by LINE expression. The LINE expression types were classified on the basis of LINEs in the 5' UTR, exon or 3' UTR sequences of the mRNAs. Our database provides further information, such as the tissue distribution and chromosomal location of the genes, and the domain structure that is changed by LINE integration. We have linked all the accession numbers to the NCBI data bank to provide mRNA sequences for subsequent users. Conclusion We believe that our work will interest genome scientists and might help them to gain insight into the implications of LINE expression for human evolution and disease. Availability http://www.primate.or.kr/line

The sandfly Lutzomyia longipalpis LL5 embryonic cell line has active Toll and Imd pathways and shows immune responses to bacteria, yeast and Leishmania.

Science.gov (United States)

Tinoco-Nunes, Bruno; Telleria, Erich Loza; da Silva-Neves, Monique; Marques, Christiane; Azevedo-Brito, Daisy Aline; Pitaluga, André Nóbrega; Traub-Csekö, Yara Maria

2016-04-20

Lutzomyia longipalpis is the main vector of visceral leishmaniasis in Latin America. Sandfly immune responses are poorly understood. In previous work we showed that these vector insects respond to bacterial infections by modulating a defensin gene expression and activate the Imd pathway in response to Leishmania infection. Aspects of innate immune pathways in insects (including mosquito vectors of human diseases) have been revealed by studying insect cell lines, and we have previously demonstrated antiviral responses in the L. longipalpis embryonic cell line LL5. The expression patterns of antimicrobial peptides (AMPs) and transcription factors were evaluated after silencing the repressors of the Toll pathway (cactus) and Imd pathway (caspar). AMPs and transcription factor expression patterns were also evaluated after challenge with heat-killed bacteria, heat-killed yeast, or live Leishmania. These studies showed that LL5 cells have active Toll and Imd pathways, since they displayed an increased expression of AMP genes following silencing of the repressors cactus and caspar, respectively. These pathways were also activated by challenges with bacteria, yeast and Leishmania infantum chagasi. We demonstrated that L. longipalpis LL5 embryonic cells respond to immune stimuli and are therefore a good model to study the immunological pathways of this important vector of leishmaniasis.

Endovascular treatment of iliofemoral deep vein thrombosis in pregnancy using US-guided percutaneous aspiration thrombectomy.

Science.gov (United States)

Gedikoglu, Murat; Oguzkurt, Levent

2017-01-01

We aimed to describe ultrasonography (US)-guided percutaneous aspiration thrombectomy in pregnant women with iliofemoral deep vein thrombosis. This study included nine pregnant women with acute and subacute iliofemoral deep vein thrombosis, who were severe symptomatic cases with massive swelling and pain of the leg. Patients were excluded from the study if they had only femoropopliteal deep vein thrombosis or mild symptoms of deep vein thrombosis. US-guided percutaneous aspiration thrombectomy was applied to achieve thrombus removal and uninterrupted venous flow. The treatment was considered successful if there was adequate venous patency and symptomatic relief. Complete or significant thrombus removal and uninterrupted venous flow from the puncture site up to the iliac veins were achieved in all patients at first intervention. Complete relief of leg pain was achieved immediately in seven patients (77.8%). Two patients (22.2%) had a recurrence of thrombosis in the first week postintervention. One of them underwent a second intervention, where percutaneous aspiration thrombectomy was performed again with successful removal of thrombus and establishment of in line flow. Two patients were lost to follow-up after birth. None of the remaining seven patients had rethrombosis throughout the postpartum period. Symptomatic relief was detected clinically in these patients. Endovascular treatment with US-guided percutaneous aspiration thrombectomy can be considered as a safe and effective way to remove thrombus from the deep veins in pregnant women with acute and subacute iliofemoral deep vein thrombosis.

Deep learning relevance

DEFF Research Database (Denmark)

Lioma, Christina; Larsen, Birger; Petersen, Casper

2016-01-01

train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared...... to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all....

E-beam irradiation effect on CdSe/ZnSe QD formation by MBE: deep level transient spectroscopy and cathodoluminescence studies

International Nuclear Information System (INIS)

Kozlovsky, V I; Litvinov, V G; Sadofyev, Yu G

2004-01-01

CdSe/ZnSe structures containing 1 or 15 thin (3-5 monolayers) CdSe layers were studied by cathodoluminescence (CL) and deep level transient spectroscopy (DLTS). The DLTS spectra consisted of peaks from deep levels (DLs) and an additional intense peak due to electron emission from the ground quantized level in the CdSe layers. Activation energy of this additional peak correlated with an energy of the CdSe-layer emission line in the CL spectra. Electron-beam irradiation of the structure during the growth process was found to influence the DLTS and CL spectra of the CdSe layers, shifting the CdSe-layer emission line to the long-wave side. The obtained results are explained using the assumption that e-beam irradiation stimulates the formation of quantum dots of various sizes in the CdSe layers

Active Cooling of Oil after Deep-frying.

Science.gov (United States)

Totani, Nagao; Yasaki, Naoko; Doi, Rena; Hasegawa, Etsuko

2017-10-01

Oil used for deep-frying is often left to stand after cooking. A major concern is oxidation during standing that might be avoidable, especially in the case of oil used repeatedly for commercial deep-frying as this involves large volumes that are difficult to cool in a conventional fryer. This paper describes a method to minimize oil oxidation. French fries were deep-fried and the oil temperature decreased in a manner typical for a commercial deep-fryer. The concentration of polar compounds generated from thermally oxidized oil remarkably increased at temperature higher than 100°C but little oxidation occurred below 60°C. Heating the oil showed that the peroxide and polar compound content did not increase when the oil was actively cooled using a running water-cooled Graham-type condenser system to cool the oil from 180°C to room temperature within 30 min. When French fries were fried and the oil was then immediately cooled using the condenser, the polar compound content during cooling did not increase. Our results demonstrate that active cooling of heated oil is simple and quite effective for inhibiting oxidation.

Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval

Directory of Open Access Journals (Sweden)

Lijuan Duan

2017-01-01

Full Text Available Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI, to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.

Feedback control in deep drawing based on experimental datasets

Science.gov (United States)

Fischer, P.; Heingärtner, J.; Aichholzer, W.; Hortig, D.; Hora, P.

2017-09-01

In large-scale production of deep drawing parts, like in automotive industry, the effects of scattering material properties as well as warming of the tools have a significant impact on the drawing result. In the scope of the work, an approach is presented to minimize the influence of these effects on part quality by optically measuring the draw-in of each part and adjusting the settings of the press to keep the strain distribution, which is represented by the draw-in, inside a certain limit. For the design of the control algorithm, a design of experiments for in-line tests is used to quantify the influence of the blank holder force as well as the force distribution on the draw-in. The results of this experimental dataset are used to model the process behavior. Based on this model, a feedback control loop is designed. Finally, the performance of the control algorithm is validated in the production line.

Top Tagging by Deep Learning Algorithm

CERN Document Server

Akil, Ali

2015-01-01

In this report I will show the application of a deep learning algorithm on a Monte Carlo simulation sample to test its performance in tagging hadronic decays of boosted top quarks and compare what we get with the results of the application of some other algorithms.

Effects of synchrotron radiation spectrum energy on polymethyl methacrylate photosensitivity to deep x-ray lithography

International Nuclear Information System (INIS)

Mekaru, Harutaka; Utsumi, Yuichi; Hattori, Tadashi

2003-01-01

Since X-ray lithography requires a high photon flux to achieve deep resist exposure, a synchrotron radiation beam, which is not monochromatized, is generally used as a light source. If the synchrotron radiation beam is monochromatized, photon flux will decrease rapidly. Because of this reason, the wavelength dependence of the resist sensitivity has not been investigated for deep X-ray lithography. Measuring the spectrum of a white beam with a Si solid-state detector (SSD) is difficult because a white beam has a high intensity and an SSD has a high sensitivity. We were able to measure the spectrum and the photocurrent of a white beam from a beam line used for deep X-ray lithography by keeping the ring current below 0.05 mA. We evaluated the characteristics of the output beam based on the measured spectrum and photocurrent, and used them to investigate the relationship between the total exposure energy and the dose-processing depth with polymethyl methacrylate (PMMA). We found that it is possible to guess the processing depth of PMMA from the total exposure energy in deep X-ray lithography. (author)

Periodic arrays of deep nanopores made in silicon with reactive ion etching and deep UV lithography

International Nuclear Information System (INIS)

Woldering, Leon A; Tjerkstra, R Willem; Vos, Willem L; Jansen, Henri V; Setija, Irwan D

2008-01-01

We report on the fabrication of periodic arrays of deep nanopores with high aspect ratios in crystalline silicon. The radii and pitches of the pores were defined in a chromium mask by means of deep UV scan and step technology. The pores were etched with a reactive ion etching process with SF 6 , optimized for the formation of deep nanopores. We have realized structures with pitches between 440 and 750 nm, pore diameters between 310 and 515 nm, and depth to diameter aspect ratios up to 16. To the best of our knowledge, this is the highest aspect ratio ever reported for arrays of nanopores in silicon made with a reactive ion etching process. Our experimental results show that the etching rate of the nanopores is aspect-ratio-dependent, and is mostly influenced by the angular distribution of the etching ions. Furthermore we show both experimentally and theoretically that, for sub-micrometer structures, reducing the sidewall erosion is the best way to maximize the aspect ratio of the pores. Our structures have potential applications in chemical sensors, in the control of liquid wetting of surfaces, and as capacitors in high-frequency electronics. We demonstrate by means of optical reflectivity that our high-quality structures are very well suited as photonic crystals. Since the process studied is compatible with existing CMOS semiconductor fabrication, it allows for the incorporation of the etched arrays in silicon chips

Dalia integrated production bundle (IPB): an innovative riser solution for deep water fields

Energy Technology Data Exchange (ETDEWEB)

Reals, Th Boscals de; Gloaguen, M.; Roche, F. [Total E and P (Angola); Marion, A.; Poincheval, A. [Technip, Paris (France)

2008-07-01

The Dalia field is located 210 km north west of Luanda (Angola), about 140 km from shore in 1400 meter water-depth. It was the second major discovery out of 15 made in the block 17 operated by Total. The Dalia Umbilical, Flow lines and Risers EPCI Contract was awarded in 2003. The sea-line network to connect and control the 71 wells and 9 manifolds consist of the following: 40 km of insulated pipe in pipe (12 inches into 17 inches) production flow lines; 45 km of 12 inches water and gas injection lines; 6 off 1.7 km flexible water and gas injection risers; 8 off 1.65 km flexible Integrated Production Bundle (IPB) risers; 75 km of control umbilicals. The flow assurance and associated insulation requirement of the production transport system was one of the main challenges of the project. With a crude temperature of 45 deg C at the wellhead and the required minimum temperature of 35 deg C on arrival at the FPSO, this problem was complex. Understanding that, due to the Joule Thompson effect of the riser gas lift, a 'built in' loss of about 5 deg C is induced and together with further losses through the sub sea pipelines, some up to 6 km long, the agreed solution was 'pipe in pipe' for the production flow lines. The innovative flexible IPB riser, incorporating gas lift and heating to keep the fluid temperature above hydrate formation zone, was the selected riser solution. The IPB is new technology for deep water, developed by Technip for Dalia, and consists of a 12 inches nominal central flexible, surrounded by layers of heat tracing cables, small bore gas lift lines, optical fibres and many insulation layers with an Overall Heat Transfer Coefficient of approximately 3,4 W/m{sup 2}K. After an earlier research and development programme, a further extensive qualification programme was conducted during the course of the project, culminating with the deep water testing phase offshore Brazil. The IPB was then approved for fabrication and installation

STIMULATION TECHNOLOGIES FOR DEEP WELL COMPLETIONS

Energy Technology Data Exchange (ETDEWEB)

Stephen Wolhart

2003-06-01

The Department of Energy (DOE) is sponsoring a Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies is conducting a project to evaluate the stimulation of deep wells. The objective of the project is to assess U.S. deep well drilling & stimulation activity, review rock mechanics & fracture growth in deep, high pressure/temperature wells and evaluate stimulation technology in several key deep plays. Phase 1 was recently completed and consisted of assessing deep gas well drilling activity (1995-2007) and an industry survey on deep gas well stimulation practices by region. Of the 29,000 oil, gas and dry holes drilled in 2002, about 300 were drilled in the deep well; 25% were dry, 50% were high temperature/high pressure completions and 25% were simply deep completions. South Texas has about 30% of these wells, Oklahoma 20%, Gulf of Mexico Shelf 15% and the Gulf Coast about 15%. The Rockies represent only 2% of deep drilling. Of the 60 operators who drill deep and HTHP wells, the top 20 drill almost 80% of the wells. Six operators drill half the U.S. deep wells. Deep drilling peaked at 425 wells in 1998 and fell to 250 in 1999. Drilling is expected to rise through 2004 after which drilling should cycle down as overall drilling declines.

Deep Structures of The Angola Margin

Science.gov (United States)

Moulin, M.; Contrucci, I.; Olivet, J.-L.; Aslanian, D.; Géli, L.; Sibuet, J.-C.

1 Ifremer Centre de Brest, DRO/Géosciences Marines, B.P. 70, 29280 Plouzané cedex (France) mmoulin@ifremer.fr/Fax : 33 2 98 22 45 49 2 Université de Bretagne Occidentale, Institut Universitaire Europeen de la Mer, Place Nicolas Copernic, 29280 Plouzane (France) 3 Total Fina Elf, DGEP/GSR/PN -GEOLOGIE, 2,place de la Coupole-La Defense 6, 92078 Paris la Defense Cedex Deep reflection and refraction seismic data were collected in April 2000 on the West African margin, offshore Angola, within the framework of the Zaiango Joint Project, conducted by Ifremer and Total Fina Elf Production. Vertical multichannel reflection seismic data generated by a « single-bubble » air gun array array (Avedik et al., 1993) were recorded on a 4.5 km long, digital streamer, while refraction and wide angle reflection seismic data were acquired on OBSs (Ocean Bottom Seismometers). Despite the complexity of the margin (5 s TWT of sediment, salt tectonics), the combination of seismic reflection and refraction methods results in an image and a velocity model of the ground structures below the Aptian salt layer. Three large seismic units appear in the reflection seismic section from the deep part on the margin under the base of salt. The upper seismic unit is layered with reflectors parallel to the base of the salt ; it represents unstructured sediments, filling a basin. The middle unit is seismically transparent. The lower unit is characterized by highly energetic reflectors. According to the OBS refraction data, these two units correspond to the continental crust and the base of the high energetic unit corresponds to the Moho. The margin appears to be divided in 3 domains, from east to west : i) a domain with an unthinned, 30 km thick, continental crust ; ii) a domain located between the hinge line and the foot of the continental slope, where the crust thins sharply, from 30 km to less than 7 km, this domain is underlain by an anormal layer with velocities comprising between 7,2 and 7

Bidirectional Nonnegative Deep Model and Its Optimization in Learning

Directory of Open Access Journals (Sweden)

Xianhua Zeng

2016-01-01

Full Text Available Nonnegative matrix factorization (NMF has been successfully applied in signal processing as a simple two-layer nonnegative neural network. Projective NMF (PNMF with fewer parameters was proposed, which projects a high-dimensional nonnegative data onto a lower-dimensional nonnegative subspace. Although PNMF overcomes the problem of out-of-sample of NMF, it does not consider the nonlinear characteristic of data and is only a kind of narrow signal decomposition method. In this paper, we combine the PNMF with deep learning and nonlinear fitting to propose a bidirectional nonnegative deep learning (BNDL model and its optimization learning algorithm, which can obtain nonlinear multilayer deep nonnegative feature representation. Experiments show that the proposed model can not only solve the problem of out-of-sample of NMF but also learn hierarchical nonnegative feature representations with better clustering performance than classical NMF, PNMF, and Deep Semi-NMF algorithms.

Deep learning in TMVA Benchmarking Benchmarking TMVA DNN Integration of a Deep Autoencoder

CERN Document Server

Huwiler, Marc

2017-01-01

The TMVA library in ROOT is dedicated to multivariate analysis, and in partic- ular oers numerous machine learning algorithms in a standardized framework. It is widely used in High Energy Physics for data analysis, mainly to perform regression and classication. To keep up to date with the state of the art in deep learning, a new deep learning module was being developed this summer, oering deep neural net- work, convolutional neural network, and autoencoder. TMVA did not have yet any autoencoder method, and the present project consists in implementing the TMVA autoencoder class based on the deep learning module. It also includes some bench- marking performed on the actual deep neural network implementation, in comparison to the Keras framework with Tensorflow and Theano backend.

Al x Ga1‑ x N-based semipolar deep ultraviolet light-emitting diodes

Science.gov (United States)

Akaike, Ryota; Ichikawa, Shuhei; Funato, Mitsuru; Kawakami, Yoichi

2018-06-01

Deep ultraviolet (UV) emission from Al x Ga1‑ x N-based light-emitting diodes (LEDs) fabricated on semipolar (1\\bar{1}02) (r-plane) AlN substrates is presented. The growth conditions are optimized. A high NH3 flow rate during metalorganic vapor phase epitaxy yields atomically flat Al y Ga1‑ y N (y > x) on which Al x Ga1‑ x N/Al y Ga1‑ y N multiple quantum wells with abrupt interfaces and good periodicity are fabricated. The fabricated r-Al x Ga1‑ x N-based LED emits at 270 nm, which is in the germicidal wavelength range. Additionally, the emission line width is narrow, and the peak wavelength is stable against the injection current, so the semipolar LED shows promise as a UV emitter.

Deep-towed CSEM survey of gas hydrates in the Gulf of Mexico

Science.gov (United States)

Kannberg, P.; Constable, S.

2017-12-01

Controlled source electromagnetic (CSEM) surveys are increasingly being used to remotely detect hydrate deposits in seafloor sediments. CSEM methods are sensitive to sediment pore space resistivity, such as when electrically resistive hydrate displaces the electrically conductive pore fluid, increasing the bulk resistivity of the sediment. In July 2017, a two-week research cruise using an upgraded and expanded "Vulcan" towed receiver system collected over 250 line km of data at four sites in the Gulf of Mexico (GoM) thought to have hydrate bearing sediments. Hydrate bearing horizons at the survey sites ranged from 400-700 m below seafloor. Modeling suggested an array with source receiver offsets of up to 1600 m would be needed to properly image the deep hydrate. A deep towed electromagnetic transmitter outputting 270 Amps was towed 100 m above seafloor. Six Vulcan receivers, each recording three-axis electric field data, were towed at 200 m intervals from 600-1600 m behind the transmitter. The four sites surveyed, Walker Ridge 313, Orca Basin, Mad Dog, and Green Canyon 955, are associated with the upcoming GOM^2 coring operation scheduled for 2020. Wells at WR313 and GC955 were logged as part of a joint industry drilling project in 2009 and will be used to ground truth our inversion results. In 2008, WR313 and GC955 were surveyed using traditional CSEM seafloor receivers, accompanied by a single prototype Vulcan towed receiver. This prior survey will allow comparison of results from a seafloor receiver survey with those from a towed receiver survey. Seismic data has been collected at all the sites, which will be used to constrain inversions. In addition to the four hydrate sites surveyed, two lines were towed over Green Knoll, a deep-water salt dome located between Mad Dog and GC955. Presented here are initial results from our recent cruise.

Deep Drawing of High-Strength Tailored Blanks by Using Tailored Tools

Directory of Open Access Journals (Sweden)

Thomas Mennecart

2016-01-01

Full Text Available In most forming processes based on tailored blanks, the tool material remains the same as that of sheet metal blanks without tailored properties. A novel concept of lightweight construction for deep drawing tools is presented in this work to improve the forming behavior of tailored blanks. The investigations presented here deal with the forming of tailored blanks of dissimilar strengths using tailored dies made of two different materials. In the area of the steel blank with higher strength, typical tool steel is used. In the area of the low-strength steel, a hybrid tool made out of a polymer and a fiber-reinforced surface replaces the steel half. Cylindrical cups of DP600/HX300LAD are formed and analyzed regarding their formability. The use of two different halves of tool materials shows improved blank thickness distribution, weld-line movement and pressure distribution compared to the use of two steel halves. An improvement in strain distribution is also observed by the inclusion of springs in the polymer side of tools, which is implemented to control the material flow in the die. Furthermore, a reduction in tool weight of approximately 75% can be achieved by using this technique. An accurate finite element modeling strategy is developed to analyze the problem numerically and is verified experimentally for the cylindrical cup. This strategy is then applied to investigate the thickness distribution and weld-line movement for a complex geometry, and its transferability is validated. The inclusion of springs in the hybrid tool leads to better material flow, which results in reduction of weld-line movement by around 60%, leading to more uniform thickness distribution.

Performance of deep geothermal energy systems

Science.gov (United States)

Manikonda, Nikhil

Geothermal energy is an important source of clean and renewable energy. This project deals with the study of deep geothermal power plants for the generation of electricity. The design involves the extraction of heat from the Earth and its conversion into electricity. This is performed by allowing fluid deep into the Earth where it gets heated due to the surrounding rock. The fluid gets vaporized and returns to the surface in a heat pipe. Finally, the energy of the fluid is converted into electricity using turbine or organic rankine cycle (ORC). The main feature of the system is the employment of side channels to increase the amount of thermal energy extracted. A finite difference computer model is developed to solve the heat transport equation. The numerical model was employed to evaluate the performance of the design. The major goal was to optimize the output power as a function of parameters such as thermal diffusivity of the rock, depth of the main well, number and length of lateral channels. The sustainable lifetime of the system for a target output power of 2 MW has been calculated for deep geothermal systems with drilling depths of 8000 and 10000 meters, and a financial analysis has been performed to evaluate the economic feasibility of the system for a practical range of geothermal parameters. Results show promising an outlook for deep geothermal systems for practical applications.

[Deep continuous palliative sedation in the Opinion adopted by the Italian National Bioethics Committee (Deep palliative sedation)].

Science.gov (United States)

Cembrani, Fabio

2016-01-01

The Author examines the recent opinion delivered by the Italian National Committee for Bioethics on deep palliative sedation. In particular, it examines its strengths and ample shade that show its ideology, once again, in contrast with the right of every human being to die with dignity.

Deep inelastic singlet structure functions and scaling violation

Energy Technology Data Exchange (ETDEWEB)

Wen-zhu, Li; Bing-xun, Hu

1984-02-01

The flavour singlet structure functions of deep inelastic scattering processes can yield more decisive tests of QCD than the non-singlet. We give analytical expression for flavour singlet structure functions through analysing the lepton-nucleon deep inelastic scattering processes by means of QCD and using Jacobi polynomials. This expression contains 4 to 5 parameters and shows the changes of the singlet structure functions with x and Q/sup 2/ very well. In QCD leading order, the conclusion is in reasonable agreement with experimental data.

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks

Directory of Open Access Journals (Sweden)

Hesham Mostafa

2017-09-01

Full Text Available Artificial neural networks (ANNs trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks.

Science.gov (United States)

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.

Deep subsurface microbial processes

Science.gov (United States)

Lovley, D.R.; Chapelle, F.H.

1995-01-01

Information on the microbiology of the deep subsurface is necessary in order to understand the factors controlling the rate and extent of the microbially catalyzed redox reactions that influence the geophysical properties of these environments. Furthermore, there is an increasing threat that deep aquifers, an important drinking water resource, may be contaminated by man's activities, and there is a need to predict the extent to which microbial activity may remediate such contamination. Metabolically active microorganisms can be recovered from a diversity of deep subsurface environments. The available evidence suggests that these microorganisms are responsible for catalyzing the oxidation of organic matter coupled to a variety of electron acceptors just as microorganisms do in surface sediments, but at much slower rates. The technical difficulties in aseptically sampling deep subsurface sediments and the fact that microbial processes in laboratory incubations of deep subsurface material often do not mimic in situ processes frequently necessitate that microbial activity in the deep subsurface be inferred through nonmicrobiological analyses of ground water. These approaches include measurements of dissolved H2, which can predict the predominant microbially catalyzed redox reactions in aquifers, as well as geochemical and groundwater flow modeling, which can be used to estimate the rates of microbial processes. Microorganisms recovered from the deep subsurface have the potential to affect the fate of toxic organics and inorganic contaminants in groundwater. Microbial activity also greatly influences 1 the chemistry of many pristine groundwaters and contributes to such phenomena as porosity development in carbonate aquifers, accumulation of undesirably high concentrations of dissolved iron, and production of methane and hydrogen sulfide. Although the last decade has seen a dramatic increase in interest in deep subsurface microbiology, in comparison with the study of

Detecting atrial fibrillation by deep convolutional neural networks.

Science.gov (United States)

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

2018-02-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Deep and surface learning in problem-based learning: a review of the literature.

Science.gov (United States)

Dolmans, Diana H J M; Loyens, Sofie M M; Marcq, Hélène; Gijbels, David

2016-12-01

In problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested and try to understand what is being studied. This review investigates: (1) the effects of PBL on students' deep and surface approaches to learning, (2) whether and why these effects do differ across (a) the context of the learning environment (single vs. curriculum wide implementation), and (b) study quality. Studies were searched dealing with PBL and students' approaches to learning. Twenty-one studies were included. The results indicate that PBL does enhance deep learning with a small positive average effect size of .11 and a positive effect in eleven of the 21 studies. Four studies show a decrease in deep learning and six studies show no effect. PBL does not seem to have an effect on surface learning as indicated by a very small average effect size (.08) and eleven studies showing no increase in the surface approach. Six studies demonstrate a decrease and four an increase in surface learning. It is concluded that PBL does seem to enhance deep learning and has little effect on surface learning, although more longitudinal research using high quality measurement instruments is needed to support this conclusion with stronger evidence. Differences cannot be explained by the study quality but a curriculum wide implementation of PBL has a more positive impact on the deep approach (effect size .18) compared to an implementation within a single course (effect size of -.05). PBL is assumed to enhance active learning and students' intrinsic motivation, which enhances deep learning. A high perceived workload and assessment that is perceived as not rewarding deep learning are assumed to enhance surface learning.

Comparison of Chamfer and Deep Chamfer Preparation Designs on the Fracture Resistance of Zirconia Core Restorations

Directory of Open Access Journals (Sweden)

Ezatollah Jalalian

2011-06-01

Full Text Available Background and aims. One of the major problems of all-ceramic restorations is their probable fracture under occlusal force. The aim of the present in vitro study was to compare the effect of two marginal designs (chamfer and deep chamfer on the fracture resistance of all-ceramic restorations, CERCON. Materials and methods. This in vitro study was carried out with single-blind experimental technique. One stainless steel die with 50’ chamfer finish line design (0.8 mm deep was prepared using a milling machine. Ten epoxy resin dies were prepared. The same die was retrieved and 50' chamfer was converted into a deep chamfer design (1 mm. Again ten epoxy resin dies were prepared from the deep chamfer die. Zirconia cores with 0.4 mm thickness and 35 µm cement space were fabricated on the epoxy resin dies (10 chamfer and 10 deep chamfer samples. The zirconia cores were cemented on the epoxy resin dies and underwent a fracture test with a universal testing machine and the samples were investigated from the point of view of the origin of the failure. Results. The mean values of fracture resistance for deep chamfer and chamfer samples were 1426.10±182.60 and 991.75±112.00 N, respectively. Student’s t-test revealed statistically significant differences between the groups. Conclusion. The results indicated a relationship between the marginal design of zirconia cores and their fracture resistance. A deep chamfer margin improved the biomechanical performance of posterior single zirconia crown restorations, which might be attributed to greater thickness and rounded internal angles in deep chamfer margins.

Radio-active waste disposal and deep-sea biology

International Nuclear Information System (INIS)

Rice, A.L.

1978-01-01

The deep-sea has been widely thought of as a remote, sparsely populated, and biologically inactive environment, well suited to receive the noxious products of nuclear fission processes. Much of what is known of abyssal biology tends to support this view, but there are a few disquieting contra-indications. The realisation, in recent years, that many animal groups show a previously unsuspected high species diversity in the deep-sea emphasized the paucity of our knowledge of this environment. More dramatically, the discovery of a large, active, and highly mobile abysso-bentho-pelagic fauna changed the whole concept of abyssal life. Finally, while there is little evidence for the existence of vertical migration patterns linking the deep-sea bottom communities with those of the overlying water layers, there are similarly too few negative results for the possibility of such transport mechanisms to be dismissed. In summary, biological knowledge of the abyss is insufficient to answer the questions raised in connection with deep-sea dumping, but in the absence of adequate answers it might be dangerous to ignore the questions

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

Science.gov (United States)

Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

2016-07-05

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

Pathogenesis of deep endometriosis.

Science.gov (United States)

Gordts, Stephan; Koninckx, Philippe; Brosens, Ivo

2017-12-01

The pathophysiology of (deep) endometriosis is still unclear. As originally suggested by Cullen, change the definition "deeper than 5 mm" to "adenomyosis externa." With the discovery of the old European literature on uterine bleeding in 5%-10% of the neonates and histologic evidence that the bleeding represents decidual shedding, it is postulated/hypothesized that endometrial stem/progenitor cells, implanted in the pelvic cavity after birth, may be at the origin of adolescent and even the occasionally premenarcheal pelvic endometriosis. Endometriosis in the adolescent is characterized by angiogenic and hemorrhagic peritoneal and ovarian lesions. The development of deep endometriosis at a later age suggests that deep infiltrating endometriosis is a delayed stage of endometriosis. Another hypothesis is that the endometriotic cell has undergone genetic or epigenetic changes and those specific changes determine the development into deep endometriosis. This is compatible with the hereditary aspects, and with the clonality of deep and cystic ovarian endometriosis. It explains the predisposition and an eventual causal effect by dioxin or radiation. Specific genetic/epigenetic changes could explain the various expressions and thus typical, cystic, and deep endometriosis become three different diseases. Subtle lesions are not a disease until epi(genetic) changes occur. A classification should reflect that deep endometriosis is a specific disease. In conclusion the pathophysiology of deep endometriosis remains debated and the mechanisms of disease progression, as well as the role of genetics and epigenetics in the process, still needs to be unraveled. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Deep bite malocclusion: exploration of the skeletal and dental factors

International Nuclear Information System (INIS)

Bhateja, N.K.; Fida, M.; Shaikh, A.

2016-01-01

Correction of deep bite is crucial for maintenance of dental hard and soft tissue structures and for prevention of temporomandibular joint disorders. Exploration of underlying skeletal and dental factors is essential for efficient and individualized treatment planning. To date etiological factors of dental and skeletal deep bite have not been explored in Pakistani orthodontic patients. The objectives of this study were to explore frequencies of dental and skeletal etiological factors in deep bite patients and to determine correlations amongst dental and skeletal etiological factors of deep bite. Methods: The study included a total of 113 subjects (males=35; females=78) with no craniofacial syndromes or prior orthodontic treatment. Pre-treatment orthodontic records were used to evaluate various dental and skeletal parameters. Descriptive statistics of each parameter were calculated. The various study parameters were correlated using Pearson's Correlation. Results: Deep curve of Spee was most frequently seen factor of dental deep bite (72.6%), followed by increased coronal length of upper incisors (28.3%), retroclined upper incisors (17.7%), retroclined lower incisors (8%) and increased coronal length of lower incisors (5.3%). Decreased gonial angle was most commonly found factor of skeletal deep bite (43.4%), followed by decreased mandibular plane angle (27.4%) and maxillary plane's clockwise rotation (26.5%). Frankfort mandibular plane angle and gonial angle showed a strong positive correlation (r=0.66, p=0.000). Conclusions: Reduced gonial angle is most frequently seen skeletal factor, signifying the importance of angulation and growth of ramus in development of deep bite. Deep curve of Spee is most frequently seen dental etiological component in deep bite subjects, hence signifying the importance of intruding the lower anterior teeth. (author)

Global diversity and biogeography of deep-sea pelagic prokaryotes

KAUST Repository

Salazar, Guillem

2015-08-07

The deep-sea is the largest biome of the biosphere, and contains more than half of the whole ocean\\'s microbes. Uncovering their general patterns of diversity and community structure at a global scale remains a great challenge, as only fragmentary information of deep-sea microbial diversity exists based on regional-scale studies. Here we report the first globally comprehensive survey of the prokaryotic communities inhabiting the bathypelagic ocean using high-throughput sequencing of the 16S rRNA gene. This work identifies the dominant prokaryotes in the pelagic deep ocean and reveals that 50% of the operational taxonomic units (OTUs) belong to previously unknown prokaryotic taxa, most of which are rare and appear in just a few samples. We show that whereas the local richness of communities is comparable to that observed in previous regional studies, the global pool of prokaryotic taxa detected is modest (∼3600 OTUs), as a high proportion of OTUs are shared among samples. The water masses appear to act as clear drivers of the geographical distribution of both particle-attached and free-living prokaryotes. In addition, we show that the deep-oceanic basins in which the bathypelagic realm is divided contain different particle-attached (but not free-living) microbial communities. The combination of the aging of the water masses and a lack of complete dispersal are identified as the main drivers for this biogeographical pattern. All together, we identify the potential of the deep ocean as a reservoir of still unknown biological diversity with a higher degree of spatial complexity than hitherto considered.

A deep auto-encoder model for gene expression prediction.

Science.gov (United States)

Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

2017-11-17

Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

Global diversity and biogeography of deep-sea pelagic prokaryotes

KAUST Repository

Salazar, Guillem; Cornejo-Castillo, Francisco M.; Bení tez-Barrios, Veró nica; Fraile-Nuez, Eugenio; Á lvarez-Salgado, X. Antó n; Duarte, Carlos M.; Gasol, Josep M.; Acinas, Silvia G.

2015-01-01

The deep-sea is the largest biome of the biosphere, and contains more than half of the whole ocean's microbes. Uncovering their general patterns of diversity and community structure at a global scale remains a great challenge, as only fragmentary information of deep-sea microbial diversity exists based on regional-scale studies. Here we report the first globally comprehensive survey of the prokaryotic communities inhabiting the bathypelagic ocean using high-throughput sequencing of the 16S rRNA gene. This work identifies the dominant prokaryotes in the pelagic deep ocean and reveals that 50% of the operational taxonomic units (OTUs) belong to previously unknown prokaryotic taxa, most of which are rare and appear in just a few samples. We show that whereas the local richness of communities is comparable to that observed in previous regional studies, the global pool of prokaryotic taxa detected is modest (∼3600 OTUs), as a high proportion of OTUs are shared among samples. The water masses appear to act as clear drivers of the geographical distribution of both particle-attached and free-living prokaryotes. In addition, we show that the deep-oceanic basins in which the bathypelagic realm is divided contain different particle-attached (but not free-living) microbial communities. The combination of the aging of the water masses and a lack of complete dispersal are identified as the main drivers for this biogeographical pattern. All together, we identify the potential of the deep ocean as a reservoir of still unknown biological diversity with a higher degree of spatial complexity than hitherto considered.

DAPs: Deep Action Proposals for Action Understanding

KAUST Repository

Escorcia, Victor; Caba Heilbron, Fabian; Niebles, Juan Carlos; Ghanem, Bernard

2016-01-01

action proposals from long videos. We show how to take advantage of the vast capacity of deep learning models and memory cells to retrieve from untrimmed videos temporal segments, which are likely to contain actions. A comprehensive evaluation indicates

DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters

OpenAIRE

Kim, Hanjoo; Park, Jaehong; Jang, Jaehee; Yoon, Sungroh

2016-01-01

The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operation...

TRACING THE LOWEST PROPELLER LINE IN MAGELLANIC HIGH-MASS X-RAY BINARIES

Energy Technology Data Exchange (ETDEWEB)

Christodoulou, Dimitris M.; Laycock, Silas G. T.; Yang, Jun; Fingerman, Samuel, E-mail: dimitris_christodoulou@uml.edu, E-mail: silas_laycock@uml.edu, E-mail: jun_yang@uml.edu, E-mail: fingerman.samuel@gmail.com [Lowell Center for Space Science and Technology, 600 Suffolk Street, Lowell, MA 01854 (United States)

2016-09-20

We have combined the published observations of high-mass X-ray binary (HMXB) pulsars in the Magellanic Clouds with a new processing of the complete archival data sets from the XMM-Newton and Chandra observatories in an attempt to trace the lowest propeller line below which accretion to polar caps is inhibited by the centrifugal force and the pulsations from the most weakly magnetized pulsars cease. Previously published data reveal that some of the faster-spinning pulsars with spin periods of P {sub S} < 12 s, detected at relatively low X-ray luminosities L {sub X} , appear to define such a line in the P {sub S} – L {sub X} diagram, characterized by a magnetic moment of μ = 3 × 10{sup 29} G cm{sup 3}. This value implies the presence of surface magnetic fields of B ≥ 3 × 10{sup 11} G in the compact objects of this class. Only a few quiescent HMXBs are found below the propeller line: LXP4.40 and SXP4.78, for which XMM-Newton and Chandra null detections respectively placed firm upper limits on their X-ray fluxes in deep quiescence; and A0538-66, for which many sub-Eddington detections have never measured any pulsations. On the other hand, the data from the XMM-Newton and Chandra archives show clearly that, during routine observation cycles, several sources have been detected below the propeller line in extremely faint, nonpulsating states that can be understood as the result of weak magnetospheric emission when accretion to the poles is centrifugally stalled or severely diminished. We also pay attention to the anomalous X-ray pulsar CXOU J010043.1-721134 that was reported in HMXB surveys. Its pulsations and locations near and above the propeller line indicate that this pulsar could be accreting from a fossil disk.

A vertically integrated dynamic RAM-cell: Buried bit line memory cell with floating transfer layer

NARCIS (Netherlands)

Mouthaan, A.J.; Vertregt, Maarten

1986-01-01

A charge injection device has been realized in which charge can be injected on to an MOS-capacitor from a buried layer via an isolated transfer layer. The cell is positioned vertically between word and bit line. LOCOS (local oxidation) is used to isolate the cells and (deep) ion implantation to

Cognitive Implications of Deep Gray Matter Iron in Multiple Sclerosis.

Science.gov (United States)

Fujiwara, E; Kmech, J A; Cobzas, D; Sun, H; Seres, P; Blevins, G; Wilman, A H

2017-05-01

Deep gray matter iron accumulation is increasingly recognized in association with multiple sclerosis and can be measured in vivo with MR imaging. The cognitive implications of this pathology are not well-understood, especially vis-à-vis deep gray matter atrophy. Our aim was to investigate the relationships between cognition and deep gray matter iron in MS by using 2 MR imaging-based iron-susceptibility measures. Forty patients with multiple sclerosis (relapsing-remitting, n = 16; progressive, n = 24) and 27 healthy controls were imaged at 4.7T by using the transverse relaxation rate and quantitative susceptibility mapping. The transverse relaxation rate and quantitative susceptibility mapping values and volumes (atrophy) of the caudate, putamen, globus pallidus, and thalamus were determined by multiatlas segmentation. Cognition was assessed with the Brief Repeatable Battery of Neuropsychological Tests. Relationships between cognition and deep gray matter iron were examined by hierarchic regressions. Compared with controls, patients showed reduced memory ( P processing speed ( P = .02) and smaller putamen ( P deep gray matter iron accumulation in the current multiple sclerosis cohort. Atrophy and iron accumulation in deep gray matter both have negative but separable relationships to cognition in multiple sclerosis. © 2017 by American Journal of Neuroradiology.

An adaptive deep Q-learning strategy for handwritten digit recognition.

Science.gov (United States)

Qiao, Junfei; Wang, Gongming; Li, Wenjing; Chen, Min

2018-02-22

Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. In this paper, an adaptive deep Q-learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition. The adaptive deep Q-learning strategy combines the feature-extracting capability of deep learning and the decision-making of reinforcement learning to form an adaptive Q-learning deep belief network (Q-ADBN). First, Q-ADBN extracts the features of original images using an adaptive deep auto-encoder (ADAE), and the extracted features are considered as the current states of Q-learning algorithm. Second, Q-ADBN receives Q-function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q-function using Q-learning algorithm. Finally, experimental results from the well-known MNIST dataset show that the proposed Q-ADBN has a superiority to other similar methods in terms of accuracy and running time. Copyright © 2018 Elsevier Ltd. All rights reserved.

DEEP 21 cm H I OBSERVATIONS AT z ∼ 0.1: THE PRECURSOR TO THE ARECIBO ULTRA DEEP SURVEY

International Nuclear Information System (INIS)

Freudling, Wolfram; Zwaan, Martin; Staveley-Smith, Lister; Meyer, Martin; Catinella, Barbara; Minchin, Robert; Calabretta, Mark; Momjian, Emmanuel; O'Neil, Karen

2011-01-01

The 'ALFA Ultra Deep Survey' (AUDS) is an ongoing 21 cm spectral survey with the Arecibo 305 m telescope. AUDS will be the most sensitive blind survey undertaken with Arecibo's 300 MHz Mock spectrometer. The survey searches for 21 cm H I line emission at redshifts between 0 and 0.16. The main goals of the survey are to investigate the H I content and probe the evolution of H I gas within that redshift region. In this paper, we report on a set of precursor observations with a total integration time of 53 hr. The survey detected a total of eighteen 21 cm emission lines at redshifts between 0.07 and 0.15 in a region centered around α 2000 ∼ 0 h , δ ∼ 15 0 42'. The rate of detection is consistent with the one expected from the local H I mass function. The derived relative H I density at the median redshift of the survey is ρ H I [z = 0.125] = (1.0 ± 0.3)ρ 0 , where ρ 0 is the H I density at zero redshift.

The Spectral Energy Distributions of z ~ 8 Galaxies from the IRAC Ultra Deep Fields: Emission Lines, Stellar Masses, and Specific Star Formation Rates at 650 Myr

Science.gov (United States)

Labbé, I.; Oesch, P. A.; Bouwens, R. J.; Illingworth, G. D.; Magee, D.; González, V.; Carollo, C. M.; Franx, M.; Trenti, M.; van Dokkum, P. G.; Stiavelli, M.

2013-11-01

Using new ultradeep Spitzer/InfraRed Array Camera (IRAC) photometry from the IRAC Ultra Deep Field program, we investigate the stellar populations of a sample of 63 Y-dropout galaxy candidates at z ~ 8, only 650 Myr after the big bang. The sources are selected from HST/ACS+WFC3/IR data over the Hubble Ultra Deep Field (HUDF), two HUDF parallel fields, and wide area data over the CANDELS/GOODS-South. The new Spitzer/IRAC data increase the coverage in [3.6] and [4.5] to ~120h over the HUDF reaching depths of ~28 (AB,1σ). The improved depth and inclusion of brighter candidates result in direct >=3σ InfraRed Array Camera (IRAC) detections of 20/63 sources, of which 11/63 are detected at >=5σ. The average [3.6]-[4.5] colors of IRAC detected galaxies at z ~ 8 are markedly redder than those at z ~ 7, observed only 130 Myr later. The simplest explanation is that we witness strong rest-frame optical emission lines (in particular [O III] λλ4959, 5007 + Hβ) moving through the IRAC bandpasses with redshift. Assuming that the average rest-frame spectrum is the same at both z ~ 7 and z ~ 8 we estimate a rest-frame equivalent width of {W}_{[O\\,\\scriptsize{III}]\\ \\lambda \\lambda 4959,5007+H\\beta }=670^{+260}_{-170} Å contributing 0.56^{+0.16}_{-0.11} mag to the [4.5] filter at z ~ 8. The corresponding {W}_{H\\alpha }=430^{+160}_{-110} Å implies an average specific star formation rate of sSFR=11_{-5}^{+11} Gyr-1 and a stellar population age of 100_{-50}^{+100} Myr. Correcting the spectral energy distribution for the contribution of emission lines lowers the average best-fit stellar masses and mass-to-light ratios by ~3 ×, decreasing the integrated stellar mass density to \\rho ^*(z=8,M_{\\rm{UV}}Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with programs #11563, 9797. Based on observations with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of

Cisgenic Rvi6 scab-resistant apple lines show no differences in Rvi6 transcription when compared with conventionally bred cultivars.

Science.gov (United States)

Chizzali, Cornelia; Gusberti, Michele; Schouten, Henk J; Gessler, Cesare; Broggini, Giovanni A L

2016-03-01

The expression of the apple scab resistance gene Rvi6 in different apple cultivars and lines is not modulated by biotic or abiotic factors. All commercially important apple cultivars are susceptible to Venturia inaequalis, the causal organism of apple scab. A limited number of apple cultivars were bred to express the resistance gene Vf from the wild apple genotype Malus floribunda 821. Positional cloning of the Vf locus allowed the identification of the Rvi6 (formerly HcrVf2) scab resistance gene that was subsequently used to generate cisgenic apple lines. It is important to understand and compare how this resistance gene is transcribed and modulated during infection in conventionally bred cultivars and in cisgenic lines. The aim of this work was to study the transcription pattern of Rvi6 in three classically bred apple cultivars and six lines of 'Gala' genetically modified to express Rvi6. Rvi6 transcription was analyzed at two time points using quantitative real-time PCR (RT-qPCR) following inoculation with V. inaequalis conidia or water. Rvi6 transcription was assessed in relation to five reference genes. β-Actin, RNAPol, and UBC were the most suited to performing RT-qPCR experiments on Malus × domestica. Inoculation with V. inaequalis conidia under conditions conducive to scab infection failed to produce any significant changes to the transcription level of Rvi6. Rvi6 expression levels were inconsistent in response to external treatments in the different apple cultivars, and transgenic, intragenic or cisgenic lines.

DeepPVP: phenotype-based prioritization of causative variants using deep learning

KAUST Repository

Boudellioua, Imene; Kulmanov, Maxat; Schofield, Paul N; Gkoutos, Georgios V; Hoehndorf, Robert

2018-01-01

phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well

Deep-inelastic electron-proton diffraction

International Nuclear Information System (INIS)

Dainton, J.B.

1995-11-01

Recent measurements by the H1 collaboration at HERA of the cross section for deep-inelastic electron-proton scattering in which the proton interacts with minimal energy transfer and limited 4-momentum transfer squared are presented in the form of the contribution F 2 D(3) to the proton structure function F 2 . By parametrising the cross section phenomenologically in terms of a leading effective Regge pole exchange and comparing the result with a similar parametrisation of hadronic pp physics, the proton interaction is demonstrated to be dominantly of a diffractive nature. The quantitative interpretation of the parametrisation in terms of the properties of an effective leading Regge pole exchange, the pomeron (IP), shows that there is no evidence for a 'harder' BFKL-motivated IP in such deep-inelastic proton diffraction. The total contribution of proton diffraction to deep-inelastic electron-proton scattering is measured to be ∝10% and to be rather insensitive to Bjorken-x and Q 2 . A first measurement of the partonic structure of diffractive exchange is presented. It is shown to be readily interpreted in terms of the exchange of gluons, and to suggest that the bulk of diffractive momentum transfer is carried by a leading gluon. (orig.)

Optical subnet concepts for the deep space network

Science.gov (United States)

Shaik, K.; Wonica, D.; Wilhelm, M.

1993-01-01

This article describes potential enhancements to the Deep Space Network, based on a subnet of receiving stations that will utilize optical communications technology in the post-2010 era. Two optical subnet concepts are presented that provide full line-of-sight coverage of the ecliptic, 24 hours a day, with high weather availability. The technical characteristics of the optical station and the user terminal are presented, as well as the effects of cloud cover, transmittance through the atmosphere, and background noise during daytime or nighttime operation on the communications link. In addition, this article identifies candidate geographic sites for the two network concepts and includes a link design for a hypothetical Pluto mission in 2015.

Deep learning-based Diabetic Retinopathy assessment on embedded system.

Science.gov (United States)

Ardiyanto, Igi; Nugroho, Hanung Adi; Buana, Ratna Lestari Budiani

2017-07-01

Diabetic Retinopathy (DR) is a disease which affect the vision ability. The observation by an ophthalmologist usually conducted by analyzing the retinal images of the patient which are marked by some DR features. However some misdiagnosis are usually found due to human error. Here, a deep learning-based low-cost embedded system is established to assist the doctor for grading the severity of the DR from the retinal images. A compact deep learning algorithm named Deep-DR-Net which fits on a small embedded board is afterwards proposed for such purposes. In the heart of Deep-DR-Net, a cascaded encoder-classifier network is arranged using residual style for ensuring the small model size. The usage of different types of convolutional layers subsequently guarantees the features richness of the network for differentiating the grade of the DR. Experimental results show the capability of the proposed system for detecting the existence as well as grading the severity of the DR symptomps.

Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning.

Science.gov (United States)

Ren, Zhipeng; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Zhipeng Ren; Daoyi Dong; Huaxiong Li; Chunlin Chen; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Ren, Zhipeng

2018-06-01

In this paper, a new training paradigm is proposed for deep reinforcement learning using self-paced prioritized curriculum learning with coverage penalty. The proposed deep curriculum reinforcement learning (DCRL) takes the most advantage of experience replay by adaptively selecting appropriate transitions from replay memory based on the complexity of each transition. The criteria of complexity in DCRL consist of self-paced priority as well as coverage penalty. The self-paced priority reflects the relationship between the temporal-difference error and the difficulty of the current curriculum for sample efficiency. The coverage penalty is taken into account for sample diversity. With comparison to deep Q network (DQN) and prioritized experience replay (PER) methods, the DCRL algorithm is evaluated on Atari 2600 games, and the experimental results show that DCRL outperforms DQN and PER on most of these games. More results further show that the proposed curriculum training paradigm of DCRL is also applicable and effective for other memory-based deep reinforcement learning approaches, such as double DQN and dueling network. All the experimental results demonstrate that DCRL can achieve improved training efficiency and robustness for deep reinforcement learning.

Stimulation Technologies for Deep Well Completions

Energy Technology Data Exchange (ETDEWEB)

None

2003-09-30

The Department of Energy (DOE) is sponsoring the Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies is conducting a study to evaluate the stimulation of deep wells. The objective of the project is to assess U.S. deep well drilling & stimulation activity, review rock mechanics & fracture growth in deep, high pressure/temperature wells and evaluate stimulation technology in several key deep plays. An assessment of historical deep gas well drilling activity and forecast of future trends was completed during the first six months of the project; this segment of the project was covered in Technical Project Report No. 1. The second progress report covers the next six months of the project during which efforts were primarily split between summarizing rock mechanics and fracture growth in deep reservoirs and contacting operators about case studies of deep gas well stimulation.

The transcriptome of the Didelphis virginiana opossum kidney OK proximal tubule cell line.

Science.gov (United States)

Eshbach, Megan L; Sethi, Rahil; Avula, Raghunandan; Lamb, Janette; Hollingshead, Deborah J; Finegold, David N; Locker, Joseph D; Chandran, Uma R; Weisz, Ora A

2017-09-01

The OK cell line derived from the kidney of a female opossum Didelphis virginiana has proven to be a useful model in which to investigate the unique regulation of ion transport and membrane trafficking mechanisms in the proximal tubule (PT). Sequence data and comparison of the transcriptome of this cell line to eutherian mammal PTs would further broaden the utility of this culture model. However, the genomic sequence for D. virginiana is not available and although a draft genome sequence for the opossum Monodelphis domestica (sequenced in 2012 by the Broad Institute) exists, transcripts sequenced from both species show significant divergence. The M. domestica sequence is not highly annotated, and the majority of transcripts are predicted rather than experimentally validated. Using deep RNA sequencing of the D. virginiana OK cell line, we characterized its transcriptome via de novo transcriptome assembly and alignment to the M. domestica genome. The quality of the de novo assembled transcriptome was assessed by the extent of homology to sequences in nucleotide and protein databases. Gene expression levels in the OK cell line, from both the de novo transcriptome and genes aligned to the M. domestica genome, were compared with publicly available rat kidney nephron segment expression data. Our studies demonstrate the expression in OK cells of numerous PT-specific ion transporters and other key proteins relevant for rodent and human PT function. Additionally, the sequence and expression data reported here provide an important resource for genetic manipulation and other studies on PT cell function using these cells. Copyright © 2017 the American Physiological Society.

DeepQA: improving the estimation of single protein model quality with deep belief networks.

Science.gov (United States)

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-12-05

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Yearlong moored bioluminescence and current data at KM3NeT neutrino telescope sites in the deep Ionian Sea

NARCIS (Netherlands)

van Haren, H.; de Jong, M.; Kooijman, P.

2015-01-01

Yearlong observations are presented using stand-alone small optical sensors and current meters in the deep Ionian Sea, E-Mediterranean. At two future neutrino telescope sites, off Sicily (I) and off Peloponessos (Gr), we deployed 2500–3000 m long mooring lines with oceanographic instrumentation. At

Evolutionary process of deep-sea bathymodiolus mussels.

Directory of Open Access Journals (Sweden)

Jun-Ichi Miyazaki

Full Text Available BACKGROUND: Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. METHODOLOGY/PRINCIPAL FINDING: We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI and NADH dehydrogenase subunit 4 (ND4 genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. CONCLUSIONS/SIGNIFICANCE: The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of

Deep and Ultra-deep Underground Observatory for In Situ Stress, Fluids, and Life

Science.gov (United States)

Boutt, D. F.; Wang, H.; Kieft, T. L.

2008-12-01

The question 'How deeply does life extend into the Earth?' forms a single, compelling vision for multidisciplinary science opportunities associated with physical and biological processes occurring naturally or in response to construction in the deep and ultra-deep subsurface environment of the Deep Underground Science and Engineering Laboratory (DUSEL) in the former Homestake mine. The scientific opportunity is to understand the interaction between the physical environment and microbial life, specifically, the coupling among (1) stress state and deformation; (2) flow and transport and origin of fluids; and (3) energy and nutrient sources for microbial life; and (4) microbial identity, diversity and activities. DUSEL-Homestake offers the environment in which these questions can be addressed unencumbered by competing human activities. Associated with the interaction among these variables are a number of questions that will be addressed at variety of depths and scales in the facility: What factors control the distribution of life as a function of depth and temperature? What patterns in microbial diversity, microbial activity and nutrients are found along this gradient? How do state variables (stress, strain, temperature, and pore pressure) and constitutive properties (permeability, porosity, modulus, etc.) vary with scale (space, depth, time) in a large 4D heterogeneous system: core - borehole - drift - whole mine - regional? How are fluid flow and stress coupled in a low-permeability, crystalline environment dominated by preferential flow paths? How does this interaction influence the distribution of fluids, solutes, gases, colloids, and biological resources (e.g. energy and nutritive substrates) in the deep continental subsurface? What is the interaction between geomechanics/geohydrology and microbiology (microbial abundance, diversity, distribution, and activities)? Can relationships elucidated within the mechanically and hydrologically altered subsurface habitat

Deep water recycling through time.

Science.gov (United States)

Magni, Valentina; Bouilhol, Pierre; van Hunen, Jeroen

2014-11-01

We investigate the dehydration processes in subduction zones and their implications for the water cycle throughout Earth's history. We use a numerical tool that combines thermo-mechanical models with a thermodynamic database to examine slab dehydration for present-day and early Earth settings and its consequences for the deep water recycling. We investigate the reactions responsible for releasing water from the crust and the hydrated lithospheric mantle and how they change with subduction velocity ( v s ), slab age ( a ) and mantle temperature (T m ). Our results show that faster slabs dehydrate over a wide area: they start dehydrating shallower and they carry water deeper into the mantle. We parameterize the amount of water that can be carried deep into the mantle, W (×10 5 kg/m 2 ), as a function of v s (cm/yr), a (Myrs), and T m (°C):[Formula: see text]. We generally observe that a 1) 100°C increase in the mantle temperature, or 2) ∼15 Myr decrease of plate age, or 3) decrease in subduction velocity of ∼2 cm/yr all have the same effect on the amount of water retained in the slab at depth, corresponding to a decrease of ∼2.2×10 5 kg/m 2 of H 2 O. We estimate that for present-day conditions ∼26% of the global influx water, or 7×10 8 Tg/Myr of H 2 O, is recycled into the mantle. Using a realistic distribution of subduction parameters, we illustrate that deep water recycling might still be possible in early Earth conditions, although its efficiency would generally decrease. Indeed, 0.5-3.7 × 10 8 Tg/Myr of H 2 O could still be recycled in the mantle at 2.8 Ga. Deep water recycling might be possible even in early Earth conditions We provide a scaling law to estimate the amount of H 2 O flux deep into the mantle Subduction velocity has a a major control on the crustal dehydration pattern.

Late Holocene variations in Pacific surface circulation and biogeochemistry inferred from proteinaceous deep-sea corals

Directory of Open Access Journals (Sweden)

T. P. Guilderson

2013-09-01

Full Text Available δ15N and δ13C data obtained from samples of proteinaceous deep-sea corals collected from the North Pacific Subtropical Gyre (Hawaiian Archipelago and the central equatorial Pacific (Line Islands document multidecadal to century-scale variability in the isotopic composition of surface-produced particulate organic matter exported to the deep sea. Comparison of the δ13C data, where Line Islands samples are 0.6‰ more positive than the Hawaiian samples, supports the contention that the North Pacific Subtropical Gyre is more efficient than the tropical upwelling system at trapping and/or recycling nutrients within the mixed layer. δ15N values from the Line Islands samples are also more positive than those from the central gyre, and within the Hawaiian samples there is a gradient with more positive δ15N values in samples from the main Hawaiian Islands versus the French Frigate Shoals in the Northwestern Hawaiian Islands. The gradient in the Hawaiian samples likely reflects the relative importance of algal acquisition of metabolic N via dissolved seawater nitrate uptake versus nitrogen fixation. The Hawaiian sample set also exhibits a strong decrease in δ15N values from the mid-Holocene to present. We hypothesize that this decrease is most likely the result of decreasing trade winds, and possibly a commensurate decrease in entrainment of more positive δ15N-NO3 subthermocline water masses.

Resolution function in deep inelastic neutron scattering using the Foil Cycling Technique

International Nuclear Information System (INIS)

Pietropaolo, A.; Andreani, C.; Filabozzi, A.; Pace, E.; Senesi, R.

2007-01-01

New perspectives for epithermal neutron spectroscopy are being opened up by the development of the Resonance Detector (RD) and its use on inverse geometry time of flight (TOF) spectrometers at spallation sources. The most recent result is the Foil Cycling Technique (FCT), which has been developed and applied on the VESUVIO spectrometer operating in the RD configuration. This technique has demonstrated its capability to improve the resolution function of the spectrometer and to provide an effective neutron and gamma background subtraction method. This paper reports a detailed analysis of the line shape of the resolution function in Deep Inelastic Neutron Scattering (DINS) measurements on VESUVIO spectrometer, operating in the RD configuration and employing the FCT. The aim is to provide an analytical approximation for the analyzer energy transfer function, an useful tool for data analysis on VESUVIO. Simulated and experimental results of DINS measurements on a lead sample are compared. The line shape analysis shows that the most reliable analytical approximation of the energy transfer function is a sum of a Gaussian and a power of a Lorentzian. A comparison with the Double Difference Method (DDM) is also discussed. It is shown that the energy resolution improvement for the FCT and the DDM is almost the same, while the counting efficiency is a factor of about 1.4 higher for the FCT

Stimulation Technologies for Deep Well Completions

Energy Technology Data Exchange (ETDEWEB)

Stephen Wolhart

2005-06-30

The Department of Energy (DOE) is sponsoring the Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies conducted a study to evaluate the stimulation of deep wells. The objective of the project was to review U.S. deep well drilling and stimulation activity, review rock mechanics and fracture growth in deep, high-pressure/temperature wells and evaluate stimulation technology in several key deep plays. This report documents results from this project.

Optimization of line configuration and balancing for flexible machining lines

Science.gov (United States)

Liu, Xuemei; Li, Aiping; Chen, Zurui

2016-05-01

Line configuration and balancing is to select the type of line and allot a given set of operations as well as machines to a sequence of workstations to realize high-efficiency production. Most of the current researches for machining line configuration and balancing problems are related to dedicated transfer lines with dedicated machine workstations. With growing trends towards great product variety and fluctuations in market demand, dedicated transfer lines are being replaced with flexible machining line composed of identical CNC machines. This paper deals with the line configuration and balancing problem for flexible machining lines. The objective is to assign operations to workstations and find the sequence of execution, specify the number of machines in each workstation while minimizing the line cycle time and total number of machines. This problem is subject to precedence, clustering, accessibility and capacity constraints among the features, operations, setups and workstations. The mathematical model and heuristic algorithm based on feature group strategy and polychromatic sets theory are presented to find an optimal solution. The feature group strategy and polychromatic sets theory are used to establish constraint model. A heuristic operations sequencing and assignment algorithm is given. An industrial case study is carried out, and multiple optimal solutions in different line configurations are obtained. The case studying results show that the solutions with shorter cycle time and higher line balancing rate demonstrate the feasibility and effectiveness of the proposed algorithm. This research proposes a heuristic line configuration and balancing algorithm based on feature group strategy and polychromatic sets theory which is able to provide better solutions while achieving an improvement in computing time.

Dilution limits dissolved organic carbon utilization in the deep ocean

KAUST Repository

Arrieta, Jesus

2015-03-19

Oceanic dissolved organic carbon (DOC) is the second largest reservoir of organic carbon in the biosphere. About 72% of the global DOC inventory is stored in deep oceanic layers for years to centuries, supporting the current view that it consists of materials resistant to microbial degradation. An alternative hypothesis is that deep-water DOC consists of many different, intrinsically labile compounds at concentrations too low to compensate for the metabolic costs associated to their utilization. Here, we present experimental evidence showing that low concentrations rather than recalcitrance preclude consumption of a substantial fraction of DOC, leading to slow microbial growth in the deep ocean. These findings demonstrate an alternative mechanism for the long-term storage of labile DOC in the deep ocean, which has been hitherto largely ignored. © 2015, American Association for the Advancement of Science. All rights reserved.

Dilution limits dissolved organic carbon utilization in the deep ocean

KAUST Repository

Arrieta, J M; Mayol, Eva; Hansman, Roberta L.; Herndl, Gerhard J.; Dittmar, Thorsten; Duarte, Carlos M.

2015-01-01

Oceanic dissolved organic carbon (DOC) is the second largest reservoir of organic carbon in the biosphere. About 72% of the global DOC inventory is stored in deep oceanic layers for years to centuries, supporting the current view that it consists of materials resistant to microbial degradation. An alternative hypothesis is that deep-water DOC consists of many different, intrinsically labile compounds at concentrations too low to compensate for the metabolic costs associated to their utilization. Here, we present experimental evidence showing that low concentrations rather than recalcitrance preclude consumption of a substantial fraction of DOC, leading to slow microbial growth in the deep ocean. These findings demonstrate an alternative mechanism for the long-term storage of labile DOC in the deep ocean, which has been hitherto largely ignored. © 2015, American Association for the Advancement of Science. All rights reserved.

DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.

Science.gov (United States)

Wang, Feng; Gong, Huichao; Liu, Gaochao; Li, Meijing; Yan, Chuangye; Xia, Tian; Li, Xueming; Zeng, Jianyang

2016-09-01

Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python. Copyright © 2016 Elsevier Inc. All rights reserved.

Stable isotope geochemistry of deep sea cherts

Energy Technology Data Exchange (ETDEWEB)

Kolodny, Y; Epstein, S [California Inst. of Tech., Pasadena (USA). Div. of Geological Sciences

1976-10-01

Seventy four samples of DSDP (Deep Sea Drilling Project) recovered cherts of Jurassic to Miocene age from varying locations, and 27 samples of on-land exposed cherts were analyzed for the isotopic composition of their oxygen and hydrogen. These studies were accompanied by mineralogical analyses and some isotopic analyses of the coexisting carbonates. delta/sup 18/0 of chert ranges between 27 and 39 parts per thousand relative to SMOW, delta/sup 18/0 of porcellanite - between 30 and 42 parts per thousand. The consistent enrichment of opal-CT in porcellanites in /sup 18/0 with respect to coexisting microcrystalline quartz in chert is probably a reflection of a different temperature (depth) of diagenesis of the two phases. delta/sup 18/0 of deep sea cherts generally decrease with increasing age, indicating an overall cooling of the ocean bottom during the last 150 m.y. A comparison of this trend with that recorded by benthonic foraminifera (Douglas et al., Initial Reports of the Deep Sea Drilling Project; 32:509(1975)) indicates the possibility of delta/sup 18/0 in deep sea cherts not being frozen in until several tens of millions of years after deposition. Cherts of any Age show a spread of delta/sup 18/0 values, increasing diagenesis being reflected in a lowering of delta/sup 18/0. Drusy quartz has the lowest delta/sup 18/0 values. On land exposed cherts are consistently depleted in /sup 18/0 in comparison to their deep sea time equivalent cherts. Water extracted from deep sea cherts ranges between 0.5 and 1.4 wt%. deltaD of this water ranges between -78 and -95 parts per thousand and is not a function of delta/sup 18/0 of the cherts (or the temperature of their formation).

DeepBase: annotation and discovery of microRNAs and other noncoding RNAs from deep-sequencing data.

Science.gov (United States)

Yang, Jian-Hua; Qu, Liang-Hu

2012-01-01

Recent advances in high-throughput deep-sequencing technology have produced large numbers of short and long RNA sequences and enabled the detection and profiling of known and novel microRNAs (miRNAs) and other noncoding RNAs (ncRNAs) at unprecedented sensitivity and depth. In this chapter, we describe the use of deepBase, a database that we have developed to integrate all public deep-sequencing data and to facilitate the comprehensive annotation and discovery of miRNAs and other ncRNAs from these data. deepBase provides an integrative, interactive, and versatile web graphical interface to evaluate miRBase-annotated miRNA genes and other known ncRNAs, explores the expression patterns of miRNAs and other ncRNAs, and discovers novel miRNAs and other ncRNAs from deep-sequencing data. deepBase also provides a deepView genome browser to comparatively analyze these data at multiple levels. deepBase is available at http://deepbase.sysu.edu.cn/.

Exploring orange peel treatment with deep eutectic solvents and diluted organic acids

NARCIS (Netherlands)

van den Bruinhorst, A.; Kouris, P.; Timmer, J.M.K.; de Croon, M.H.J.M.; Kroon, M.C.

2016-01-01

The disintegration of orange peel waste in deep eutectic solvents and diluted organic acids is presented in this work. The albedo and flavedo layers of the peel were studied separately, showing faster disintegration of the latter. Addition of water to the deep eutectic solvents lowered the amount of

AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling.

Science.gov (United States)

Wang, Sheng; Sun, Siqi; Xu, Jinbo

2016-09-01

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also has similar performance as the other two training methods on solvent accessibility prediction, which has three equally-distributed labels. Furthermore, our experimental results show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks. The data and software related to this paper are available at https://github.com/realbigws/DeepCNF_AUC.

Large-eddy simulation of maritime deep tropical convection

Directory of Open Access Journals (Sweden)

Peter A Bogenschutz

2009-12-01

Full Text Available This study represents an attempt to apply Large-Eddy Simulation (LES resolution to simulate deep tropical convection in near equilibrium for 24 hours over an area of about 205 x 205 km2, which is comparable to that of a typical horizontal grid cell in a global climate model. The simulation is driven by large-scale thermodynamic tendencies derived from mean conditions during the GATE Phase III field experiment. The LES uses 2048 x 2048 x 256 grid points with horizontal grid spacing of 100 m and vertical grid spacing ranging from 50 m in the boundary layer to 100 m in the free troposphere. The simulation reaches a near equilibrium deep convection regime in 12 hours. The simulated vertical cloud distribution exhibits a trimodal vertical distribution of deep, middle and shallow clouds similar to that often observed in Tropics. A sensitivity experiment in which cold pools are suppressed by switching off the evaporation of precipitation results in much lower amounts of shallow and congestus clouds. Unlike the benchmark LES where the new deep clouds tend to appear along the edges of spreading cold pools, the deep clouds in the no-cold-pool experiment tend to reappear at the sites of the previous deep clouds and tend to be surrounded by extensive areas of sporadic shallow clouds. The vertical velocity statistics of updraft and downdraft cores below 6 km height are compared to aircraft observations made during GATE. The comparison shows generally good agreement, and strongly suggests that the LES simulation can be used as a benchmark to represent the dynamics of tropical deep convection on scales ranging from large turbulent eddies to mesoscale convective systems. The effect of horizontal grid resolution is examined by running the same case with progressively larger grid sizes of 200, 400, 800, and 1600 m. These runs show a reasonable agreement with the benchmark LES in statistics such as convective available potential energy, convective inhibition

Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

Energy Technology Data Exchange (ETDEWEB)

Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea.

Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

International Nuclear Information System (INIS)

Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu

2016-01-01

In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea

Deep imitation learning for 3D navigation tasks.

Science.gov (United States)

Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina

2018-01-01

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

Behavior of candidate canister materials in deep ocean environments

International Nuclear Information System (INIS)

Smyrl, W.H.; Stephenson, L.L.; Braithwaite, J.W.

1977-04-01

Corrosion tests have been conducted under simulated deep ocean conditions for nine months. The materials tested were base alloys of titanium, zirconium, and nickel. All materials tested showed corrosion rates that were very low even at the highest test temperature. None showed susceptibility to either stress corrosion cracking or differential aeration corrosion. Ambient electrochemical tests confirmed the findings that none should be sensitive to differential oxygen effects. The zirconium alloys may be more susceptible to pitting corrosion than the others, although the pitting conditions are unlikely to be found in service, unless higher temperatures are encountered. All the alloys tested could give long life under deep ocean conditions and are candidates for more detailed corrosion studies

Deep UV LEDs

Science.gov (United States)

Han, Jung; Amano, Hiroshi; Schowalter, Leo

2014-06-01

Deep ultraviolet (DUV) photons interact strongly with a broad range of chemical and biological molecules; compact DUV light sources could enable a wide range of applications in chemi/bio-sensing, sterilization, agriculture, and industrial curing. The much shorter wavelength also results in useful characteristics related to optical diffraction (for lithography) and scattering (non-line-of-sight communication). The family of III-N (AlGaInN) compound semiconductors offers a tunable energy gap from infrared to DUV. While InGaN-based blue light emitters have been the primary focus for the obvious application of solid state lighting, there is a growing interest in the development of efficient UV and DUV light-emitting devices. In the past few years we have witnessed an increasing investment from both government and industry sectors to further the state of DUV light-emitting devices. The contributions in Semiconductor Science and Technology 's special issue on DUV devices provide an up-to-date snapshot covering many relevant topics in this field. Given the expected importance of bulk AlN substrate in DUV technology, we are pleased to include a review article by Hartmann et al on the growth of AlN bulk crystal by physical vapour transport. The issue of polarization field within the deep ultraviolet LEDs is examined in the article by Braut et al. Several commercial companies provide useful updates in their development of DUV emitters, including Nichia (Fujioka et al ), Nitride Semiconductors (Muramoto et al ) and Sensor Electronic Technology (Shatalov et al ). We believe these articles will provide an excellent overview of the state of technology. The growth of AlGaN heterostructures by molecular beam epitaxy, in contrast to the common organo-metallic vapour phase epitaxy, is discussed by Ivanov et al. Since hexagonal boron nitride (BN) has received much attention as both a UV and a two-dimensional electronic material, we believe it serves readers well to include the

A Deep Chandra ACIS Study of NGC 4151. II. The Innermost Emission Line Region and Strong Evidence for Radio Jet-NLR Cloud Collision

Science.gov (United States)

Wang, Junfeng; Fabbiano, Giuseppina; Elvis, Martin; Risaliti, Guido; Mundell, Carole G.; Karovska, Margarita; Zezas, Andreas

2011-07-01

We have studied the X-ray emission within the inner ~150 pc radius of NGC 4151 by constructing high spatial resolution emission line images of blended O VII, O VIII, and Ne IX. These maps show extended structures that are spatially correlated with the radio outflow and optical [O III] emission. We find strong evidence for jet-gas cloud interaction, including morphological correspondences with regions of X-ray enhancement, peaks of near-infrared [Fe II] emission, and optical clouds. In these regions, moreover, we find evidence of elevated Ne IX/O VII ratios; the X-ray emission of these regions also exceeds that expected from nuclear photoionization. Spectral fitting reveals the presence of a collisionally ionized component. The thermal energy of the hot gas suggests that >~ 0.1% of the estimated jet power is deposited into the host interstellar medium through interaction between the radio jet and the dense medium of the circumnuclear region. We find possible pressure equilibrium between the collisionally ionized hot gas and the photoionized line-emitting cool clouds. We also obtain constraints on the extended iron and silicon fluorescent emission. Both lines are spatially unresolved. The upper limit on the contribution of an extended emission region to the Fe Kα emission is <~ 5% of the total, in disagreement with a previous claim that 65% of the Fe Kα emission originates in the extended narrow line region.

Deep Mapping and Spatial Anthropology

Directory of Open Access Journals (Sweden)

Les Roberts

2016-01-01

Full Text Available This paper provides an introduction to the Humanities Special Issue on “Deep Mapping”. It sets out the rationale for the collection and explores the broad-ranging nature of perspectives and practices that fall within the “undisciplined” interdisciplinary domain of spatial humanities. Sketching a cross-current of ideas that have begun to coalesce around the concept of “deep mapping”, the paper argues that rather than attempting to outline a set of defining characteristics and “deep” cartographic features, a more instructive approach is to pay closer attention to the multivalent ways deep mapping is performatively put to work. Casting a critical and reflexive gaze over the developing discourse of deep mapping, it is argued that what deep mapping “is” cannot be reduced to the otherwise a-spatial and a-temporal fixity of the “deep map”. In this respect, as an undisciplined survey of this increasing expansive field of study and practice, the paper explores the ways in which deep mapping can engage broader discussion around questions of spatial anthropology.

Deep Vein Thrombosis

African Journals Online (AJOL)

OWNER

Deep Vein Thrombosis: Risk Factors and Prevention in Surgical Patients. Deep Vein ... preventable morbidity and mortality in hospitalized surgical patients. ... the elderly.3,4 It is very rare before the age ... depends on the risk level; therefore an .... but also in the post-operative period. ... is continuing uncertainty regarding.

Potential for offshore geothermal developments using deep gas wells

Energy Technology Data Exchange (ETDEWEB)

Teodoriu, C.; Falcone, G. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE

2013-08-01

The development of geothermal resources is steadily increasing as operators meet the challenge of maximising the temperature difference between production and injection wells, while minimising the wellhead temperature of the latter. At present, the minimum working wellhead temperature reported for the heat-to-electricity conversion cycles is limited to about 80 C. The cycle efficiency can be improved by reducing the injection temperature, which is the temperature at which the fluid exits the process. This paper evaluates the potential for generating electricity with a subsea geothermal plant using the difference between downhole reservoir temperature and that of the cold seawater at the mud line. The temperature in the world's oceans is relatively constant, ranging from 0 to 4 C at around 400 meters water depth. The use of these lower offshore water temperatures may help boost geothermal energy development. Deep gas resources are considered to be held within reservoirs below 4600 meters (15000 feet) and are relatively undeveloped as the risks and costs involved in drilling and producing such resources are extremely high. These deep resources have high reservoir temperatures, which offer an opportunity for geothermal exploitation if a new development concept can be formulated. In particular, the well design and reservoir development plan should consider reutilising existing well stock, including dry and plugged and abandoned wells for geothermal application once the gas field has been depleted. The major risks considered in this study include alternative uses of wells in no flow or rapid depletion situations. Reutilisation of the wells of depleted gas reservoirs will invariably lead to lower geothermal development costs compared with starting a geothermal campaign by drilling new wells. In particular, the well design and reservoir development plan should consider reutilising existing well stock, including dry and plugged and abandoned wells for geothermal

MICROLENSING OF QUASAR BROAD EMISSION LINES: CONSTRAINTS ON BROAD LINE REGION SIZE

Energy Technology Data Exchange (ETDEWEB)

Guerras, E.; Mediavilla, E. [Instituto de Astrofisica de Canarias, Via Lactea S/N, La Laguna E-38200, Tenerife (Spain); Jimenez-Vicente, J. [Departamento de Fisica Teorica y del Cosmos, Universidad de Granada, Campus de Fuentenueva, E-18071 Granada (Spain); Kochanek, C. S. [Department of Astronomy and the Center for Cosmology and Astroparticle Physics, The Ohio State University, 4055 McPherson Lab, 140 West 18th Avenue, Columbus, OH 43221 (United States); Munoz, J. A. [Departamento de Astronomia y Astrofisica, Universidad de Valencia, E-46100 Burjassot, Valencia (Spain); Falco, E. [Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Motta, V. [Departamento de Fisica y Astronomia, Universidad de Valparaiso, Avda. Gran Bretana 1111, Valparaiso (Chile)

2013-02-20

We measure the differential microlensing of the broad emission lines between 18 quasar image pairs in 16 gravitational lenses. We find that the broad emission lines are in general weakly microlensed. The results show, at a modest level of confidence (1.8{sigma}), that high ionization lines such as C IV are more strongly microlensed than low ionization lines such as H{beta}, indicating that the high ionization line emission regions are more compact. If we statistically model the distribution of microlensing magnifications, we obtain estimates for the broad line region size of r{sub s} = 24{sup +22} {sub -15} and r{sub s} = 55{sup +150} {sub -35} lt-day (90% confidence) for the high and low ionization lines, respectively. When the samples are divided into higher and lower luminosity quasars, we find that the line emission regions of more luminous quasars are larger, with a slope consistent with the expected scaling from photoionization models. Our estimates also agree well with the results from local reveberation mapping studies.

Lining seam elimination algorithm and surface crack detection in concrete tunnel lining

Science.gov (United States)

Qu, Zhong; Bai, Ling; An, Shi-Quan; Ju, Fang-Rong; Liu, Ling

2016-11-01

Due to the particularity of the surface of concrete tunnel lining and the diversity of detection environments such as uneven illumination, smudges, localized rock falls, water leakage, and the inherent seams of the lining structure, existing crack detection algorithms cannot detect real cracks accurately. This paper proposed an algorithm that combines lining seam elimination with the improved percolation detection algorithm based on grid cell analysis for surface crack detection in concrete tunnel lining. First, check the characteristics of pixels within the overlapping grid to remove the background noise and generate the percolation seed map (PSM). Second, cracks are detected based on the PSM by the accelerated percolation algorithm so that the fracture unit areas can be scanned and connected. Finally, the real surface cracks in concrete tunnel lining can be obtained by removing the lining seam and performing percolation denoising. Experimental results show that the proposed algorithm can accurately, quickly, and effectively detect the real surface cracks. Furthermore, it can fill the gap in the existing concrete tunnel lining surface crack detection by removing the lining seam.

Three-Dimensional Numerical Analysis of Compound Lining in Complex Underground Surge-Shaft Structure

Directory of Open Access Journals (Sweden)

Juntao Chen

2015-01-01

Full Text Available The mechanical behavior of lining structure of deep-embedded cylinder surge shaft with multifork tunnel is analyzed using three-dimensional nonlinear FEM. With the elastic-plastic constitutive relations of rock mass imported and the implicit bolt element and distributed concrete cracking model adopted, a computing method of complex surge shaft is presented for the simulation of underground excavations and concrete lining cracks. In order to reflect the interaction and initial gap between rock mass and concrete lining, a three-dimensional nonlinear interface element is adopted, which can take into account both the normal and tangential characteristics. By an actual engineering computation, the distortion characteristics and stress distribution rules of the dimensional multifork surge-shaft lining structure under different behavior are revealed. The results verify the rationality and feasibility of this computation model and method and provide a new idea and reference for the complex surge-shaft design and construction.

Deep learning with convolutional neural networks for EEG decoding and visualization.

Science.gov (United States)

Schirrmeister, Robin Tibor; Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

2017-11-01

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Deep learning with convolutional neural networks for EEG decoding and visualization

Science.gov (United States)

Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

2017-01-01

Abstract Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping. Hum Brain Mapp 38:5391–5420, 2017. © 2017 Wiley Periodicals, Inc. PMID:28782865

Deep Learning for ECG Classification

Science.gov (United States)

Pyakillya, B.; Kazachenko, N.; Mikhailovsky, N.

2017-10-01

The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.

Species-energy relationship in the deep sea: A test using the Quaternary fossil record

Science.gov (United States)

Hunt, G.; Cronin, T. M.; Roy, K.

2005-01-01

Little is known about the processes regulating species richness in deep-sea communities. Here we take advantage of natural experiments involving climate change to test whether predictions of the species-energy hypothesis hold in the deep sea. In addition, we test for the relationship between temperature and species richness predicted by a recent model based on biochemical kinetics of metabolism. Using the deep-sea fossil record of benthic foraminifera and statistical meta-analyses of temperature-richness and productivity-richness relationships in 10 deep-sea cores, we show that temperature but not productivity is a significant predictor of species richness over the past c. 130 000 years. Our results not only show that the temperature-richness relationship in the deep-sea is remarkably similar to that found in terrestrial and shallow marine habitats, but also that species richness tracks temperature change over geological time, at least on scales of c. 100 000 years. Thus, predicting biotic response to global climate change in the deep sea would require better understanding of how temperature regulates the occurrences and geographical ranges of species. ??2005 Blackwell Publishing Ltd/CNRS.

Multiagent cooperation and competition with deep reinforcement learning.

Directory of Open Access Journals (Sweden)

Ardi Tampuu

Full Text Available Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments.

Multiagent cooperation and competition with deep reinforcement learning

Science.gov (United States)

Kodelja, Dorian; Kuzovkin, Ilya; Korjus, Kristjan; Aru, Juhan; Aru, Jaan; Vicente, Raul

2017-01-01

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments. PMID:28380078

Multiagent cooperation and competition with deep reinforcement learning.

Science.gov (United States)

Tampuu, Ardi; Matiisen, Tambet; Kodelja, Dorian; Kuzovkin, Ilya; Korjus, Kristjan; Aru, Juhan; Aru, Jaan; Vicente, Raul

2017-01-01

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments.

Neopetrosiquinones A and B, sesquiterpene benzoquinones isolated from the deep-water sponge Neopetrosia cf. proxima.

Science.gov (United States)

Winder, Priscilla L; Baker, Heather L; Linley, Patricia; Guzmán, Esther A; Pomponi, Shirley A; Diaz, M Cristina; Reed, John K; Wright, Amy E

2011-11-15

Two new marine-derived sesquiterpene benzoquinones which we designate as neopetrosiquinones A (1) and B (2), have been isolated from a deep-water sponge of the family Petrosiidae. The structures were elucidated on the basis of their spectroscopic data. Compounds 1 and 2 inhibit the in vitro proliferation of the DLD-1 human colorectal adenocarcinoma cell line with IC(50) values of 3.7 and 9.8 μM, respectively, and the PANC-1 human pancreatic carcinoma cell line with IC(50) values of 6.1 and 13.8 μM, respectively. Neopetrosiquinone A (1) also inhibited the in vitro proliferation of the AsPC-1 human pancreatic carcinoma cell line with an IC(50) value of 6.1 μM. The compounds are structurally related to alisiaquinone A, cyclozonarone, and xestoquinone. Copyright © 2011 Elsevier Ltd. All rights reserved.

THE RESPONSE OF TUNNEL LINING ON THERMAL LOADING

Directory of Open Access Journals (Sweden)

Markéta Levorová

2012-07-01

Full Text Available The long-term functionality, i.e. stability of the lining of disposal tunnels is a precondition for the safe removal and reprocessing of spent nuclear waste from deep underground repositories in the near or more distant future. The reason for removing containers with radioactive waste from such repositories lies in the potential development of presently unavailable “perfect” technology for its reprocessing. The stability problems of the tunnel lining exposed to the long-term thermal load generated by the waste in the disposal container was the subject of one task of the European TIMODAZ project (Thermal Impact on the Damaged Zone around a Radioactive Waste Disposal in Clay Host Rocks. Research was carried out by means of physical modeling. Although the project was terminated in September 2010, recorded data is being further analyzed. This paper describes the design, construction and results of an in-situ model which has been built at the Underground Research Centre Josef in the Czech Republic.

Advanced Steel Microstructural Classification by Deep Learning Methods.

Science.gov (United States)

Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank

2018-02-01

The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

Predicting healthcare trajectories from medical records: A deep learning approach.

Science.gov (United States)

Pham, Trang; Tran, Truyen; Phung, Dinh; Venkatesh, Svetha

2017-05-01

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden - diabetes and mental health - the results show improved prediction accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

Numerical simulation of phenomenon on zonal disintegration in deep underground mining in case of unsupported roadway

Science.gov (United States)

Han, Fengshan; Wu, Xinli; Li, Xia; Zhu, Dekang

2018-02-01

Zonal disintegration phenomenon was found in deep mining roadway surrounding rock. It seriously affects the safety of mining and underground engineering and it may lead to the occurrence of natural disasters. in deep mining roadway surrounding rock, tectonic stress in deep mining roadway rock mass, horizontal stress is much greater than the vertical stress, When the direction of maximum principal stress is parallel to the axis of the roadway in deep mining, this is the main reasons for Zonal disintegration phenomenon. Using ABAQUS software to numerical simulation of the three-dimensional model of roadway rupture formation process systematically, and the study shows that when The Direction of maximum main stress in deep underground mining is along the roadway axial direction, Zonal disintegration phenomenon in deep underground mining is successfully reproduced by our numerical simulation..numerical simulation shows that using ABAQUA simulation can reproduce Zonal disintegration phenomenon and the formation process of damage of surrounding rock can be reproduced. which have important engineering practical significance.

Deep Sludge Gas Release Event Analytical Evaluation

International Nuclear Information System (INIS)

Sams, Terry L.

2013-01-01

Long Abstract. Full Text. The purpose of the Deep Sludge Gas Release Event Analytical Evaluation (DSGRE-AE) is to evaluate the postulated hypothesis that a hydrogen GRE may occur in Hanford tanks containing waste sludges at levels greater than previously experienced. There is a need to understand gas retention and release hazards in sludge beds which are 200 -300 inches deep. These sludge beds are deeper than historical Hanford sludge waste beds, and are created when waste is retrieved from older single-shell tanks (SST) and transferred to newer double-shell tanks (DST).Retrieval of waste from SSTs reduces the risk to the environment from leakage or potential leakage of waste into the ground from these tanks. However, the possibility of an energetic event (flammable gas accident) in the retrieval receiver DST is worse than slow leakage. Lines of inquiry, therefore, are (1) can sludge waste be stored safely in deep beds; (2) can gas release events (GRE) be prevented by periodically degassing the sludge (e.g., mixer pump); or (3) does the retrieval strategy need to be altered to limit sludge bed height by retrieving into additional DSTs? The scope of this effort is to provide expert advice on whether or not to move forward with the generation of deep beds of sludge through retrieval of C-Farm tanks. Evaluation of possible mitigation methods (e.g., using mixer pumps to release gas, retrieving into an additional DST) are being evaluated by a second team and are not discussed in this report. While available data and engineering judgment indicate that increased gas retention (retained gas fraction) in DST sludge at depths resulting from the completion of SST 241-C Tank Farm retrievals is not expected and, even if gas releases were to occur, they would be small and local, a positive USQ was declared (Occurrence Report EM-RP--WRPS-TANKFARM-2012-0014, 'Potential Exists for a Large Spontaneous Gas Release Event in Deep Settled Waste Sludge'). The purpose of this technical

Active semi-supervised learning method with hybrid deep belief networks.

Science.gov (United States)

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

Novel mesostructured inclusions in the epidermal lining of Artemia franciscana ovisacs show optical activity

Directory of Open Access Journals (Sweden)

Elena Hollergschwandtner

2017-10-01

Full Text Available Background Biomineralization, e.g., in sea urchins or mollusks, includes the assembly of mesoscopic superstructures from inorganic crystalline components and biopolymers. The resulting mesocrystals inspire biophysicists and material scientists alike, because of their extraordinary physical properties. Current efforts to replicate mesocrystal synthesis in vitro require understanding the principles of their self-assembly in vivo. One question, not addressed so far, is whether intracellular crystals of proteins can assemble with biopolymers into functional mesocrystal-like structures. During our electron microscopy studies into Artemia franciscana (Crustacea: Branchiopoda, we found initial evidence of such proteinaceous mesostructures. Results EM preparations with high-pressure freezing and accelerated freeze substitution revealed an extraordinary intracellular source of mesostructured inclusions in both the cyto-and nucleoplasm of the epidermal lining of ovisacs of A. franciscana. Confocal reflection microscopy not only confirmed our finding; it also revealed reflective, light dispersing activity of these flake-like structures, their positioning and orientation with respect to the ovisac inside. Both the striation of alternating electron dense and electron-lucent components and the sharp edges of the flakes indicate self-assembly of material of yet unknown origin under supposed participation of crystallization. However, selected area electron diffraction could not verify the status of crystallization. Energy dispersive X-ray analysis measured a marked increase in nitrogen within the flake-like inclusion, and the almost complete absence of elements that are typically involved in inorganic crystallization. This rise in nitrogen could possibility be related to higher package density of proteins, achieved by mesostructure assembly. Conclusions The ovisac lining of A. franciscana is endowed with numerous mesostructured inclusions that have not been

Subsurface Hybrid Power Options for Oil & Gas Production at Deep Ocean Sites

Energy Technology Data Exchange (ETDEWEB)

Farmer, J C; Haut, R; Jahn, G; Goldman, J; Colvin, J; Karpinski, A; Dobley, A; Halfinger, J; Nagley, S; Wolf, K; Shapiro, A; Doucette, P; Hansen, P; Oke, A; Compton, D; Cobb, M; Kopps, R; Chitwood, J; Spence, W; Remacle, P; Noel, C; Vicic, J; Dee, R

2010-02-19

An investment in deep-sea (deep-ocean) hybrid power systems may enable certain off-shore oil and gas exploration and production. Advanced deep-ocean drilling and production operations, locally powered, may provide commercial access to oil and gas reserves otherwise inaccessible. Further, subsea generation of electrical power has the potential of featuring a low carbon output resulting in improved environmental conditions. Such technology therefore, enhances the energy security of the United States in a green and environmentally friendly manner. The objective of this study is to evaluate alternatives and recommend equipment to develop into hybrid energy conversion and storage systems for deep ocean operations. Such power systems will be located on the ocean floor and will be used to power offshore oil and gas exploration and production operations. Such power systems will be located on the oceans floor, and will be used to supply oil and gas exploration activities, as well as drilling operations required to harvest petroleum reserves. The following conceptual hybrid systems have been identified as candidates for powering sub-surface oil and gas production operations: (1) PWR = Pressurized-Water Nuclear Reactor + Lead-Acid Battery; (2) FC1 = Line for Surface O{sub 2} + Well Head Gas + Reformer + PEMFC + Lead-Acid & Li-Ion Batteries; (3) FC2 = Stored O2 + Well Head Gas + Reformer + Fuel Cell + Lead-Acid & Li-Ion Batteries; (4) SV1 = Submersible Vehicle + Stored O{sub 2} + Fuel Cell + Lead-Acid & Li-Ion Batteries; (5) SV2 = Submersible Vehicle + Stored O{sub 2} + Engine or Turbine + Lead-Acid & Li-Ion Batteries; (6) SV3 = Submersible Vehicle + Charge at Docking Station + ZEBRA & Li-Ion Batteries; (7) PWR TEG = PWR + Thermoelectric Generator + Lead-Acid Battery; (8) WELL TEG = Thermoelectric Generator + Well Head Waste Heat + Lead-Acid Battery; (9) GRID = Ocean Floor Electrical Grid + Lead-Acid Battery; and (10) DOC = Deep Ocean Current + Lead-Acid Battery.

Genetic diversity of archaea in deep-sea hydrothermal vent environments.

OpenAIRE

Takai, K; Horikoshi, K

1999-01-01

Molecular phylogenetic analysis of naturally occurring archaeal communities in deep-sea hydrothermal vent environments was carried out by PCR-mediated small subunit rRNA gene (SSU rDNA) sequencing. As determined through partial sequencing of rDNA clones amplified with archaea-specific primers, the archaeal populations in deep-sea hydrothermal vent environments showed a great genetic diversity, and most members of these populations appeared to be uncultivated and unidentified organisms. In the...

X-ray heating and ionization of broad-emission-line regions in QSO's and active galaxies

International Nuclear Information System (INIS)

Weisheit, J.C.; Shields, G.A.; Tarter, C.B.

1980-07-01

Absorption of x-rays deep within the broad-line emitting clouds in QSO's and the nuclei of active galaxies creates extensive zones of warm (T approx. 10 4 K), partially ionized N/sub e//N approx. 0.1) gas. Because Lyman alpha photons are trapped in these regions, the x-ray energy is efficiently channeled into Balmer lines collisionally excited from the n = 2 level. The HI regions plus the HII regions created by ultraviolet photons illuminating the surfaces of the clouds give rise to integrated Lα/Hα line emission ratios between 1 and 2. Enhanced MgII line emission from the HI regions gives rise to integrated MgII/Hα ratios near 0.5. The OI line lambda 8446 is efficiently pumped by trapped Hα photons and in the x-ray heated zone an intensity ratio I (lambda 8446)/I(Hα) approx. < 0.1 is calculated. All of these computed ratios now are in agreement with observations

Deep vein thrombosis of the leg

Energy Technology Data Exchange (ETDEWEB)

Lee, Eun Hee; Rhee, Kwang Woo; Jeon, Suk Chul; Joo, Kyung Bin; Lee, Seung Ro; Seo, Heung Suk; Hahm, Chang Kok [College of Medicine, Hanyang University, Seoul (Korea, Republic of)

1987-04-15

Ascending contrast venography is the definitive standard method for the diagnosis of deep vein thrombosis (DVT) of the lower extremities. Authors analysed 22 cases of DVT clinically and radiographically. 1.The patients ranged in age from 15 to 70 yrs and the most prevalent age group was 7th decade (31%). There was an equal distribution of males and females. 2.In 11 cases of 22 cases, variable etiologic factors were recognized, such as abdominal surgery, chronic bedridden state, local trauma on the leg, pregnancy, postpartum, Behcet's syndrome, iliac artery aneurysm, and chronic medication of estrogen. 3.Nineteen cases out of 22 cases showed primary venographic signs of DVT, such as well-defined filling defect in opacified veins and narrowed, irregularly filled venous lumen. In only 3 cases, the diagnosis of DVT was base upon the segmental nonvisualization of deep veins with good opacification of proximal and distal veins and presence of collaterals. 4.Extent of thrombosis: 3 cases were confined to calf vein, 4 cases extended to femoral vein, and 15 cases had involvement above iliac vein. 5.In 17 cases involving relatively long segment of deep veins, propagation pattern of thrombus was evaluated by its radiologic morphology according to the age of thrombus: 9 cases suggested central or antegrade propagation pattern and 8 cases, peripheral or retrograde pattern. 6.None of 22 cases showed clinical evidence of pulmonary embolism. The cause of the rarity of pulmonary embolism in Korean in presumed to be related to the difference in major involving site and propagation pattern of DVT in the leg.

Deep vein thrombosis of the leg

International Nuclear Information System (INIS)

Lee, Eun Hee; Rhee, Kwang Woo; Jeon, Suk Chul; Joo, Kyung Bin; Lee, Seung Ro; Seo, Heung Suk; Hahm, Chang Kok

1987-01-01

Ascending contrast venography is the definitive standard method for the diagnosis of deep vein thrombosis (DVT) of the lower extremities. Authors analysed 22 cases of DVT clinically and radiographically. 1.The patients ranged in age from 15 to 70 yrs and the most prevalent age group was 7th decade (31%). There was an equal distribution of males and females. 2.In 11 cases of 22 cases, variable etiologic factors were recognized, such as abdominal surgery, chronic bedridden state, local trauma on the leg, pregnancy, postpartum, Behcet's syndrome, iliac artery aneurysm, and chronic medication of estrogen. 3.Nineteen cases out of 22 cases showed primary venographic signs of DVT, such as well-defined filling defect in opacified veins and narrowed, irregularly filled venous lumen. In only 3 cases, the diagnosis of DVT was base upon the segmental nonvisualization of deep veins with good opacification of proximal and distal veins and presence of collaterals. 4.Extent of thrombosis: 3 cases were confined to calf vein, 4 cases extended to femoral vein, and 15 cases had involvement above iliac vein. 5.In 17 cases involving relatively long segment of deep veins, propagation pattern of thrombus was evaluated by its radiologic morphology according to the age of thrombus: 9 cases suggested central or antegrade propagation pattern and 8 cases, peripheral or retrograde pattern. 6.None of 22 cases showed clinical evidence of pulmonary embolism. The cause of the rarity of pulmonary embolism in Korean in presumed to be related to the difference in major involving site and propagation pattern of DVT in the leg

Deep surface rolling for fatigue life enhancement of laser clad aircraft aluminium alloy

Energy Technology Data Exchange (ETDEWEB)

Zhuang, W., E-mail: wyman.zhuang@dsto.defence.gov.au [Aerospace Division, Defence Science and Technology Organisation, 506 Lorimer Street, Fishermans Bend, Victoria 3207 (Australia); Liu, Q.; Djugum, R.; Sharp, P.K. [Aerospace Division, Defence Science and Technology Organisation, 506 Lorimer Street, Fishermans Bend, Victoria 3207 (Australia); Paradowska, A. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW 2232 (Australia)

2014-11-30

Highlights: • Deep surface rolling as a post-repair enhancement technology was applied to the laser cladded 7075-T651 aluminium alloy specimens that simulated corrosion damage blend-out repair. • The residual stresses induced by the deep surface rolling process were measured. • The deep surface rolling process can introduce deep and high magnitude compressive residual stresses beyond the laser clad and substrate interface. • Spectrum fatigue test showed the fatigue life was significantly increased by deep surface rolling. - Abstract: Deep surface rolling can introduce deep compressive residual stresses into the surface of aircraft metallic structure to extend its fatigue life. To develop cost-effective aircraft structural repair technologies such as laser cladding, deep surface rolling was considered as an advanced post-repair surface enhancement technology. In this study, aluminium alloy 7075-T651 specimens with a blend-out region were first repaired using laser cladding technology. The surface of the laser cladding region was then treated by deep surface rolling. Fatigue testing was subsequently conducted for the laser clad, deep surface rolled and post-heat treated laser clad specimens. It was found that deep surface rolling can significantly improve the fatigue life in comparison with the laser clad baseline repair. In addition, three dimensional residual stresses were measured using neutron diffraction techniques. The results demonstrate that beneficial compressive residual stresses induced by deep surface rolling can reach considerable depths (more than 1.0 mm) below the laser clad surface.

Deep surface rolling for fatigue life enhancement of laser clad aircraft aluminium alloy

International Nuclear Information System (INIS)

Zhuang, W.; Liu, Q.; Djugum, R.; Sharp, P.K.; Paradowska, A.

2014-01-01

Highlights: • Deep surface rolling as a post-repair enhancement technology was applied to the laser cladded 7075-T651 aluminium alloy specimens that simulated corrosion damage blend-out repair. • The residual stresses induced by the deep surface rolling process were measured. • The deep surface rolling process can introduce deep and high magnitude compressive residual stresses beyond the laser clad and substrate interface. • Spectrum fatigue test showed the fatigue life was significantly increased by deep surface rolling. - Abstract: Deep surface rolling can introduce deep compressive residual stresses into the surface of aircraft metallic structure to extend its fatigue life. To develop cost-effective aircraft structural repair technologies such as laser cladding, deep surface rolling was considered as an advanced post-repair surface enhancement technology. In this study, aluminium alloy 7075-T651 specimens with a blend-out region were first repaired using laser cladding technology. The surface of the laser cladding region was then treated by deep surface rolling. Fatigue testing was subsequently conducted for the laser clad, deep surface rolled and post-heat treated laser clad specimens. It was found that deep surface rolling can significantly improve the fatigue life in comparison with the laser clad baseline repair. In addition, three dimensional residual stresses were measured using neutron diffraction techniques. The results demonstrate that beneficial compressive residual stresses induced by deep surface rolling can reach considerable depths (more than 1.0 mm) below the laser clad surface

Deep-sea biodiversity in the Mediterranean Sea: the known, the unknown, and the unknowable.

Directory of Open Access Journals (Sweden)

Roberto Danovaro

Full Text Available Deep-sea ecosystems represent the largest biome of the global biosphere, but knowledge of their biodiversity is still scant. The Mediterranean basin has been proposed as a hot spot of terrestrial and coastal marine biodiversity but has been supposed to be impoverished of deep-sea species richness. We summarized all available information on benthic biodiversity (Prokaryotes, Foraminifera, Meiofauna, Macrofauna, and Megafauna in different deep-sea ecosystems of the Mediterranean Sea (200 to more than 4,000 m depth, including open slopes, deep basins, canyons, cold seeps, seamounts, deep-water corals and deep-hypersaline anoxic basins and analyzed overall longitudinal and bathymetric patterns. We show that in contrast to what was expected from the sharp decrease in organic carbon fluxes and reduced faunal abundance, the deep-sea biodiversity of both the eastern and the western basins of the Mediterranean Sea is similarly high. All of the biodiversity components, except Bacteria and Archaea, displayed a decreasing pattern with increasing water depth, but to a different extent for each component. Unlike patterns observed for faunal abundance, highest negative values of the slopes of the biodiversity patterns were observed for Meiofauna, followed by Macrofauna and Megafauna. Comparison of the biodiversity associated with open slopes, deep basins, canyons, and deep-water corals showed that the deep basins were the least diverse. Rarefaction curves allowed us to estimate the expected number of species for each benthic component in different bathymetric ranges. A large fraction of exclusive species was associated with each specific habitat or ecosystem. Thus, each deep-sea ecosystem contributes significantly to overall biodiversity. From theoretical extrapolations we estimate that the overall deep-sea Mediterranean biodiversity (excluding prokaryotes reaches approximately 2805 species of which about 66% is still undiscovered. Among the biotic components

A nonintrusive temperature measuring system for estimating deep body temperature in bed.

Science.gov (United States)

Sim, S Y; Lee, W K; Baek, H J; Park, K S

2012-01-01

Deep body temperature is an important indicator that reflects human being's overall physiological states. Existing deep body temperature monitoring systems are too invasive to apply to awake patients for a long time. Therefore, we proposed a nonintrusive deep body temperature measuring system. To estimate deep body temperature nonintrusively, a dual-heat-flux probe and double-sensor probes were embedded in a neck pillow. When a patient uses the neck pillow to rest, the deep body temperature can be assessed using one of the thermometer probes embedded in the neck pillow. We could estimate deep body temperature in 3 different sleep positions. Also, to reduce the initial response time of dual-heat-flux thermometer which measures body temperature in supine position, we employed the curve-fitting method to one subject. And thereby, we could obtain the deep body temperature in a minute. This result shows the possibility that the system can be used as practical temperature monitoring system with appropriate curve-fitting model. In the next study, we would try to establish a general fitting model that can be applied to all of the subjects. In addition, we are planning to extract meaningful health information such as sleep structure analysis from deep body temperature data which are acquired from this system.

Ensemble Network Architecture for Deep Reinforcement Learning

Directory of Open Access Journals (Sweden)

Xi-liang Chen

2018-01-01

Full Text Available The popular deep Q learning algorithm is known to be instability because of the Q-value’s shake and overestimation action values under certain conditions. These issues tend to adversely affect their performance. In this paper, we develop the ensemble network architecture for deep reinforcement learning which is based on value function approximation. The temporal ensemble stabilizes the training process by reducing the variance of target approximation error and the ensemble of target values reduces the overestimate and makes better performance by estimating more accurate Q-value. Our results show that this architecture leads to statistically significant better value evaluation and more stable and better performance on several classical control tasks at OpenAI Gym environment.

DAPs: Deep Action Proposals for Action Understanding

KAUST Repository

Escorcia, Victor

2016-09-17

Object proposals have contributed significantly to recent advances in object understanding in images. Inspired by the success of this approach, we introduce Deep Action Proposals (DAPs), an effective and efficient algorithm for generating temporal action proposals from long videos. We show how to take advantage of the vast capacity of deep learning models and memory cells to retrieve from untrimmed videos temporal segments, which are likely to contain actions. A comprehensive evaluation indicates that our approach outperforms previous work on a large scale action benchmark, runs at 134 FPS making it practical for large-scale scenarios, and exhibits an appealing ability to generalize, i.e. to retrieve good quality temporal proposals of actions unseen in training.

Development of Elite BPH-Resistant Wide-Spectrum Restorer Lines for Three and Two Line Hybrid Rice.

Science.gov (United States)

Fan, Fengfeng; Li, Nengwu; Chen, Yunping; Liu, Xingdan; Sun, Heng; Wang, Jie; He, Guangcun; Zhu, Yingguo; Li, Shaoqing

2017-01-01

Hybrid rice has contributed significantly to the world food security. Breeding of elite high-yield, strong-resistant broad-spectrum restorer line is an important strategy for hybrid rice in commercial breeding programs. Here, we developed three elite brown planthopper (BPH)-resistant wide-spectrum restorer lines by pyramiding big-panicle gene Gn8.1 , BPH-resistant genes Bph6 and Bph9 , fertility restorer genes Rf3, Rf4, Rf5 , and Rf6 through molecular marker assisted selection. Resistance analysis revealed that the newly developed restorer lines showed stronger BPH-resistance than any of the single-gene donor parent Luoyang-6 and Luoyang-9. Moreover, the three new restorer lines had broad spectrum recovery capabilities for Honglian CMS, Wild abortive CMS and two-line GMS sterile lines, and higher grain yields than that of the recurrent parent 9,311 under nature field conditions. Importantly, the hybrid crosses also showed good performance for grain yield and BPH-resistance. Thus, the development of elite BPH-resistant wide-spectrum restorer lines has a promising future for breeding of broad spectrum BPH-resistant high-yield varieties.

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

Science.gov (United States)

Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

2017-11-13

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

Deep Charging Evaluation of Satellite Power and Communication System Components

Science.gov (United States)

Schneider, T. A.; Vaughn, J. A.; Chu, B.; Wong, F.; Gardiner, G.; Wright, K. H.; Phillips, B.

2016-01-01

Deep charging, in contrast to surface charging, focuses on electron penetration deep into insulating materials applied over conductors. A classic example of this scenario is an insulated wire. Deep charging can pose a threat to material integrity, and to sensitive electronics, when it gives rise to an electrostatic discharge or arc. With the advent of Electric Orbit Raising, which requires spiraling through Earth's radiation belts, satellites are subjected to high energy electron environments which they normally would not encounter. Beyond Earth orbit, missions to Jupiter and Saturn face deep charging concerns due to the high energy radiation environments. While predictions can be made about charging in insulating materials, it is difficult to extend those predictions to complicated geometries, such as the case of an insulating coating around a small wire, or a non-uniform silicone grouting on a bus bar. Therefore, to conclusively determine the susceptibility of a system to arcs from deep charging, experimental investigations must be carried out. This paper will describe the evaluation carried out by NASA's Marshall Space Flight Center on subscale flight-like samples developed by Space Systems/Loral, LLC. Specifically, deep charging evaluations of solar array wire coupons, a photovoltaic cell coupon, and a coaxial microwave transmission cable, will be discussed. The results of each evaluation will be benchmarked against control sample tests, as well as typical power system levels, to show no significant deep charging threat existed for this set of samples under the conditions tested.

Deep and intermediate mediterranean water in the western Alboran Sea

Science.gov (United States)

Parrilla, Gregorio; Kinder, Thomas H.; Preller, Ruth H.

1986-01-01

Hydrographic and current meter data, obtained during June to October 1982, and numerical model experiments are used to study the distribution and flow of Mediterranean waters in the western Alboran Sea. The Intermediate Water is more pronounced in the northern three-fourths of the sea, but its distribution is patchy as manifested by variability of the temperature and salinity maxima at scales ≤10 km. Current meters in the lower Intermediate Water showed mean flow toward the Strait at 2 cm s -1. A reversal of this flow lasted about 2 weeks. A rough estimate of the mean westward Intermediate Water transport was 0.4 × 10 6 m 3 s -1, about one-third of the total outflow, so that the best estimates of the contributions of traditionally defined Intermediate Water and Deep Water account for only about one-half of the total outflow. The Deep Water was uplifted against the southern continental slope from Alboran Island (3°W) to the Strait. There was also a similar but much weaker banking against the Spanish slope, but a deep current record showed that the eastward recirculation implied by this banking is probably intermittent. Two-layer numerical model experiments simulated the Intermediate Water flow with a flat bottom and the Deep Water with realistic bottom topography. Both experiments replicated the major circulation features, and the Intermediate Water flow was concentrated in the north because of rotation and the Deep Water flow in the south because of topographic control.

Waste disposal in the deep ocean: An overview

International Nuclear Information System (INIS)

O'Connor, T.P.; Kester, D.R.; Burt, W.V.; Capuzzo, J.M.; Park, P.K.; Duedall, I.W.

1985-01-01

Incineration at sea, industrial and sewage waste disposal in the surface mixing zone, and disposal of low-level nuclear wastes, obsolete munitions, and nerve gas onto the seafloor have been the main uses of the deep sea for waste management. In 1981 the wastes disposed of in the deep sea consisted of 48 X 10/sup 4/ t of liquid industrial wastes and 2 X 10/sup 4/ t of sewage sludge by the United States; 1.5 X 10/sup 4/ t (solids) of sewage sludge by the Federal Republic of German; 5300 t of liquid industrial wastes by Denmark; 99 t of solid industrial wastes by the United Kingdom; and 9400 t of low-level radioactive wastes by several European countries. Also in 1981 at-sea incineration of slightly more than 10/sup 5/ t of organic wastes from Belgium, France, the Federal Republic of Germany, the Netherlands, Norway, Sweden, and the United Kingdom was carried out in the North Sea. Unique oceanographic features of the deep sea include its large dilution capacity; the long residence time of deep-sea water (on the order of 10/sup 2/ y); low biological productivity in the surface water of the open ocean (≅50 g m/sup -2/ of carbon per year); the existence of an oxygen minimum zone at several hundred meters deep in the mid-latitudes; and the abyssal-clay regions showing sedimentary records of tens of millions of years of slow, uninterrupted deposition of fine-grained clay. Any deep-sea waste disposal strategy must take into account oceanic processes and current scientific knowledge in order to attain a safe solution that will last for centuries

Deep learning for image classification

Science.gov (United States)

McCoppin, Ryan; Rizki, Mateen

2014-06-01

This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.

Science.gov (United States)

Zheng, Yu-Jun; Sheng, Wei-Guo; Sun, Xing-Ming; Chen, Sheng-Yong

2017-12-01

Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

Deep learning for computational chemistry

Energy Technology Data Exchange (ETDEWEB)

Goh, Garrett B. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Hodas, Nathan O. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Vishnu, Abhinav [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354

2017-03-08

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.

Deep learning for computational chemistry.

Science.gov (United States)

Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

2017-06-15

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Diagnosis of deep vein thrombosis using autologous indium-III-labelled platelets

International Nuclear Information System (INIS)

Fenech, A.; Hussey, J.K.; Smith, F.W.; Dendy, P.P.; Bennett, B.; Douglas, A.S.

1981-01-01

Forty-eight patients who had undergone surgical reduction of a fractured neck of femur or in whom deep vein thrombosis was suspected clinically were studied by ascending phlebography and imaging after injection of autologous indium-III-labelled platelets to assess the accuracy and value of the radioisotopic technique in diagnosing deep vein thrombosis. Imaging was performed with a wide-field gammacamera linked with data display facilities. Phlebography showed thrombi in 26 out of 54 limbs examined and a thrombus in the inferior vena cava of one patient; imaging the labelled platelets showed the thrombi in 24 of the 26 limbs and the thrombus in the inferior vena cava. The accumulation of indium-III at sites corresponding to those at which venous thrombi have been shown phlebographically indicates that this radioisotopic technique is a useful addition to methods already available for the detection of deep vein thrombosis. (author)

Diagnosis of deep vein thrombosis using autologous indium-III-labelled platelets

Energy Technology Data Exchange (ETDEWEB)

Fenech, A.; Hussey, J.K.; Smith, F.W.; Dendy, P.P.; Bennett, B.; Douglas, A.S. (Aberdeen Univ. (UK))

1981-03-28

Forty-eight patients who had undergone surgical reduction of a fractured neck of femur or in whom deep vein thrombosis was suspected clinically were studied by ascending phlebography and imaging after injection of autologous indium-III-labelled platelets to assess the accuracy and value of the radioisotopic technique in diagnosing deep vein thrombosis. Imaging was performed with a wide-field gamma camera linked with data display facilities. Phlebography showed thrombi in 26 out of 54 limbs examined and a thrombus in the inferior vena cava of one patient; imaging the labelled platelets showed the thrombi in 24 of the 26 limbs and the thrombus in the inferior vena cava. The accumulation of indium-III at sites corresponding to those at which venous thrombi have been shown phlebographically indicates that this radioisotopic technique is a useful addition to methods already available for the detection of deep vein thrombosis.

A DEEP CHANDRA ACIS STUDY OF NGC 4151. II. THE INNERMOST EMISSION LINE REGION AND STRONG EVIDENCE FOR RADIO JET-NLR CLOUD COLLISION

International Nuclear Information System (INIS)

Wang Junfeng; Fabbiano, Giuseppina; Elvis, Martin; Risaliti, Guido; Karovska, Margarita; Zezas, Andreas; Mundell, Carole G.

2011-01-01

We have studied the X-ray emission within the inner ∼150 pc radius of NGC 4151 by constructing high spatial resolution emission line images of blended O VII, O VIII, and Ne IX. These maps show extended structures that are spatially correlated with the radio outflow and optical [O III] emission. We find strong evidence for jet-gas cloud interaction, including morphological correspondences with regions of X-ray enhancement, peaks of near-infrared [Fe II] emission, and optical clouds. In these regions, moreover, we find evidence of elevated Ne IX/O VII ratios; the X-ray emission of these regions also exceeds that expected from nuclear photoionization. Spectral fitting reveals the presence of a collisionally ionized component. The thermal energy of the hot gas suggests that ∼> 0.1% of the estimated jet power is deposited into the host interstellar medium through interaction between the radio jet and the dense medium of the circumnuclear region. We find possible pressure equilibrium between the collisionally ionized hot gas and the photoionized line-emitting cool clouds. We also obtain constraints on the extended iron and silicon fluorescent emission. Both lines are spatially unresolved. The upper limit on the contribution of an extended emission region to the Fe Kα emission is ∼< 5% of the total, in disagreement with a previous claim that 65% of the Fe Kα emission originates in the extended narrow line region.

What Really is Deep Learning Doing?

OpenAIRE

Xiong, Chuyu

2017-01-01

Deep learning has achieved a great success in many areas, from computer vision to natural language processing, to game playing, and much more. Yet, what deep learning is really doing is still an open question. There are a lot of works in this direction. For example, [5] tried to explain deep learning by group renormalization, and [6] tried to explain deep learning from the view of functional approximation. In order to address this very crucial question, here we see deep learning from perspect...

New results on the 3-loop heavy flavor corrections in deep-inelastic scattering

Energy Technology Data Exchange (ETDEWEB)

Behring, A.; Bluemlein, J.; Freitas, A. de [Deutsches Elektronen-Synchrotron, Zeuthen (Germany); and others

2013-12-15

We report on recent progress in the calculation of the 3-loop massiveWilson coefficients in deep inelastic scattering at general values of N for neutral- and charged-current reactions in the asymptotic region Q{sup 2}>>m{sup 2}. Four new out of eight massive operator matrix elements and Wilson coefficients have been obtained recently. We also discuss recent results on Feynman graphs containing two massive fermion lines and present complete results for the bubble topologies for all processes.

A new cytotoxic sterol methoxymethyl ether from a deep water marine sponge Scleritoderma sp. cf. paccardi.

Science.gov (United States)

Gunasekera, S P; Kelly-Borges, M; Longley, R E

1996-02-01

24(R)-Methyl-5 alpha-cholest-7-enyl 3 beta-methoxymethyl ether (1), a new sterol ether, has been isolated from a deep-water marine sponge Scleritoderma sp. cf. paccardi. Compound 1 exhibited in vitro cytotoxicity against the cultured murine P-388 tumor cell line with an IC50 of 2.3 micrograms/mL. The isolation and structure elucidation of 1 by NMR spectroscopy is described.

Taoism and Deep Ecology.

Science.gov (United States)

Sylvan, Richard; Bennett, David

1988-01-01

Contrasted are the philosophies of Deep Ecology and ancient Chinese. Discusses the cosmology, morality, lifestyle, views of power, politics, and environmental philosophies of each. Concludes that Deep Ecology could gain much from Taoism. (CW)

Deep learning for staging liver fibrosis on CT: a pilot study.

Science.gov (United States)

Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Abe, Osamu; Kiryu, Shigeru

2018-05-14

To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images. This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (F DLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance. The F DLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p deep learning model based on CT images, with moderate performance. • Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance. • Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging. • Further improvement are necessary before utilisation in clinical settings.

Deep structure of crust and the upper mantle of the Mendeleev Rise on the Arktic­-2012 DSS profile

DEFF Research Database (Denmark)

Kashubin, Sergey; Petrov, Oleg; Artemieva, Irina

2016-01-01

During high­latitude combined geological and geophysical expedition “Arctic­-2012”, deep seismic sounding (DSS) with ocean bottom seismometers were carried out in the Arctic Ocean along the line 740 km long, crossing the Mendeleev Rise at about 77° N. Crustal and upper mantle Vp­velocity and Vp...

Optimization of antibody immobilization for on-line or off-line immunoaffinity chromatography

DEFF Research Database (Denmark)

Beyer, Natascha Helena; Schou, Christian; Højrup, Peter

2009-01-01

-POROS. Protein G-based matrices are very stable showing essentially no decline in performance after 50 application-wash-elution-reequilibration cycles and being easily prepared within 2-3 h of working time with a typical antibody coupling yield of above 80%. In off-line applications where constant flow....... A systematic study was conducted to determine the most versatile antibody immobilization method for use in on-line and off-line IA chromatography applications using commonly accessible immobilization methods. Four chemistries were examined using polyclonal and monoclonal antibodies and antibody fragments. We...

Shakeout: A New Approach to Regularized Deep Neural Network Training.

Science.gov (United States)

Kang, Guoliang; Li, Jun; Tao, Dacheng

2018-05-01

Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines , and regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.

Quantification of deep medullary veins at 7 T brain MRI

Energy Technology Data Exchange (ETDEWEB)

Kuijf, Hugo J.; Viergever, Max A.; Vincken, Koen L. [University Medical Center Utrecht, Image Sciences Institute, Utrecht (Netherlands); Bouvy, Willem H.; Razoux Schultz, Tom B.; Biessels, Geert Jan [University Medical Center Utrecht, Department of Neurology, Brain Center Rudolf Magnus, Utrecht (Netherlands); Zwanenburg, Jaco J.M. [University Medical Center Utrecht, Image Sciences Institute, Utrecht (Netherlands); University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands)

2016-10-15

Deep medullary veins support the venous drainage of the brain and may display abnormalities in the context of different cerebrovascular diseases. We present and evaluate a method to automatically detect and quantify deep medullary veins at 7 T. Five participants were scanned twice, to assess the robustness and reproducibility of manual and automated vein detection. Additionally, the method was evaluated on 24 participants to demonstrate its application. Deep medullary veins were assessed within an automatically created region-of-interest around the lateral ventricles, defined such that all veins must intersect it. A combination of vesselness, tubular tracking, and hysteresis thresholding located individual veins, which were quantified by counting and computing (3-D) density maps. Visual assessment was time-consuming (2 h/scan), with an intra-/inter-observer agreement on absolute vein count of ICC = 0.76 and 0.60, respectively. The automated vein detection showed excellent inter-scan reproducibility before (ICC = 0.79) and after (ICC = 0.88) visually censoring false positives. It had a positive predictive value of 71.6 %. Imaging at 7 T allows visualization and quantification of deep medullary veins. The presented method offers fast and reliable automated assessment of deep medullary veins. (orig.)

Classification of radiolarian images with hand-crafted and deep features

Science.gov (United States)

Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

2017-12-01

Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

Covariant electromagnetic field lines

Science.gov (United States)

Hadad, Y.; Cohen, E.; Kaminer, I.; Elitzur, A. C.

2017-08-01

Faraday introduced electric field lines as a powerful tool for understanding the electric force, and these field lines are still used today in classrooms and textbooks teaching the basics of electromagnetism within the electrostatic limit. However, despite attempts at generalizing this concept beyond the electrostatic limit, such a fully relativistic field line theory still appears to be missing. In this work, we propose such a theory and define covariant electromagnetic field lines that naturally extend electric field lines to relativistic systems and general electromagnetic fields. We derive a closed-form formula for the field lines curvature in the vicinity of a charge, and show that it is related to the world line of the charge. This demonstrates how the kinematics of a charge can be derived from the geometry of the electromagnetic field lines. Such a theory may also provide new tools in modeling and analyzing electromagnetic phenomena, and may entail new insights regarding long-standing problems such as radiation-reaction and self-force. In particular, the electromagnetic field lines curvature has the attractive property of being non-singular everywhere, thus eliminating all self-field singularities without using renormalization techniques.

pathways to deep decarbonization - 2014 report

International Nuclear Information System (INIS)

Sachs, Jeffrey; Guerin, Emmanuel; Mas, Carl; Schmidt-Traub, Guido; Tubiana, Laurence; Waisman, Henri; Colombier, Michel; Bulger, Claire; Sulakshana, Elana; Zhang, Kathy; Barthelemy, Pierre; Spinazze, Lena; Pharabod, Ivan

2014-09-01

The Deep Decarbonization Pathways Project (DDPP) is a collaborative initiative to understand and show how individual countries can transition to a low-carbon economy and how the world can meet the internationally agreed target of limiting the increase in global mean surface temperature to less than 2 degrees Celsius (deg. C). Achieving the 2 deg. C limit will require that global net emissions of greenhouse gases (GHG) approach zero by the second half of the century. This will require a profound transformation of energy systems by mid-century through steep declines in carbon intensity in all sectors of the economy, a transition we call 'deep decarbonization.' Successfully transition to a low-carbon economy will require unprecedented global cooperation, including a global cooperative effort to accelerate the development and diffusion of some key low carbon technologies. As underscored throughout this report, the results of the DDPP analyses remain preliminary and incomplete. The DDPP proceeds in two phases. This 2014 report describes the DDPP's approach to deep decarbonization at the country level and presents preliminary findings on technically feasible pathways to deep decarbonization, utilizing technology assumptions and timelines provided by the DDPP Secretariat. At this stage we have not yet considered the economic and social costs and benefits of deep decarbonization, which will be the topic for the next report. The DDPP is issuing this 2014 report to the UN Secretary-General Ban Ki-moon in support of the Climate Leaders' Summit at the United Nations on September 23, 2014. This 2014 report by the Deep Decarbonization Pathway Project (DDPP) summarizes preliminary findings of the technical pathways developed by the DDPP Country Research Partners with the objective of achieving emission reductions consistent with limiting global warming to less than 2 deg. C., without, at this stage, consideration of economic and social costs and benefits. The DDPP is a knowledge

Is Multitask Deep Learning Practical for Pharma?

Science.gov (United States)

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

Study of microorganisms present in deep geologic formations

International Nuclear Information System (INIS)

Camus, H.; Lion, R.; Bianchi, A.; Garcin, J.

1987-01-01

This work has been executed in the scope of the studies on high activity radioactive wastes storage in deep geological environments. The authors make reference to an as complete as possible literature on the existence of microorganisms in those environments or under similar conditions. Then they describe the equipment and methods they have implemented to perform their study of the populations present in three deep-reaching drill-holes in Auriat (France), Mol (Belgique) and Troon (Great Britain). The results of the study exhibit the presence of a certain biological activity, well adapted to that particular life environment. Strains appear to be very varied from the taxonomic point of view and seemingly show an important potential of mineral alteration when provided with an adequate source of energy. Complementary studies, using advanced techniques such as those employed during the work forming the basis of this paper, seem necessary for a more accurate evaluation of long-term risks of perturbation of a deep storage site [fr

Unexpected Positive Buoyancy in Deep Sea Sharks, Hexanchus griseus, and a Echinorhinus cookei.

Science.gov (United States)

Nakamura, Itsumi; Meyer, Carl G; Sato, Katsufumi

2015-01-01

We do not expect non air-breathing aquatic animals to exhibit positive buoyancy. Sharks, for example, rely on oil-filled livers instead of gas-filled swim bladders to increase their buoyancy, but are nonetheless ubiquitously regarded as either negatively or neutrally buoyant. Deep-sea sharks have particularly large, oil-filled livers, and are believed to be neutrally buoyant in their natural habitat, but this has never been confirmed. To empirically determine the buoyancy status of two species of deep-sea sharks (bluntnose sixgill sharks, Hexanchus griseus, and a prickly shark, Echinorhinus cookei) in their natural habitat, we used accelerometer-magnetometer data loggers to measure their swimming performance. Both species of deep-sea sharks showed similar diel vertical migrations: they swam at depths of 200-300 m at night and deeper than 500 m during the day. Ambient water temperature was around 15°C at 200-300 m but below 7°C at depths greater than 500 m. During vertical movements, all deep-sea sharks showed higher swimming efforts during descent than ascent to maintain a given swimming speed, and were able to glide uphill for extended periods (several minutes), indicating that these deep-sea sharks are in fact positively buoyant in their natural habitats. This positive buoyancy may adaptive for stealthy hunting (i.e. upward gliding to surprise prey from underneath) or may facilitate evening upward migrations when muscle temperatures are coolest, and swimming most sluggish, after spending the day in deep, cold water. Positive buoyancy could potentially be widespread in fish conducting daily vertical migration in deep-sea habitats.

Lip line preference for variant face types.

Science.gov (United States)

Anwar, Nabila; Fida, Mubassar

2012-06-01

To determine the effect of altered lip line on attractiveness and to find preferred lip line for vertical face types in both genders. Cross-sectional analytical study. The Aga Khan University Hospital, Karachi, from May to July 2009. Photographs of two selected subjects were altered to produce three face types for the same individual with the aim of keeping the frame of the smile constant. Lip line was then altered for both the subjects as: both dentitions visible, upper incisors visible, upper incisors and 2 mm gum and 4 mm gum visible. The pictures were rated by different professionals for attractiveness. Descriptive statistics for the raters and multiple factor ANOVA was used to find the most attractive lip line. The total number of raters was 100 with the mean age of 30.3 ± 8 years. The alterations in the smile parameters produced statistically significant difference in the attractiveness of faces, whereas the perception difference was found to be insignificant amongst raters of different professions. Preferred lip line was the one showing only the upper incisors in dolico and mesofacial male and female genders whereas 2 mm gum show was preferred in brachyfacial subjects. The variability in lip line showed significant difference in the perceived attractiveness. Preferred lip lines as the one showing only the upper incisors in dolico and mesofacial male and female genders whereas 2 mm gum show was preferred in brachyfacial subjects.

Shear Strengthening of RC Deep Beam Using Externally Bonded GFRP Fabrics

Science.gov (United States)

Kumari, A.; Patel, S. S.; Nayak, A. N.

2018-06-01

This work presents the experimental investigation of RC deep beams wrapped with externally bonded Glass Fibre Reinforced Polymer (GFRP) fabrics in order to study the Load versus deflection behavior, cracking pattern, failure modes and ultimate shear strength. A total number of five deep beams have been casted, which is designed with conventional steel reinforcement as per IS: 456 (Indian standard plain and reinforced concrete—code for practice, Bureau of Indian Standards, New Delhi, 2000). The spans to depth ratio for all RC deep beams have been kept less than 2 as per the above specification. Out of five RC deep beams, one without retrofitting serves as a reference beam and the rest four have been wrapped with GFRP fabrics in multiple layers and tested with two point loading condition. The first cracking load, ultimate load and the shear contribution of GFRP to the deep beams have been observed. A critical discussion is made with respect to the enhancement of the strength, behaviour and performance of retrofitted deep beams in comparison to the deep beam without GFRP in order to explore the potential use of GFRP for strengthening the RC deep beams. Test results have demonstrated that the deep beams retrofitted with GFRP shows a slower development of the diagonal cracks and improves shear carrying capacity of the RC deep beam. A comparative study of the experimental results with the theoretical ones predicted by various researchers available in the literatures has also been presented. It is observed that the ultimate load of the beams retrofitted with GFRP fabrics increases with increase of number of GFRP layers up to a specific number of layers, i.e. 3 layers, beyond which it decreases.

Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine Canyons.

Directory of Open Access Journals (Sweden)

Magdalena Guardiola

Full Text Available Marine sediments are home to one of the richest species pools on Earth, but logistics and a dearth of taxonomic work-force hinders the knowledge of their biodiversity. We characterized α- and β-diversity of deep-sea assemblages from submarine canyons in the western Mediterranean using an environmental DNA metabarcoding. We used a new primer set targeting a short eukaryotic 18S sequence (ca. 110 bp. We applied a protocol designed to obtain extractions enriched in extracellular DNA from replicated sediment corers. With this strategy we captured information from DNA (local or deposited from the water column that persists adsorbed to inorganic particles and buffered short-term spatial and temporal heterogeneity. We analysed replicated samples from 20 localities including 2 deep-sea canyons, 1 shallower canal, and two open slopes (depth range 100-2,250 m. We identified 1,629 MOTUs, among which the dominant groups were Metazoa (with representatives of 19 phyla, Alveolata, Stramenopiles, and Rhizaria. There was a marked small-scale heterogeneity as shown by differences in replicates within corers and within localities. The spatial variability between canyons was significant, as was the depth component in one of the canyons where it was tested. Likewise, the composition of the first layer (1 cm of sediment was significantly different from deeper layers. We found that qualitative (presence-absence and quantitative (relative number of reads data showed consistent trends of differentiation between samples and geographic areas. The subset of exclusively benthic MOTUs showed similar patterns of β-diversity and community structure as the whole dataset. Separate analyses of the main metazoan phyla (in number of MOTUs showed some differences in distribution attributable to different lifestyles. Our results highlight the differentiation that can be found even between geographically close assemblages, and sets the ground for future monitoring and conservation

EDDINGTON RATIO DISTRIBUTION OF X-RAY-SELECTED BROAD-LINE AGNs AT 1.0 < z < 2.2

International Nuclear Information System (INIS)

Suh, Hyewon; Hasinger, Günther; Steinhardt, Charles; Silverman, John D.; Schramm, Malte

2015-01-01

We investigate the Eddington ratio distribution of X-ray-selected broad-line active galactic nuclei (AGNs) in the redshift range 1.0 < z < 2.2, where the number density of AGNs peaks. Combining the optical and Subaru/Fiber Multi Object Spectrograph near-infrared spectroscopy, we estimate black hole masses for broad-line AGNs in the Chandra Deep Field South (CDF-S), Extended Chandra Deep Field South (E-CDF-S), and the XMM-Newton Lockman Hole (XMM-LH) surveys. AGNs with similar black hole masses show a broad range of AGN bolometric luminosities, which are calculated from X-ray luminosities, indicating that the accretion rate of black holes is widely distributed. We find a substantial fraction of massive black holes accreting significantly below the Eddington limit at z ≲ 2, in contrast to what is generally found for luminous AGNs at high redshift. Our analysis of observational selection biases indicates that the “AGN cosmic downsizing” phenomenon can be simply explained by the strong evolution of the comoving number density at the bright end of the AGN luminosity function, together with the corresponding selection effects. However, one might need to consider a correlation between the AGN luminosity and the accretion rate of black holes, in which luminous AGNs have higher Eddington ratios than low-luminosity AGNs, in order to understand the relatively small fraction of low-luminosity AGNs with high accretion rates in this epoch. Therefore, the observed downsizing trend could be interpreted as massive black holes with low accretion rates, which are relatively fainter than less-massive black holes with efficient accretion

DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian

Directory of Open Access Journals (Sweden)

Chao Li

2017-02-01

Full Text Available Human gait, as a soft biometric, helps to recognize people through their walking. To further improve the recognition performance, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features and introduce Joint Bayesian to model view variance. DeepGait is generated by using a pre-trained “very deep” network “D-Net” (VGG-D without any fine-tuning. For non-view setting, DeepGait outperforms hand-crafted representations (e.g., Gait Energy Image, Frequency-Domain Feature and Gait Flow Image, etc.. Furthermore, for cross-view setting, 256-dimensional DeepGait after PCA significantly outperforms the state-of-the-art methods on the OU-ISR large population (OULP dataset. The OULP dataset, which includes 4007 subjects, makes our result reliable in a statistically reliable way.

Invited talk: Deep Learning Meets Physics

CERN Multimedia

CERN. Geneva

2018-01-01

Deep Learning has emerged as one of the most successful fields of machine learning and artificial intelligence with overwhelming success in industrial speech, text and vision benchmarks. Consequently it evolved into the central field of research for IT giants like Google, facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. The main obstacle to learning deep neural networks is the vanishing gradient problem. The vanishing gradient impedes credit assignment to the first layers of a deep network or to early elements of a sequence, therefore limits model selection. Major advances in Deep Learning can be related to avoiding the vanishing gradient like stacking, ReLUs, residual networks, highway networks, and LSTM. For Deep Learning, we suggested self-normalizing neural networks (SNNs) which automatica...

Spatial Vertical Directionality and Correlation of Low-Frequency Ambient Noise in Deep Ocean Direct-Arrival Zones

Directory of Open Access Journals (Sweden)

Qiulong Yang

2018-01-01

Full Text Available Wind-driven and distant shipping noise sources contribute to the total noise field in the deep ocean direct-arrival zones. Wind-driven and distant shipping noise sources may significantly and simultaneously affect the spatial characteristics of the total noise field to some extent. In this work, a ray approach and parabolic equation solution method were jointly utilized to model the low-frequency ambient noise field in a range-dependent deep ocean environment by considering their calculation accuracy and efficiency in near-field wind-driven and far-field distant shipping noise fields. The reanalysis databases of National Center of Environment Prediction (NCEP and Volunteer Observation System (VOS were used to model the ambient noise source intensity and distribution. Spatial vertical directionality and correlation were analyzed in three scenarios that correspond to three wind speed conditions. The noise field was dominated by distant shipping noise sources when the wind speed was less than 3 m/s, and then the spatial vertical directionality and vertical correlation of the total noise field were nearly consistent with those of distant shipping noise field. The total noise field was completely dominated by near field wind generated noise sources when the wind speed was greater than 12 m/s at 150 Hz, and then the spatial vertical correlation coefficient and directionality pattern of the total noise field was approximately consistent with that of the wind-driven noise field. The spatial characteristics of the total noise field for wind speeds between 3 m/s and 12 m/s were the weighted results of wind-driven and distant shipping noise fields. Furthermore, the spatial characteristics of low-frequency ambient noise field were compared with the classical Cron/Sherman deep water noise field coherence function. Simulation results with the described modeling method showed good agreement with the experimental measurement results based on the vertical line

Spatial Vertical Directionality and Correlation of Low-Frequency Ambient Noise in Deep Ocean Direct-Arrival Zones

Science.gov (United States)

Yang, Qiulong; Yang, Kunde; Cao, Ran; Duan, Shunli

2018-01-01

Wind-driven and distant shipping noise sources contribute to the total noise field in the deep ocean direct-arrival zones. Wind-driven and distant shipping noise sources may significantly and simultaneously affect the spatial characteristics of the total noise field to some extent. In this work, a ray approach and parabolic equation solution method were jointly utilized to model the low-frequency ambient noise field in a range-dependent deep ocean environment by considering their calculation accuracy and efficiency in near-field wind-driven and far-field distant shipping noise fields. The reanalysis databases of National Center of Environment Prediction (NCEP) and Volunteer Observation System (VOS) were used to model the ambient noise source intensity and distribution. Spatial vertical directionality and correlation were analyzed in three scenarios that correspond to three wind speed conditions. The noise field was dominated by distant shipping noise sources when the wind speed was less than 3 m/s, and then the spatial vertical directionality and vertical correlation of the total noise field were nearly consistent with those of distant shipping noise field. The total noise field was completely dominated by near field wind generated noise sources when the wind speed was greater than 12 m/s at 150 Hz, and then the spatial vertical correlation coefficient and directionality pattern of the total noise field was approximately consistent with that of the wind-driven noise field. The spatial characteristics of the total noise field for wind speeds between 3 m/s and 12 m/s were the weighted results of wind-driven and distant shipping noise fields. Furthermore, the spatial characteristics of low-frequency ambient noise field were compared with the classical Cron/Sherman deep water noise field coherence function. Simulation results with the described modeling method showed good agreement with the experimental measurement results based on the vertical line array deployed near

Modeling the effect of deep impurity ionization on GaAs photoconductive switches

Energy Technology Data Exchange (ETDEWEB)

Yee, J.H.; Khanaka, G.H.; Druce, R.L.; Pocha, M.D.

1992-01-01

The ionization coefficient of deep traps in GaAs is determined from a gas breakdown model together with the recent experimental data obtained at LLNL (Lawrence Livermore National Laboratory) and Boeing. Using this coefficient in our nonlinear device transport code, we have investigated theoretically the nonlinear switching phenomena in GaAs devices. The results obtained from our investigations show that if we take into consideration the effect of the field ionization of the deep traps, we can show how the Lock-On'' phenomena could occur in the device.

Modeling the effect of deep impurity ionization on GaAs photoconductive switches

Energy Technology Data Exchange (ETDEWEB)

Yee, J.H.; Khanaka, G.H.; Druce, R.L.; Pocha, M.D.

1992-01-01

The ionization coefficient of deep traps in GaAs is determined from a gas breakdown model together with the recent experimental data obtained at LLNL (Lawrence Livermore National Laboratory) and Boeing. Using this coefficient in our nonlinear device transport code, we have investigated theoretically the nonlinear switching phenomena in GaAs devices. The results obtained from our investigations show that if we take into consideration the effect of the field ionization of the deep traps, we can show how the ``Lock-On`` phenomena could occur in the device.

Deep Recurrent Neural Networks for Human Activity Recognition

Directory of Open Access Journals (Sweden)

Abdulmajid Murad

2017-11-01

Full Text Available Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM and k-nearest neighbors (KNN. Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs and CNNs.

Deep Recurrent Neural Networks for Human Activity Recognition.

Science.gov (United States)

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

Deep Borehole Field Test Requirements and Controlled Assumptions.

Energy Technology Data Exchange (ETDEWEB)

Hardin, Ernest [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-07-01

This document presents design requirements and controlled assumptions intended for use in the engineering development and testing of: 1) prototype packages for radioactive waste disposal in deep boreholes; 2) a waste package surface handling system; and 3) a subsurface system for emplacing and retrieving packages in deep boreholes. Engineering development and testing is being performed as part of the Deep Borehole Field Test (DBFT; SNL 2014a). This document presents parallel sets of requirements for a waste disposal system and for the DBFT, showing the close relationship. In addition to design, it will also inform planning for drilling, construction, and scientific characterization activities for the DBFT. The information presented here follows typical preparations for engineering design. It includes functional and operating requirements for handling and emplacement/retrieval equipment, waste package design and emplacement requirements, borehole construction requirements, sealing requirements, and performance criteria. Assumptions are included where they could impact engineering design. Design solutions are avoided in the requirements discussion. Deep Borehole Field Test Requirements and Controlled Assumptions July 21, 2015 iv ACKNOWLEDGEMENTS This set of requirements and assumptions has benefited greatly from reviews by Gordon Appel, Geoff Freeze, Kris Kuhlman, Bob MacKinnon, Steve Pye, David Sassani, Dave Sevougian, and Jiann Su.

Characterization of failure modes in deep UV and deep green LEDs utilizing advanced semiconductor localization techniques.

Energy Technology Data Exchange (ETDEWEB)

Tangyunyong, Paiboon; Miller, Mary A.; Cole, Edward Isaac, Jr.

2012-03-01

We present the results of a two-year early career LDRD that focused on defect localization in deep green and deep ultraviolet (UV) light-emitting diodes (LEDs). We describe the laser-based techniques (TIVA/LIVA) used to localize the defects and interpret data acquired. We also describe a defect screening method based on a quick electrical measurement to determine whether defects should be present in the LEDs. We then describe the stress conditions that caused the devices to fail and how the TIVA/LIVA techniques were used to monitor the defect signals as the devices degraded and failed. We also describe the correlation between the initial defects and final degraded or failed state of the devices. Finally we show characterization results of the devices in the failed conditions and present preliminary theories as to why the devices failed for both the InGaN (green) and AlGaN (UV) LEDs.

Deep geothermics

International Nuclear Information System (INIS)

Anon.

1995-01-01

The hot-dry-rocks located at 3-4 km of depth correspond to low permeable rocks carrying a large amount of heat. The extraction of this heat usually requires artificial hydraulic fracturing of the rock to increase its permeability before water injection. Hot-dry-rocks geothermics or deep geothermics is not today a commercial channel but only a scientific and technological research field. The Soultz-sous-Forets site (Northern Alsace, France) is characterized by a 6 degrees per meter geothermal gradient and is used as a natural laboratory for deep geothermal and geological studies in the framework of a European research program. Two boreholes have been drilled up to 3600 m of depth in the highly-fractured granite massif beneath the site. The aim is to create a deep heat exchanger using only the natural fracturing for water transfer. A consortium of german, french and italian industrial companies (Pfalzwerke, Badenwerk, EdF and Enel) has been created for a more active participation to the pilot phase. (J.S.). 1 fig., 2 photos

Deep challenges for China's war on water pollution.

Science.gov (United States)

Han, Dongmei; Currell, Matthew J; Cao, Guoliang

2016-11-01

China's Central government has released an ambitious plan to tackle the nation's water pollution crisis. However, this is inhibited by a lack of data, particularly for groundwater. We compiled and analyzed water quality classification data from publicly available government sources, further revealing the scale and extent of the crisis. We also compiled nitrate data in shallow and deep groundwater from a range of literature sources, covering 52 of China's groundwater systems; the most comprehensive national-scale assessment yet. Nitrate pollution at levels exceeding the US EPA's maximum contaminant level (10 mg/L NO 3 N) occurs at the 90th percentile in 25 of 36 shallow aquifers and 10 out of 37 deep or karst aquifers. Isotopic compositions of groundwater nitrate (δ 15 N and δ 18 O NO3 values ranging from -14.9‰ to 35.5‰ and -8.1‰ to 51.0‰, respectively) indicate many nitrate sources including soil nitrogen, agricultural fertilizers, untreated wastewater and/or manure, and locally show evidence of de-nitrification. From these data, it is clear that contaminated groundwater is ubiquitous in deep aquifers as well as shallow groundwater (and surface water). Deep aquifers contain water recharged tens of thousands of years before present, long before widespread anthropogenic nitrate contamination. This groundwater has therefore likely been contaminated due to rapid bypass flow along wells or other conduits. Addressing the issue of well condition is urgently needed to stop further pollution of China's deep aquifers, which are some of China's most important drinking water sources. China's new 10-point Water Pollution Plan addresses previous shortcomings, however, control and remediation of deep groundwater pollution will take decades of sustained effort. Copyright © 2016. Published by Elsevier Ltd.

Gas Classification Using Deep Convolutional Neural Networks

Science.gov (United States)

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-01

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

Gas Classification Using Deep Convolutional Neural Networks.

Science.gov (United States)

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-08

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

The Immediate Effects of Deep Pressure on Young People with Autism and Severe Intellectual Difficulties: Demonstrating Individual Differences

Directory of Open Access Journals (Sweden)

Lana Bestbier

2017-01-01

Full Text Available Background. Deep pressure is widely used by occupational therapists for people with autism spectrum disorders. There is limited research evaluating deep pressure. Objective. To evaluate the immediate effects of deep pressure on young people with autism and severe intellectual disabilities. Methods. Mood and behaviour were rated for 13 pupils with ASD and severe ID before and after deep pressure sessions. Results. Sufficient data was available from 8 participants to be analysed using Tau-U, a nonparametric technique that allows for serial dependence in data. Six showed benefits statistically. Five of these showed benefits across all domains, and one showed benefits on three out of five domains. Relevance to Clinical Practice. Deep pressure appears to be of immediate benefit to this population with autism and severe ID, but the heterogeneity of response suggests that careful monitoring of response should be used and deep pressure discontinued when it is no longer of benefit. Limitations. This is an open label evaluation study using rating scales. Recommendations for Future Research. Future studies of the use of deep pressure should use physiological response measures, in addition to blinded raters for aspects of behaviours such as attitude to learning psychological health not captured physiologically.

Deep subcritical levels measurements dependents upon kinetic distortion factors

International Nuclear Information System (INIS)

Pan Shibiao; Li Xiang; Fu Guo'en; Huang Liyuan; Mu Keliang

2013-01-01

The measurement of deep subcritical levels, with the increase of subcriticality, showed that the results impact on the kinetic distortion effect, along with neutron flux strongly deteriorated. Using the diffusion theory, calculations have been carried out to quantify the kinetic distortion correction factors in subcritical systems, and these indicate that epithermal neutron distributions are strongly affected by kinetic distortion. Subcriticality measurements in four different rod-state combination at the zero power device was carried out. The test data analysis shows that, with increasing subcriticality, kinetic distortion effect correction factor gradually increases from 1.052 to 1.065, corresponding reactive correction amount of 0.78β eff ∼ 3.01β eff . Thus, it is necessary to consider the kinetic distortion effect in the deep subcritical reactivity measurements. (authors)

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

Science.gov (United States)

Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

2016-01-22

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists.

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

2013-01-01

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

Deep unsupervised learning on a desktop PC: A primer for cognitive scientists

Directory of Open Access Journals (Sweden)

Alberto eTestolin

2013-05-01

Full Text Available Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programming parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low-cost graphic cards (GPUs without any specific programming effort, thanks to the use of high-level programming routines (available in MATLAB or Python. We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

2013-01-01

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior. PMID:23653617

Spiraling pathways of global deep waters to the surface of the Southern Ocean.

Science.gov (United States)

Tamsitt, Veronica; Drake, Henri F; Morrison, Adele K; Talley, Lynne D; Dufour, Carolina O; Gray, Alison R; Griffies, Stephen M; Mazloff, Matthew R; Sarmiento, Jorge L; Wang, Jinbo; Weijer, Wilbert

2017-08-02

Upwelling of global deep waters to the sea surface in the Southern Ocean closes the global overturning circulation and is fundamentally important for oceanic uptake of carbon and heat, nutrient resupply for sustaining oceanic biological production, and the melt rate of ice shelves. However, the exact pathways and role of topography in Southern Ocean upwelling remain largely unknown. Here we show detailed upwelling pathways in three dimensions, using hydrographic observations and particle tracking in high-resolution models. The analysis reveals that the northern-sourced deep waters enter the Antarctic Circumpolar Current via southward flow along the boundaries of the three ocean basins, before spiraling southeastward and upward through the Antarctic Circumpolar Current. Upwelling is greatly enhanced at five major topographic features, associated with vigorous mesoscale eddy activity. Deep water reaches the upper ocean predominantly south of the Antarctic Circumpolar Current, with a spatially nonuniform distribution. The timescale for half of the deep water to upwell from 30° S to the mixed layer is ~60-90 years.Deep waters of the Atlantic, Pacific and Indian Oceans upwell in the Southern Oceanbut the exact pathways are not fully characterized. Here the authors present a three dimensional view showing a spiralling southward path, with enhanced upwelling by eddy-transport at topographic hotspots.

Hydrochemistry, origin and residence time of deep groundwater in the Yuseong area

International Nuclear Information System (INIS)

Koh, Yong Kwon; Kim, Geon Young; Bae, Dae Seok; Park, Kyung Woo

2005-01-01

As a part of the radioactive waste disposal research program in Korea, the geological, hydrogeological and hydrogeochemical investigations have been carried out in the Yuseong area (KAERI). The temperature or groundwater is measured up to 24 .deg. C and thermal gradient is obtained, to 0.26 .deg. C/100m. pH of groundwater at upper section shows about 7 and the pH of groundwater of 200m below surface reaches almost constant value as 9.9∼10.3. The redox potential of groundwater varied with depth and more negative values were recognized in deep groundwater. The redox potential of deep groundwater, main factor of U solubility, was measured up to -150 mV. These high pH and reduced conditions indicates that the maximum U concentration in groundwater would be limited by the equilibrium solubility of U minerals. The chemistry of shallow groundwater shows Ca-HCO 3 or Ca-Na-HCO 3 type, whereas the deep groundwater belongs to typical Na-HCO 3 type. The chemistry of groundwater below 250m from the surface is constant with depth, indicating that the extent of water-rock reaction is almost unique, which is controlled by the residence time of groundwater. The carbon isotope data (δ 13 C) of groundwater show the contribution of carbon from either that microbial oxidation of organic matter or carbon dioxide from plant respiration. The measurement and interpretation of C-14 indicate that the residence time of borehole deep groundwater ranges from about 2,000 to 6,000 yr BP. The high δ 34 S so4 value of groundwater indicate that the sulfate reduction might be occurred in the deep environment

Temperature impacts on deep-sea biodiversity.

Science.gov (United States)

Yasuhara, Moriaki; Danovaro, Roberto

2016-05-01

Temperature is considered to be a fundamental factor controlling biodiversity in marine ecosystems, but precisely what role temperature plays in modulating diversity is still not clear. The deep ocean, lacking light and in situ photosynthetic primary production, is an ideal model system to test the effects of temperature changes on biodiversity. Here we synthesize current knowledge on temperature-diversity relationships in the deep sea. Our results from both present and past deep-sea assemblages suggest that, when a wide range of deep-sea bottom-water temperatures is considered, a unimodal relationship exists between temperature and diversity (that may be right skewed). It is possible that temperature is important only when at relatively high and low levels but does not play a major role in the intermediate temperature range. Possible mechanisms explaining the temperature-biodiversity relationship include the physiological-tolerance hypothesis, the metabolic hypothesis, island biogeography theory, or some combination of these. The possible unimodal relationship discussed here may allow us to identify tipping points at which on-going global change and deep-water warming may increase or decrease deep-sea biodiversity. Predicted changes in deep-sea temperatures due to human-induced climate change may have more adverse consequences than expected considering the sensitivity of deep-sea ecosystems to temperature changes. © 2014 Cambridge Philosophical Society.

Human Brain Activity Patterns beyond the Isoelectric Line of Extreme Deep Coma

Science.gov (United States)

Kroeger, Daniel; Florea, Bogdan; Amzica, Florin

2013-01-01

The electroencephalogram (EEG) reflects brain electrical activity. A flat (isoelectric) EEG, which is usually recorded during very deep coma, is considered to be a turning point between a living brain and a deceased brain. Therefore the isoelectric EEG constitutes, together with evidence of irreversible structural brain damage, one of the criteria for the assessment of brain death. In this study we use EEG recordings for humans on the one hand, and on the other hand double simultaneous intracellular recordings in the cortex and hippocampus, combined with EEG, in cats. They serve to demonstrate that a novel brain phenomenon is observable in both humans and animals during coma that is deeper than the one reflected by the isoelectric EEG, and that this state is characterized by brain activity generated within the hippocampal formation. This new state was induced either by medication applied to postanoxic coma (in human) or by application of high doses of anesthesia (isoflurane in animals) leading to an EEG activity of quasi-rhythmic sharp waves which henceforth we propose to call ν-complexes (Nu-complexes). Using simultaneous intracellular recordings in vivo in the cortex and hippocampus (especially in the CA3 region) we demonstrate that ν-complexes arise in the hippocampus and are subsequently transmitted to the cortex. The genesis of a hippocampal ν-complex depends upon another hippocampal activity, known as ripple activity, which is not overtly detectable at the cortical level. Based on our observations, we propose a scenario of how self-oscillations in hippocampal neurons can lead to a whole brain phenomenon during coma. PMID:24058669

Unexpected Positive Buoyancy in Deep Sea Sharks, Hexanchus griseus, and a Echinorhinus cookei.

Directory of Open Access Journals (Sweden)

Itsumi Nakamura

Full Text Available We do not expect non air-breathing aquatic animals to exhibit positive buoyancy. Sharks, for example, rely on oil-filled livers instead of gas-filled swim bladders to increase their buoyancy, but are nonetheless ubiquitously regarded as either negatively or neutrally buoyant. Deep-sea sharks have particularly large, oil-filled livers, and are believed to be neutrally buoyant in their natural habitat, but this has never been confirmed. To empirically determine the buoyancy status of two species of deep-sea sharks (bluntnose sixgill sharks, Hexanchus griseus, and a prickly shark, Echinorhinus cookei in their natural habitat, we used accelerometer-magnetometer data loggers to measure their swimming performance. Both species of deep-sea sharks showed similar diel vertical migrations: they swam at depths of 200-300 m at night and deeper than 500 m during the day. Ambient water temperature was around 15°C at 200-300 m but below 7°C at depths greater than 500 m. During vertical movements, all deep-sea sharks showed higher swimming efforts during descent than ascent to maintain a given swimming speed, and were able to glide uphill for extended periods (several minutes, indicating that these deep-sea sharks are in fact positively buoyant in their natural habitats. This positive buoyancy may adaptive for stealthy hunting (i.e. upward gliding to surprise prey from underneath or may facilitate evening upward migrations when muscle temperatures are coolest, and swimming most sluggish, after spending the day in deep, cold water. Positive buoyancy could potentially be widespread in fish conducting daily vertical migration in deep-sea habitats.

Experimental Study on the Axis Line Deflection of Ti6A14V Titanium Alloy in Gun-Drilling Process

Science.gov (United States)

Li, Liang; Xue, Hu; Wu, Peng

2018-01-01

Titanium alloy is widely used in aerospace industry, but it is also a typical difficult-to-cut material. During Deep hole drilling of the shaft parts of a certain large aircraft, there are problems of bad surface roughness, chip control and axis deviation, so experiments on gun-drilling of Ti6A14V titanium alloy were carried out to measure the axis line deflection, diameter error and surface integrity, and the reasons of these errors were analyzed. Then, the optimized process parameter was obtained during gun-drilling of Ti6A14V titanium alloy with deep hole diameter of 17mm. Finally, we finished the deep hole drilling of 860mm while the comprehensive error is smaller than 0.2mm and the surface roughness is less than 1.6μm.

Using Cooperative Structures to Promote Deep Learning

Science.gov (United States)

Millis, Barbara J.

2014-01-01

The author explores concrete ways to help students learn more and have fun doing it while they support each other's learning. The article specifically shows the relationships between cooperative learning and deep learning. Readers will become familiar with the tenets of cooperative learning and its power to enhance learning--even more so when…

Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

Directory of Open Access Journals (Sweden)

Shaonian Huang

2018-01-01

Full Text Available Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies. In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes. Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements. Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns. Then a multimodal fusion scheme is utilized to learn the deep representation of crowd patterns. Based on the learned deep representation, a one-class support vector machine model is used to detect anomaly events. The proposed method is evaluated using two available public datasets and compared with state-of-the-art methods. The experimental results show its competitive performance for anomaly event detection in video surveillance.

A ground-up construction of deep learning

CERN Multimedia

CERN. Geneva

2015-01-01

I propose to give a ground up construction of deep learning as it is in it's modern state. Starting from it's beginnings in the 90's, I plan on showing the relevant (for physics) differences in optimization, construction, activation functions, initialization, and other tricks that have been accrued over the last 20 years. In addition, I plan on showing why deeper, wider basic feedforward architectures can be used. Coupling this with MaxOut layers, modern GPUs, and including both l1 and l2 forms of regularization, we have the current "state of the art" in basic feedforward networks. I plan on discussing pre-training using deep autoencoders and RBMs, and explaining why this has fallen out of favor when you have lots of labeled data. While discussing each of these points, I propose to explain why these particular characteristics are valuable for HEP. Finally, the last topic on basic feedforward networks -- interpretation. I plan on discussing latent representations of important variables (i.e., mass, pT) that ar...

Prognostic value of gonioscopy after deep sclerectomy.

Science.gov (United States)

Moreno-Montañés, J; Rebolleda, G; Muñoz-Negrete, F J

2007-01-01

To ascertain gonioscopic characteristics and identify prognostic indicators related to intraocular pressure (IOP) after deep sclerectomy (DS). A transversal, prospective, and nonselected study was performed in 106 eyes (95 patients) after DS. Three surgeons performed all the surgeries and the gonioscopic examination, using the same protocol including 13 gonioscopic data. These data were evaluated for an association with postoperative IOP and time after surgery. A subscleral space was found in 91 eyes (85.8%), with visualization of the line of scleral flap in 48 eyes (45.3%). The trabeculo-Descemet membrane (TDM) was transparent in 46 eyes (43.4%), opaque in 4 cases, and pigmented in 18 eyes. This TDM was broken using Nd:YAG laser goniopuncture in 38 eyes(35.8%). Thin vessels around TDM were found in 58 eyes (54.7%), and blood remained in 25 eyes (23.5%). Gonioscopic variables significantly positively related with postoperative IOP were as follows: presence of subscleral space, scleral flap line view, and a Schwalbe line depressed. A narrow anterior chamber angle and iris synechia in TDM had a statistically significant negative effect on the postoperative IOP control. Similarly, eyes requiring Nd:YAG goniopuncture had a worse IOP control. The frequency of eyes with visible subscleral space and transparent TDM decreases with time after surgery (p=0.001). A visible subscleral space was a gonioscopic sign positively related to IOP control after surgery, although it decreased with follow-up. Eyes with goniopuncture, postoperative narrow angle, and iris synechia had worse postoperative IOP control. Although new vessels in TDM were a common finding after DS, the authors did not find any association with postoperative IOP.

The influence of deep cryogenic treatment on the properties of high-vanadium alloy steel

Energy Technology Data Exchange (ETDEWEB)

Li, Haizhi [Key Laboratory of Electromagnetic Processing of Materials (Ministry of Education), Northeastern University, Shenyang 110819 (China); Tong, Weiping, E-mail: wptong@mail.neu.edu.cn [Key Laboratory of Electromagnetic Processing of Materials (Ministry of Education), Northeastern University, Shenyang 110819 (China); Cui, Junjun [State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819 (China); Zhang, Hui [Key Laboratory of Electromagnetic Processing of Materials (Ministry of Education), Northeastern University, Shenyang 110819 (China); Chen, Liqing [State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819 (China); Zuo, Liang [Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials and Metallurgy, Northeastern University, Shenyang 110819 (China)

2016-04-26

Deep cryogenic treatment can improve the mechanical properties of many metallic materials, but there are few reports of the effect of deep cryogenic treatment on high-vanadium alloy steel. The main objective of this work is to investigate the effect of deep cryogenic treatment on the microstructure, hardness, impact toughness and abrasive wear resistance of high-vanadium alloy steel. The results show that large amounts of small secondary carbide precipitation after deep cryogenic treatment and microcracks were detected and occurred preferentially at carbide/matrix interfaces; except for the hardness, the mechanical properties increased compared to those of the conventional treatment sample. By increasing the deep cryogenic processing time and cycle number, impact toughness and abrasive wear resistance can be further improved, the carbide contents continuously increase, and the hardness decreases.

New optimized drill pipe size for deep-water, extended reach and ultra-deep drilling

Energy Technology Data Exchange (ETDEWEB)

Jellison, Michael J.; Delgado, Ivanni [Grant Prideco, Inc., Hoston, TX (United States); Falcao, Jose Luiz; Sato, Ademar Takashi [PETROBRAS, Rio de Janeiro, RJ (Brazil); Moura, Carlos Amsler [Comercial Perfuradora Delba Baiana Ltda., Rio de Janeiro, RJ (Brazil)

2004-07-01

A new drill pipe size, 5-7/8 in. OD, represents enabling technology for Extended Reach Drilling (ERD), deep water and other deep well applications. Most world-class ERD and deep water wells have traditionally been drilled with 5-1/2 in. drill pipe or a combination of 6-5/8 in. and 5-1/2 in. drill pipe. The hydraulic performance of 5-1/2 in. drill pipe can be a major limitation in substantial ERD and deep water wells resulting in poor cuttings removal, slower penetration rates, diminished control over well trajectory and more tendency for drill pipe sticking. The 5-7/8 in. drill pipe provides a significant improvement in hydraulic efficiency compared to 5-1/2 in. drill pipe and does not suffer from the disadvantages associated with use of 6-5/8 in. drill pipe. It represents a drill pipe assembly that is optimized dimensionally and on a performance basis for casing and bit programs that are commonly used for ERD, deep water and ultra-deep wells. The paper discusses the engineering philosophy behind 5-7/8 in. drill pipe, the design challenges associated with development of the product and reviews the features and capabilities of the second-generation double-shoulder connection. The paper provides drilling case history information on significant projects where the pipe has been used and details results achieved with the pipe. (author)

Deep Reinforcement Learning: An Overview

OpenAIRE

Li, Yuxi

2017-01-01

We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsuperv...

The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line

DEFF Research Database (Denmark)

Suzuki, Harukazu; Forrest, Alistair R R; van Nimwegen, Erik

2009-01-01

, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks......Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites...... involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process....

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

Science.gov (United States)

Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

2018-06-01

MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

Impact disruption and recovery of the deep subsurface biosphere

Science.gov (United States)

Cockell, Charles S.; Voytek, Mary A.; Gronstal, Aaron L.; Finster, Kai; Kirshtein, Julie D.; Howard, Kieren; Reitner, Joachim; Gohn, Gregory S.; Sanford, Ward E.; Horton, J. Wright; Kallmeyer, Jens; Kelly, Laura; Powars, David S.

2012-01-01

Although a large fraction of the world's biomass resides in the subsurface, there has been no study of the effects of catastrophic disturbance on the deep biosphere and the rate of its subsequent recovery. We carried out an investigation of the microbiology of a 1.76 km drill core obtained from the ~35 million-year-old Chesapeake Bay impact structure, USA, with robust contamination control. Microbial enumerations displayed a logarithmic downward decline, but the different gradient, when compared to previously studied sites, and the scatter of the data are consistent with a microbiota influenced by the geological disturbances caused by the impact. Microbial abundance is low in buried crater-fill, ocean-resurge, and avalanche deposits despite the presence of redox couples for growth. Coupled with the low hydraulic conductivity, the data suggest the microbial community has not yet recovered from the impact ~35 million years ago. Microbial enumerations, molecular analysis of microbial enrichment cultures, and geochemical analysis showed recolonization of a deep region of impact-fractured rock that was heated to above the upper temperature limit for life at the time of impact. These results show how, by fracturing subsurface rocks, impacts can extend the depth of the biosphere. This phenomenon would have provided deep refugia for life on the more heavily bombarded early Earth, and it shows that the deeply fractured regions of impact craters are promising targets to study the past and present habitability of Mars.

Radial Profile of the 3.5 kev Line Out to R200 in the Perseus Cluster

Science.gov (United States)

Franse, Jeroen; Bulbul, Esra; Foster, Adam; Boyarsky, Alexey; Markevitch, Maxim; Bautz, Mark; Lakubovskyi, Dmytro; Loewenstein, Michael; McDonald, Michael; Miller, Eric;

Equivalence between deep energy-dependent and shallow angular momentum dependent potentials

International Nuclear Information System (INIS)

Fiedeldey, H.; Sofianos, S.A.; Papastylianos, A.; Amos, K.A.; Allen, L.J.

1989-01-01

Recently Baye showed that supersymmetry can be applied to determine a shallow l-dependent potential phase equivalent to a deep potential, assumed to be energy-independent and have Panli forbidden states (PFS), for α-α scattering. The PFS are eliminated by this procedure. Such deep potentials are generated as equivalent local potentials (ELP) to the Resonating Group Model (RGM) and are generally energy-dependent. To eliminate this E-dependence as required for the application of Baye's method, l-dependent, but E-independent, deep local potentials were generated by the exact inversion method of Marchenko. Subsequently, the supersymmetric method was used to eliminate the PFS, ensuring that the generalized Levinson theorem is satisfied. As an example, the method was applied to the simple model of two dineutrons scattering in the RGM, where the deep ELP of Horiuchi has a substantial energy-dependence and one PFS only for l=O. 16 refs., 5 figs

Docker Containers for Deep Learning Experiments

OpenAIRE

Gerke, Paul K.

2017-01-01

Deep learning is a powerful tool to solve problems in the area of image analysis. The dominant compute platform for deep learning is Nvidia’s proprietary CUDA, which can only be used together with Nvidia graphics cards. The nivida-docker project allows exposing Nvidia graphics cards to docker containers and thus makes it possible to run deep learning experiments in docker containers.In our department, we use deep learning to solve problems in the area of medical image analysis and use docker ...

Nuclear waste and a deep geological disposal facility

International Nuclear Information System (INIS)

Vokal, A.; Laciok, A.; Vasa, I.

2005-01-01

The paper presents a systematic analysis of the individual areas of research into nuclear waste and deep geological disposal with emphasis on the contribution of Nuclear Research Institute Rez plc to such efforts within international projects, specifically the EURATOM 6th Framework Programme. Research in the area of new advanced fuel cycles with focus on waste minimisation is based on EU's REDIMPACT project. The individual fuel cycles, which are currently studied within the EU, are briefly described. Special attention is paid to fast breeders and accelerator-driven reactor concepts associated with new spent fuel reprocessing technologies. Results obtained so far show that none even of the most advanced fuel cycles, currently under consideration, would eliminate the necessity to have a deep geological repository for a safe storage of residual radioactive waste. As regards deep geological repository barriers, the fact is highlighted that the safety of a repository is assured by complementary engineered and natural barriers. In order to demonstrate the safety of a repository, a deep insight must be gained into any and all of the individual processes that occur inside the repository and thus may affect radioactivity releases beyond the repository boundaries. The final section of the paper describes methods of radioactive waste conditioning for its disposal in a repository. Research into waste matrices used for radionuclide immobilisation is also highlighted. (author)

Study of the earth's deep interior and crystallography. X-ray and neutron diffraction experiments under high pressures

International Nuclear Information System (INIS)

Yagi, Takehiko

2014-01-01

History of the study of the Earth's deep interior was reviewed. In order to understand Earth's deep interior from the view point of materials science, X-ray diffraction under high pressure and high temperature played very important role. Use of synchrotron radiation dramatically advanced this experimental technique and it is now possible to make precise X-ray study under the P-T conditions corresponding even to the center of the Earth. In order to clarify the behavior of light elements such as hydrogen, however, studies using neutron diffraction are also required. A new neutron beam line dedicated for high-pressure science is constructed at J-PARC and is now ready for use. (author)

Accelerating Deep Learning with Shrinkage and Recall

OpenAIRE

Zheng, Shuai; Vishnu, Abhinav; Ding, Chris

2016-01-01

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Lea...

Hydro-mechanical deep drawing of rolled magnesium sheets

Energy Technology Data Exchange (ETDEWEB)

Bach, F.W.; Rodman, M.; Rossberg, A. [Hannover Univ., Garbsen (Germany). Inst. of Materials Science; Behrens, B.A.; Vogt, O. [Hannover Univ., Garbsen (DE). Inst. of Metal Forming and Metal Forming Machine Tools (IFUM)

2005-12-01

Magnesium sheets offer high specific properties which make them very attractive in modern light weight constructions. The main obstacles for a wider usage are their high production costs, the poor corrosion properties and the limited ductility. Until today, forming processes have to be conducted at temperatures well above T=220 C. In the first place, this is a cost factor. Moreover, technical aspects, such as grain growth or the limited use of lubrication speak against high temperatures. The first aim of the presented research work is to increase the ductility at lower temperatures by alloy modification and by an adapted rolling technology. The key factor to reach isotropic mechanical properties and increased limit drawing ratios in deep drawing tools, is to achieve fine, homogeneous microstructures. This can be done by cross rolling at moderate temperatures. The heat treatment has to be adapted accordingly. In a second stage, hydro-mechanical deep drawing experiments were carried out at elevated temperature. The results show that the forming behaviour of the tested Mg-alloys is considerably improved compared to conventional deep drawing. (orig.)

Virtual synchrotron experiments for deep Earth studies

Science.gov (United States)

Jackson, J. M.; Alp, E. E.; Zhao, J.; Alatas, A.; Sturhahn, W.

2011-12-01

National facilities offer one-of-a-kind opportunities to apply state-of-the-art experimental techniques to the pressing scientific problems of today. Yet, few students are able to experience research projects at national facilities due to limited accessibility caused in part by limited involvement in the local academic institution, constrained working areas at the experimental stations, and/or travel costs. We present a virtual and remote beam-line for deep Earth mineral physics studies using nuclear resonant and inelastic x-ray scattering methods at Sector 3 of the Advanced Photon Source at Argonne National Laboratory. Off-site students have the capability of controlling their measurements via secure internet connections and webcams. Students can access a 'view only mode' for ease of interaction and safety-control. More experienced users have exclusive control of the experiment and can remotely change variables within the experimental setup.

Consolidated Deep Actor Critic Networks (DRAFT)

NARCIS (Netherlands)

Van der Laan, T.A.

2015-01-01

The works [Volodymyr et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.] and [Volodymyr et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.] have demonstrated the power of combining deep neural networks with

Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks

OpenAIRE

Khalifa, Nour Eldeen M.; Taha, Mohamed Hamed N.; Hassanien, Aboul Ella; Selim, I. M.

2017-01-01

In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep galaxies architecture consists of 8 layers, one main convolutional layer for features extraction with 96 filters, followed by two principles fully connected layers for classification. It is trained over 1356 images and achieved 97.272% in testing accuracy. A c...

Deep electroproduction of exotic hybrid mesons

International Nuclear Information System (INIS)

Anikin, I.V.; Pire, B.; Szymanowski, L.; Teryaev, O.V.; Wallon, S.

2004-01-01

We evaluate the leading order amplitude for the deep exclusive electroproduction of an exotic hybrid meson in the Bjorken regime. We show that, contrarily to naive expectation, this amplitude factorizes at the twist 2 level and thus scales like usual meson electroproduction when the virtual photon and the hybrid meson are longitudinally polarized. Exotic hybrid mesons may thus be studied in electroproduction experiments at JLAB, HERA (HERMES) or CERN (Compass)

Iron oxide reduction in methane-rich deep Baltic Sea sediments

DEFF Research Database (Denmark)

Egger, Matthias; Hagens, Mathilde; Sapart, Celia J.

2017-01-01

/L transition. Our results reveal a complex interplay between production, oxidation and transport of methane showing that besides organoclastic Fe reduction, oxidation of downward migrating methane with Fe oxides may also explain the elevated concentrations of dissolved ferrous Fe in deep Baltic Sea sediments...... profiles and numerical modeling, we propose that a potential coupling between Fe oxide reduction and methane oxidation likely affects deep Fe cycling and related biogeochemical processes, such as burial of phosphorus, in systems subject to changes in organic matter loading or bottom water salinity....

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

Science.gov (United States)

Wehmeyer, Christoph; Noé, Frank

2018-06-01

Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes—beyond the capabilities of linear dimension reduction techniques.

Detecting voids in a 0. 6m coal seam, 7m deep, using seismic reflection

Energy Technology Data Exchange (ETDEWEB)

Miller, R.D.; Steeples, D.W. (University of Kansas, Lawrence, KS (USA). Kansas Geological Survey)

1991-07-01

Surface collapse over abandoned subsurface coal mines is a problem in many parts of the world. High-resolution P-wave reflection seismology was successfully used to evaluate the risk of an active sinkhole to a main north-south railroad line in an undermined area of southeastern Kansas, USA. Water-filled cavities responsible for sinkholes in this area are in a 0.6 m thick coal seam, 7 m deep. Dominant reflection frequencies in excess of 200 Hz enabled reflections from the coal seam to be discerned from the direct wave, refractions, air wave, and ground roll on unprocessed field files. Repetitive void sequences within competent coal on three seismic profiles are consistent with the 'room and pillar' mining technique practiced in this area near the turn of the century. The seismic survey showed that the apparent active sinkhole was not the result of reactivated subsidence but probably the results of erosion. 14 refs., 6 figs.

Quantitative trait locus mapping of deep rooting by linkage and association analysis in rice.

Science.gov (United States)

Lou, Qiaojun; Chen, Liang; Mei, Hanwei; Wei, Haibin; Feng, Fangjun; Wang, Pei; Xia, Hui; Li, Tiemei; Luo, Lijun

2015-08-01

Deep rooting is a very important trait for plants' drought avoidance, and it is usually represented by the ratio of deep rooting (RDR). Three sets of rice populations were used to determine the genetic base for RDR. A linkage mapping population with 180 recombinant inbred lines and an association mapping population containing 237 rice varieties were used to identify genes linked to RDR. Six quantitative trait loci (QTLs) of RDR were identified as being located on chromosomes 1, 2, 4, 7, and 10. Using 1 019 883 single-nucleotide polymorphisms (SNPs), a genome-wide association study of the RDR was performed. Forty-eight significant SNPs of the RDR were identified and formed a clear peak on the short arm of chromosome 1 in a Manhattan plot. Compared with the shallow-rooting group and the whole collection, the deep-rooting group had selective sweep regions on chromosomes 1 and 2, especially in the major QTL region on chromosome 2. Seven of the nine candidate SNPs identified by association mapping were verified in two RDR extreme groups. The findings from this study will be beneficial to rice drought-resistance research and breeding. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Deep Feature Consistent Variational Autoencoder

OpenAIRE

Hou, Xianxu; Shen, Linlin; Sun, Ke; Qiu, Guoping

2016-01-01

We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural net...

Isometachromin, a new cytotoxic sesquiterpenoid from a deep water sponge of the family Spongiidae.

Science.gov (United States)

McConnell, O J; Longley, R; Gunasekera, M

1992-09-15

Isometachromin (1), a new sesquiterpene-quinone that is related structurally to metachromin C (2), and the known compounds ilimaquinone (3) and 5-epi-ilimaquinone (4), were isolated from a deep water sponge in the family Spongiidae; the structure of isometachromin was elucidated by spectral methods. Isometachromin exhibits in vitro cytotoxicity against the human lung cancer cell line A549 (IC50 = 2.6 micrograms/ml), but not against P388 murine leukemia (IC 50 > or equal to 10 micrograms/ml) and also exhibits antimicrobial activity.

Blind source deconvolution for deep Earth seismology

Science.gov (United States)

Stefan, W.; Renaut, R.; Garnero, E. J.; Lay, T.

2007-12-01

We present an approach to automatically estimate an empirical source characterization of deep earthquakes recorded teleseismically and subsequently remove the source from the recordings by applying regularized deconvolution. A principle goal in this work is to effectively deblur the seismograms, resulting in more impulsive and narrower pulses, permitting better constraints in high resolution waveform analyses. Our method consists of two stages: (1) we first estimate the empirical source by automatically registering traces to their 1st principal component with a weighting scheme based on their deviation from this shape, we then use this shape as an estimation of the earthquake source. (2) We compare different deconvolution techniques to remove the source characteristic from the trace. In particular Total Variation (TV) regularized deconvolution is used which utilizes the fact that most natural signals have an underlying spareness in an appropriate basis, in this case, impulsive onsets of seismic arrivals. We show several examples of deep focus Fiji-Tonga region earthquakes for the phases S and ScS, comparing source responses for the separate phases. TV deconvolution is compared to the water level deconvolution, Tikenov deconvolution, and L1 norm deconvolution, for both data and synthetics. This approach significantly improves our ability to study subtle waveform features that are commonly masked by either noise or the earthquake source. Eliminating source complexities improves our ability to resolve deep mantle triplications, waveform complexities associated with possible double crossings of the post-perovskite phase transition, as well as increasing stability in waveform analyses used for deep mantle anisotropy measurements.

Gene expression inference with deep learning.

Science.gov (United States)

Chen, Yifei; Li, Yi; Narayan, Rajiv; Subramanian, Aravind; Xie, Xiaohui

2016-06-15

Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. D-GEX is available at https://github.com/uci-cbcl/D-GEX CONTACT: xhx@ics.uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Capability to Recover Plutonium-238 in H-Canyon/HB-Line - 13248

Energy Technology Data Exchange (ETDEWEB)

Fuller, Kenneth S. Jr.; Smith, Robert H. Jr.; Goergen, Charles R. [Savannah River Nuclear Solutions, LLC, Savannah River Site, Aiken, SC 29802 (United States)

2013-07-01

Plutonium-238 is used in Radioisotope Thermoelectric Generators (RTGs) to generate electrical power and in Radioisotope Heater Units (RHUs) to produce heat for electronics and environmental control for deep space missions. The domestic supply of Pu-238 consists of scrap material from previous mission production or material purchased from Russia. Currently, the United States has no significant production scale operational capability to produce and separate new Pu-238 from irradiated neptunium-237 targets. The Department of Energy - Nuclear Energy is currently evaluating and developing plans to reconstitute the United States capability to produce Pu-238 from irradiated Np-237 targets. The Savannah River Site had previously produced and/or processed all the Pu-238 utilized in Radioisotope Thermoelectric Generators (RTGs) for deep space missions up to and including the majority of the plutonium for the Cassini Mission. The previous full production cycle capabilities included: Np- 237 target fabrication, target irradiation, target dissolution and Np-237 and Pu-238 separation and purification, conversion of Np-237 and Pu-238 to oxide, scrap recovery, and Pu-238 encapsulation. The capability and equipment still exist and could be revitalized or put back into service to recover and purify Pu-238/Np-237 or broken General Purpose Heat Source (GPHS) pellets utilizing existing process equipment in HB-Line Scrap Recovery, and H-Canyon Frame Waste Recovery processes. The conversion of Np-237 and Pu-238 to oxide can be performed in the existing HB-Line Phase-2 and Phase- 3 Processes. Dissolution of irradiated Np-237 target material, and separation and purification of Np-237 and Pu-238 product streams would be possible at production rates of ∼2 kg/month of Pu-238 if the existing H-Canyon Frames Process spare equipment were re-installed. Previously, the primary H-Canyon Frames equipment was removed to be replaced: however, the replacement project was stopped. The spare equipment

Deep sequencing shows that oocytes are not prone to accumulate mtDNA heteroplasmic mutations during ovarian ageing.

Science.gov (United States)

Boucret, L; Bris, C; Seegers, V; Goudenège, D; Desquiret-Dumas, V; Domin-Bernhard, M; Ferré-L'Hotellier, V; Bouet, P E; Descamps, P; Reynier, P; Procaccio, V; May-Panloup, P

2017-10-01

Does ovarian ageing increase the number of heteroplasmic mitochondrial DNA (mtDNA) point mutations in oocytes? Our results suggest that oocytes are not subject to the accumulation of mtDNA point mutations during ovarian ageing. Ageing is associated with the alteration of mtDNA integrity in various tissues. Primary oocytes, present in the ovary since embryonic life, may accumulate mtDNA mutations during the process of ovarian ageing. This was an observational study of 53 immature oocyte-cumulus complexes retrieved from 35 women undergoing IVF at the University Hospital of Angers, France, from March 2013 to March 2014. The women were classified in two groups, one including 19 women showing signs of ovarian ageing objectified by a diminished ovarian reserve (DOR), and the other, including 16 women with a normal ovarian reserve (NOR), which served as a control group. mtDNA was extracted from isolated oocytes, and from their corresponding cumulus cells (CCs) considered as a somatic cell compartment. The average mtDNA content of each sample was assessed by using a quantitative real-time PCR technique. Deep sequencing was performed using the Ion Torrent Proton for Next-Generation Sequencing. Signal processing and base calling were done by the embedded pre-processing pipeline and the variants were analyzed using an in-house workflow. The distribution of the different variants between DOR and NOR patients, on one hand, and oocyte and CCs, on the other, was analyzed with the generalized mixed linear model to take into account the cluster of cells belonging to a given mother. There were no significant differences between the numbers of mtDNA variants between the DOR and the NOR patients, either in the oocytes (P = 0.867) or in the surrounding CCs (P = 0.154). There were also no differences in terms of variants with potential functional consequences. De-novo mtDNA variants were found in 28% of the oocytes and in 66% of the CCs with the mean number of variants being

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

Science.gov (United States)

Young, Jonathan D; Cai, Chunhui; Lu, Xinghua

2017-10-03

further investigate the disease mechanisms underlying each of these clusters. In summary, we show that a deep learning model can be trained to represent biologically and clinically meaningful abstractions of cancer gene expression data. Understanding what additional relationships these hidden layer abstractions have with the cancer cellular signaling system could have a significant impact on the understanding and treatment of cancer.

The flinders sensitive line rats, a genetic model of depression, show abnormal serotonin receptor mRNA expression in the brain that is reversed by 17beta-estradiol.

Science.gov (United States)

Osterlund, M K; Overstreet, D H; Hurd, Y L

1999-12-10

The possible link between estrogen and serotonin (5-HT) in depression was investigated using a genetic animal model of depression, the Flinders Sensitive Line (FSL) rats, in comparison to control Flinders Resistant Line rats. The mRNA levels of the estrogen receptor (ER) alpha and beta subtypes and the 5-HT(1A) and 5-HT(2A) receptors were analyzed in several limbic-related areas of ovariectomized FSL and FRL rats treated with 17beta-estradiol (0.15 microg/g) or vehicle. The FSL animals were shown to express significantly lower levels of the 5-HT(2A) receptor transcripts in the perirhinal cortex, piriform cortex, and medial anterodorsal amygdala and higher levels in the CA 2-3 region of the hippocampus. The only significant difference between the rat lines in ER mRNA expression was found in the medial posterodorsal amygdala, where the FSL rats showed lower ERalpha expression levels. Overall, estradiol treatment increased 5-HT(2A) and decreased 5-HT(1A) receptor mRNA levels in several of the examined regions of both lines. Thus, in many areas, estradiol was found to regulate the 5-HT receptor mRNA expression in the opposite direction to the alterations found in the FSL rats. These findings further support the implication of 5-HT receptors, in particular the 5-HT(2A) subtype, in the etiology of affective disorders. Moreover, the ability of estradiol to regulate the expression of the 5-HT(1A) and 5-HT(2A) receptor genes might account for the reported influence of gonadal hormones in mood and depression.

[Spatiotemporal succession of algae functional groups and the influence of environment change in a deep-water reservoir].

Science.gov (United States)

Lu, Jin-Suo; Hu, Ya-Pan

2013-07-01

Algae functional group has become an important theory and method of algae research in recent years. In order to explore the spatiotemporal succession of algae functional groups and the influence of environment change, water samples were collected in August, 2011 from a deep-water reservoir in Northwest China. The research combined the methods of on-line monitoring and laboratory analysis. The results showed that there were 10 functional groups of algae in the reservoir. They were designated as B, D, P, X1, X3, F, G, J, L(M) and MP. Wherein, the groups B, P, F, X1, MP, D and J were comparatively common functional groups, and the groups X3, G and L(M) were less common. The populations of groups B, D, P, X1 and X3 were larger than those of the others. Besides, the analysis of changes in the environment factors suggested that temperature was the most important factor influencing the spatiotemporal succession of algae functional groups. The strategy of algal growth followed the law: R/CR in spring --> CR/C in late spring and early summer C/CR/R/CS/S in late summer and early autumn --> CR/R in late autumn and winter. The purpose of this article is to provide theoretical support for water withdrawal safety in deep-water reservoirs.

Deep generative learning for automated EHR diagnosis of traditional Chinese medicine.

Science.gov (United States)

Liang, Zhaohui; Liu, Jun; Ou, Aihua; Zhang, Honglai; Li, Ziping; Huang, Jimmy Xiangji

2018-05-04

Computer-aided medical decision-making (CAMDM) is the method to utilize massive EMR data as both empirical and evidence support for the decision procedure of healthcare activities. Well-developed information infrastructure, such as hospital information systems and disease surveillance systems, provides abundant data for CAMDM. However, the complexity of EMR data with abstract medical knowledge makes the conventional model incompetent for the analysis. Thus a deep belief networks (DBN) based model is proposed to simulate the information analysis and decision-making procedure in medical practice. The purpose of this paper is to evaluate a deep learning architecture as an effective solution for CAMDM. A two-step model is applied in our study. At the first step, an optimized seven-layer deep belief network (DBN) is applied as an unsupervised learning algorithm to perform model training to acquire feature representation. Then a support vector machine model is adopted to DBN at the second step of the supervised learning. There are two data sets used in the experiments. One is a plain text data set indexed by medical experts. The other is a structured dataset on primary hypertension. The data are randomly divided to generate the training set for the unsupervised learning and the testing set for the supervised learning. The model performance is evaluated by the statistics of mean and variance, the average precision and coverage on the data sets. Two conventional shallow models (support vector machine / SVM and decision tree / DT) are applied as the comparisons to show the superiority of our proposed approach. The deep learning (DBN + SVM) model outperforms simple SVM and DT on two data sets in terms of all the evaluation measures, which confirms our motivation that the deep model is good at capturing the key features with less dependence when the index is built up by manpower. Our study shows the two-step deep learning model achieves high performance for medical

Evidence for the bioerosion of deep-water corals by echinoids in the Northeast Atlantic

Science.gov (United States)

Stevenson, Angela; Rocha, Carlos

2013-01-01

In situ video observations of echinoids interacting with deep-sea coral are common in the deep-sea, but paradoxically the deep-sea literature is devoid of reports of bioerosion by extant echinoids. Here we present evidence of contemporary bioerosion of cold-water coral by four species of deep-sea echinoids, Gracilechinus elegans, Gracilechinus alexandri, Cidaris cidaris, and Araeosoma fenestratum, showing that they actively predate on the living framework of reef building corals, Lophelia pertusa and Madrepora oculata, in the NE Atlantic. Echinoid specimens were collected in six canyons located in the Bay of Biscay, France and two canyons on the north side of the Porcupine Bank and Goban Spur, Ireland. A total of 44 live specimens from the four taxa (9 of G. elegans, 4 of G. alexandri, 21 of C. cidaris and 10 of A. fenestratum) showed recent ingestion of the coral infrastructure. Upon dissection, live coral skeleton was observed encased in a thick mucus layer within the gastrointestinal tract of G. elegans and G. alexandri while both live and dead coral fragments were found in C. cidaris and A. fenestratum. Echinoid bioerosion limits the growth of shallow-water reefs. Our observations suggest that echinoids may also play an important role in the ecology of deep-water coral reefs.

Deep ocean nutrients during the Last Glacial Maximum deduced from sponge silicon isotopic compositions

Science.gov (United States)

Hendry, Katharine R.; Georg, R. Bastian; Rickaby, Rosalind E. M.; Robinson, Laura F.; Halliday, Alex N.

2010-04-01

The relative importance of biological and physical processes within the Southern Ocean for the storage of carbon and atmospheric pCO 2 on glacial-interglacial timescales remains uncertain. Understanding the impact of surface biological production on carbon export in the past relies on the reconstruction of the nutrient supply from upwelling deep waters. In particular, the upwelling of silicic acid (Si(OH) 4) is tightly coupled to carbon export in the Southern Ocean via diatom productivity. Here, we address how changes in deep water Si(OH) 4 concentrations can be reconstructed using the silicon isotopic composition of deep-sea sponges. We report δ30Si of modern deep-sea sponge spicules and show that they reflect seawater Si(OH) 4 concentration. The fractionation factor of sponge δ30Si compared to seawater δ30Si shows a positive relationship with Si(OH) 4, which may be a growth rate effect. Application of this proxy in two down-core records from the Scotia Sea reveals that Si(OH) 4 concentrations in the deep Southern Ocean during the Last Glacial Maximum (LGM) were no different than today. Our result does not support a coupling of carbon and nutrient build up in an isolated deep ocean reservoir during the LGM. Our data, combined with records of stable isotopes from diatoms, are only consistent with enhanced LGM Southern Ocean nutrient utilization if there was also a concurrent reduction in diatom silicification or a shift from siliceous to organic-walled phytoplankton.

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis.

Science.gov (United States)

van der Burgh, Hannelore K; Schmidt, Ruben; Westeneng, Henk-Jan; de Reus, Marcel A; van den Berg, Leonard H; van den Heuvel, Martijn P

2017-01-01

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.

Structure, functioning, and cumulative stressors of Mediterranean deep-sea ecosystems

Science.gov (United States)

Tecchio, Samuele; Coll, Marta; Sardà, Francisco

2015-06-01

Environmental stressors, such as climate fluctuations, and anthropogenic stressors, such as fishing, are of major concern for the management of deep-sea ecosystems. Deep-water habitats are limited by primary productivity and are mainly dependent on the vertical input of organic matter from the surface. Global change over the latest decades is imparting variations in primary productivity levels across oceans, and thus it has an impact on the amount of organic matter landing on the deep seafloor. In addition, anthropogenic impacts are now reaching the deep ocean. The Mediterranean Sea, the largest enclosed basin on the planet, is not an exception. However, ecosystem-level studies of response to varying food input and anthropogenic stressors on deep-sea ecosystems are still scant. We present here a comparative ecological network analysis of three food webs of the deep Mediterranean Sea, with contrasting trophic structure. After modelling the flows of these food webs with the Ecopath with Ecosim approach, we compared indicators of network structure and functioning. We then developed temporal dynamic simulations varying the organic matter input to evaluate its potential effect. Results show that, following the west-to-east gradient in the Mediterranean Sea of marine snow input, organic matter recycling increases, net production decreases to negative values and trophic organisation is overall reduced. The levels of food-web activity followed the gradient of organic matter availability at the seafloor, confirming that deep-water ecosystems directly depend on marine snow and are therefore influenced by variations of energy input, such as climate-driven changes. In addition, simulations of varying marine snow arrival at the seafloor, combined with the hypothesis of a possible fishery expansion on the lower continental slope in the western basin, evidence that the trawling fishery may pose an impact which could be an order of magnitude stronger than a climate

How Stressful Is "Deep Bubbling"?

Science.gov (United States)

Tyrmi, Jaana; Laukkanen, Anne-Maria

2017-03-01

Water resistance therapy by phonating through a tube into the water is used to treat dysphonia. Deep submersion (≥10 cm in water, "deep bubbling") is used for hypofunctional voice disorders. Using it with caution is recommended to avoid vocal overloading. This experimental study aimed to investigate how strenuous "deep bubbling" is. Fourteen subjects, half of them with voice training, repeated the syllable [pa:] in comfortable speaking pitch and loudness, loudly, and in strained voice. Thereafter, they phonated a vowel-like sound both in comfortable loudness and loudly into a glass resonance tube immersed 10 cm into the water. Oral pressure, contact quotient (CQ, calculated from electroglottographic signal), and sound pressure level were studied. The peak oral pressure P(oral) during [p] and shuttering of the outer end of the tube was measured to estimate the subglottic pressure P(sub) and the mean P(oral) during vowel portions to enable calculation of transglottic pressure P(trans). Sensations during phonation were reported with an open-ended interview. P(sub) and P(oral) were higher in "deep bubbling" and P(trans) lower than in loud syllable phonation, but the CQ did not differ significantly. Similar results were obtained for the comparison between loud "deep bubbling" and strained phonation, although P(sub) did not differ significantly. Most of the subjects reported "deep bubbling" to be stressful only for respiratory and lip muscles. No big differences were found between trained and untrained subjects. The CQ values suggest that "deep bubbling" may increase vocal fold loading. Further studies should address impact stress during water resistance exercises. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Evaluation of the DeepWind concept

DEFF Research Database (Denmark)

Schmidt Paulsen, Uwe; Borg, Michael; Gonzales Seabra, Luis Alberto

The report describes the DeepWind 5 MW conceptual design as a baseline for results obtained in the scientific and technical work packages of the DeepWind project. A comparison of DeepWi nd with existing VAWTs and paper projects are carried out and the evaluation of the concept in terms of cost...

Simulator Studies of the Deep Stall

Science.gov (United States)

White, Maurice D.; Cooper, George E.

1965-01-01

Simulator studies of the deep-stall problem encountered with modern airplanes are discussed. The results indicate that the basic deep-stall tendencies produced by aerodynamic characteristics are augmented by operational considerations. Because of control difficulties to be anticipated in the deep stall, it is desirable that adequate safeguards be provided against inadvertent penetrations.

Effect of sevoflurane on neuronal activity during deep brain stimulation surgery for epilepsy: A case report

OpenAIRE

Michaël J. Bos, MD; Linda Ackermans, MD, PhD; Frédéric L.W.V.J. Schaper, MD; Rob P.W. Rouhl, MD, PhD; Vivianne H.J.M. van Kranen-Mastenbroek, MD, PhD; Wolfgang F. Buhre, MD, PhD; Marcus L.F. Janssen, MD, PhD

2018-01-01

Deep brain stimulation of the anterior nucleus of the thalamus is an effective treatment for patients with refractory epilepsy who do not respond sufficiently to medical therapy. Optimal therapeutic effects of deep brain stimulation probably depend on accurate positioning of the stimulating electrodes. Microelectrode recordings show bursty firing neurons in the anterior nucleus of the thalamus region, which confirms the anatomical target determined by the surgeon. Deep brain stimulation elect...

Transcriptome sequences resolve deep relationships of the grape family.

Science.gov (United States)

Wen, Jun; Xiong, Zhiqiang; Nie, Ze-Long; Mao, Likai; Zhu, Yabing; Kan, Xian-Zhao; Ickert-Bond, Stefanie M; Gerrath, Jean; Zimmer, Elizabeth A; Fang, Xiao-Dong

2013-01-01

Previous phylogenetic studies of the grape family (Vitaceae) yielded poorly resolved deep relationships, thus impeding our understanding of the evolution of the family. Next-generation sequencing now offers access to protein coding sequences very easily, quickly and cost-effectively. To improve upon earlier work, we extracted 417 orthologous single-copy nuclear genes from the transcriptomes of 15 species of the Vitaceae, covering its phylogenetic diversity. The resulting transcriptome phylogeny provides robust support for the deep relationships, showing the phylogenetic utility of transcriptome data for plants over a time scale at least since the mid-Cretaceous. The pros and cons of transcriptome data for phylogenetic inference in plants are also evaluated.

Deep Learning

DEFF Research Database (Denmark)

Jensen, Morten Bornø; Bahnsen, Chris Holmberg; Nasrollahi, Kamal

2018-01-01

I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning.......I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning....

Implementation of an integrated on-line process surveillance and diagnostic system at the Halden reactor project: MOAS

International Nuclear Information System (INIS)

Kim, I.S.; Grini, R.-E.; Nilsen, S.

2001-01-01

MOAS is an integrated on-line process surveillance and diagnostic system that uses several different models for knowledge acquisition and diagnostic reasoning, such as goal-tree success-tree model, process monitor trees, and sensor failure diagnosis trees. Within these models, the knowledge of the process and its operation, including deep knowledge, like mass balance or controller algorithm, is incorporated. During an extensive review, made as part of the integrated diagnosis system project of the Halden reactor project, MOAS (Maryland Operator Advisory System) was identified as one of the most thorough systems developed thus far. MOAS encompasses diverse functional aspects that are required for an effective process disturbance management: (1) intelligent process monitoring and alarming, (2) on-line sensor data validation and sensor failure diagnosis, (3) on-line hardware (besides sensors) failure diagnosis, and (4) real-time corrective measure synthesis. The MOAS methodology was used for the NORS (Nokia Research Simulator) process at the Halden man-machine laboratory HAMMLAB of the OECD Halden reactor project. The performance tests of MOAS, implemented in G2 real-time expert system shell, show that MOAS successfully carries out its intended functions, i.e. quickly recognizing an occurring disturbance, correctly diagnosing its cause, and presenting advice on its control to the operator. The lessons learned and insights gained during the implementation and performance tests also are discussed

Giant lipoma arising from deep lobe of the parotid gland

Directory of Open Access Journals (Sweden)

Hsu Ying-Che

2006-06-01

Full Text Available Abstract Background Lipomas are common benign soft tissue neoplasms but they are found very rarely in the deep lobe of parotid gland. Surgical intervention in these tumors is challenging because of the proximity of the facial nerve, and thus knowledge of the anatomy and meticulous surgical technique are essential. Case presentation A 71-year-old female presented with a large asymptomatic mass, which had occupied the left facial area for over the past fifteen years, and she requested surgical excision for a cosmetically better facial appearance. The computed tomography (CT scan showed a well-defined giant lipoma arising from the left deep parotid gland. The lipoma was successfully enucleated after full exposure and mobilization of the overlying facial nerve branches. The surgical specimen measured 9 × 6 cm in size, and histopathology revealed fibrolipoma. The patient experienced an uneventful recovery, with a satisfying facial contour and intact facial nerve function. Conclusion Giant lipomas involving the deep parotid lobe are extremely rare. The high-resolution CT scan provides an accurate and cost-effective preoperative investigative method. Surgical management of deep lobe lipoma should be performed by experienced surgeons due to the need for meticulous dissection of the facial nerve branches. Superficial parotidectomy before deep lobe lipoma removal may be unnecessary in selected cases because preservation of the superficial lobe may contribute to a better aesthetic and functional result.