[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.

Microbial communities of the deep unfrozen: Do microbes in taliks increase permafrost carbon vulnerability? (Invited)

Science.gov (United States)

Waldrop, M. P.; Blazewicz, S.; Jones, M.; Mcfarland, J. W.; Harden, J. W.; Euskirchen, E. S.; Turetsky, M.; Hultman, J.; Jansson, J.

2013-12-01

The vast frozen terrain of northern latitude ecosystems is typically thought of as being nearly biologically inert for the winter period. Yet deep within the frozen ground of northern latitude soils reside microbial communities that can remain active during the winter months. As we have shown previously, microbial communities may remain active in permafrost soils just below the freezing point of water. Though perhaps more importantly, microbial communities persist in unfrozen areas of water, soil, and sediment beneath water bodies the entire year. Microbial activity in taliks may have significant impacts on biogeochemical cycling in northern latitude ecosystems because their activity is not limited by the winter months. Here we present compositional and functional data, including long term incubation data, for microbial communities within permafrost landscapes, in permafrost and taliks, and the implications of these activities on permafrost carbon decomposition and the flux of CO2 and CH4. Our experiment was conducted at the Alaska Peatland Experiment (APEX) within the Bonanza Creek LTER in interior Alaska. Our site consists of a black spruce forest on permafrost that has degraded into thermokarst bogs at various times over the last five hundred years. We assume the parent substrate of the deep (1-1.5m) thermokarst peat was similar to the nearby forest soil and permafrost C before thaw. At this site, flux tower and autochamber data show that the thermokarst bog is a sink of CO2 , but a significant source of CH4. Yet this does not tell the whole story as these data do not fully capture microbial activity within the deep unfrozen talik layer. There is published evidence that within thermokarst bogs, relatively rapid decomposition of old forest floor material may be occurring. There are several possible mechanisms for this pattern; one possible mechanism for accelerated decomposition is the overwintering activities of microbial communities in taliks of thermokarst

Postoperative deep venous thrombosis in Japan. Incidence and prophylaxis

International Nuclear Information System (INIS)

Inada, K.; Shirai, N.; Hayashi, M.; Matsumoto, K.; Hirose, M.

1983-01-01

The incidence of postoperative deep venous thrombosis was investigated using the iodine-125-fibrinogen method in 256 patients undergoing major surgery. Deep venous thrombosis was found in 49 patients (15.3 percent), and nonfatal pulmonary embolism developed in one of seven patients in whom the thrombus extended to the popliteal vein. The same investigation was performed in 110 patients who wore a graduated compression stocking on one leg, with the other leg serving as a control. Deep venous thrombosis was found in 4 of 110 stockinged legs (3.6 percent) and in 16 of 110 control legs (14.5 percent). The incidence of deep venous thrombosis decreased significantly in patients who wore the stocking. An increase in femoral venous flow velocity was found in the stockinged legs by the Doppler method. The mean velocity of venous return by xenon-133 clearance was significantly greater in the stockinged legs than in the control legs. These findings were considered to support the efficacy of graduated compression stockings for the prevention of deep venous thrombosis

Formability of paperboard during deep-drawing with local steam application

Science.gov (United States)

Franke, Wilken; Stein, Philipp; Dörsam, Sven; Groche, Peter

2018-05-01

The use of paperboard can significantly improve the environmental compatibility of everyday products such as packages. Nevertheless, most packages are currently made of plastics, since the three-dimensional shaping of paperboard is possible only to a limited extent. In order to increase the forming possibilities, deep drawing of cardboard has been intensively investigated for more than a decade. An improvement with regard to increased forming limits has been achieved by heating of the tool parts, which leads to a softening of paperboard constituents such as lignin. A further approach is the moistening of the samples, whereby the hydrogen bonds between the fibers are weakened and as a result an increase of the formability. It is expected that a combination of both parameter approaches will result in a significant increase in the forming capacity and in the shape accuracy. For this reason, a new tool concept is introduced within the scope of this work which makes it possible to moisten samples during the deep drawing process by means of steam supply. The conducted investigations show that spring-back in the preferred fiber direction can be reduced by 38 %. Orthogonal to the preferred fiber direction a reduction of spring back of up to 79 % is determined, which corresponds to a perfect shape. Moreover, it was determined that the steam duration and the initial moisture content have an influence on the final shape. In addition to the increased dimensional accuracy, an optimized wrinkle compression compared to conventional deep drawing is found. According to the results, it can be summarized that a steam application in the deep drawing of paperboard significantly improves the part quality.

Enhanced Higgs boson to τ(+)τ(-) search with deep learning.

Science.gov (United States)

Baldi, P; Sadowski, P; Whiteson, D

2015-03-20

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs boson to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight nonlinear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated data set of 25%.

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.

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...

Opportunities and Challenges in Deep Mining: A Brief Review

Directory of Open Access Journals (Sweden)

Pathegama G. Ranjith

2017-08-01

Full Text Available Mineral consumption is increasing rapidly as more consumers enter the market for minerals and as the global standard of living increases. As a result, underground mining continues to progress to deeper levels in order to tackle the mineral supply crisis in the 21st century. However, deep mining occurs in a very technical and challenging environment, in which significant innovative solutions and best practice are required and additional safety standards must be implemented in order to overcome the challenges and reap huge economic gains. These challenges include the catastrophic events that are often met in deep mining engineering: rockbursts, gas outbursts, high in situ and redistributed stresses, large deformation, squeezing and creeping rocks, and high temperature. This review paper presents the current global status of deep mining and highlights some of the newest technological achievements and opportunities associated with rock mechanics and geotechnical engineering in deep mining. Of the various technical achievements, unmanned working-faces and unmanned mines based on fully automated mining and mineral extraction processes have become important fields in the 21st century.

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.

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 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

Protein oxidative stress markers in peritoneal fluids of women with deep infiltrating endometriosis are increased.

Science.gov (United States)

Santulli, Pietro; Chouzenoux, Sandrine; Fiorese, Mauro; Marcellin, Louis; Lemarechal, Herve; Millischer, Anne-Elodie; Batteux, Frédéric; Borderie, Didier; Chapron, Charles

2015-01-01

Are protein oxidative stress markers [thiols, advanced oxidation protein products (AOPP), protein carbonyls and nitrates/nitrites] in perioperative peritoneal fluid higher in women with histologically proven endometriosis when compared with endometriosis-free controls? Protein oxidative stress markers are significantly increased in peritoneal fluids from women with deep infiltrating endometriosis with intestinal involvement when compared with endometriosis-free controls. Endometriosis is a common gynaecologic condition characterized by an important inflammatory process. Various source of evidence support the role of oxidative stress in the development of endometriosis. We conducted a prospective laboratory study in a tertiary-care university hospital between January 2011 and December 2012, and included 235 non-pregnant women, younger than 42 year old, undergoing surgery for a benign gynaecological condition. After complete surgical exploration of the abdomino-pelvic cavity, 150 women with histologically proven endometriosis and 85 endometriosis-free controls women were enrolled. Women with endometriosis were staged according to a surgical classification in three different phenotypes of endometriosis: superficial peritoneal endometriosis (SUP), ovarian endometrioma (OMA) and deeply infiltrating endometriosis (DIE). Perioperative peritoneal fluids samples were obtained from all study participants. Thiols, AOPP, protein carbonyls and nitrates/nitrites were assayed in all peritoneal samples. Concentrations of peritoneal AOPP were significantly higher in endometriosis patients than in the control group (median, 128.9 µmol/l; range, 0.3-1180.1 versus median, 77.8 µmol/l; range, 0.8-616.1; P peritoneal nitrates/nitrites were higher in endometriosis patients than in the control group (median, 24.8 µmol/l; range, 1.6-681.6 versus median, 18.5 µmol/l; range, 1.6-184.5; P peritoneal fluids protein AOPP and nitrates/nitrites were significantly increased only in DIE samples

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

Mammalian niche conservation through deep time.

Directory of Open Access Journals (Sweden)

Larisa R G DeSantis

Full Text Available Climate change alters species distributions, causing plants and animals to move north or to higher elevations with current warming. Bioclimatic models predict species distributions based on extant realized niches and assume niche conservation. Here, we evaluate if proxies for niches (i.e., range areas are conserved at the family level through deep time, from the Eocene to the Pleistocene. We analyze the occurrence of all mammalian families in the continental USA, calculating range area, percent range area occupied, range area rank, and range polygon centroids during each epoch. Percent range area occupied significantly increases from the Oligocene to the Miocene and again from the Pliocene to the Pleistocene; however, mammalian families maintain statistical concordance between rank orders across time. Families with greater taxonomic diversity occupy a greater percent of available range area during each epoch and net changes in taxonomic diversity are significantly positively related to changes in percent range area occupied from the Eocene to the Pleistocene. Furthermore, gains and losses in generic and species diversity are remarkably consistent with ~2.3 species gained per generic increase. Centroids demonstrate southeastern shifts from the Eocene through the Pleistocene that may correspond to major environmental events and/or climate changes during the Cenozoic. These results demonstrate range conservation at the family level and support the idea that niche conservation at higher taxonomic levels operates over deep time and may be controlled by life history traits. Furthermore, families containing megafauna and/or terminal Pleistocene extinction victims do not incur significantly greater declines in range area rank than families containing only smaller taxa and/or only survivors, from the Pliocene to Pleistocene. Collectively, these data evince the resilience of families to climate and/or environmental change in deep time, the absence of

Major impacts of climate change on deep-sea benthic ecosystems

Directory of Open Access Journals (Sweden)

Andrew K. Sweetman

2017-02-01

Full Text Available The deep sea encompasses the largest ecosystems on Earth. Although poorly known, deep seafloor ecosystems provide services that are vitally important to the entire ocean and biosphere. Rising atmospheric greenhouse gases are bringing about significant changes in the environmental properties of the ocean realm in terms of water column oxygenation, temperature, pH and food supply, with concomitant impacts on deep-sea ecosystems. Projections suggest that abyssal (3000–6000 m ocean temperatures could increase by 1°C over the next 84 years, while abyssal seafloor habitats under areas of deep-water formation may experience reductions in water column oxygen concentrations by as much as 0.03 mL L–1 by 2100. Bathyal depths (200–3000 m worldwide will undergo the most significant reductions in pH in all oceans by the year 2100 (0.29 to 0.37 pH units. O2 concentrations will also decline in the bathyal NE Pacific and Southern Oceans, with losses up to 3.7% or more, especially at intermediate depths. Another important environmental parameter, the flux of particulate organic matter to the seafloor, is likely to decline significantly in most oceans, most notably in the abyssal and bathyal Indian Ocean where it is predicted to decrease by 40–55% by the end of the century. Unfortunately, how these major changes will affect deep-seafloor ecosystems is, in some cases, very poorly understood. In this paper, we provide a detailed overview of the impacts of these changing environmental parameters on deep-seafloor ecosystems that will most likely be seen by 2100 in continental margin, abyssal and polar settings. We also consider how these changes may combine with other anthropogenic stressors (e.g., fishing, mineral mining, oil and gas extraction to further impact deep-seafloor ecosystems and discuss the possible societal implications.

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.

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.

NATURAL GAS RESOURCES IN DEEP SEDIMENTARY BASINS

Energy Technology Data Exchange (ETDEWEB)

Thaddeus S. Dyman; Troy Cook; Robert A. Crovelli; Allison A. Henry; Timothy C. Hester; Ronald C. Johnson; Michael D. Lewan; Vito F. Nuccio; James W. Schmoker; Dennis B. Riggin; Christopher J. Schenk

2002-02-05

From a geological perspective, deep natural gas resources are generally defined as resources occurring in reservoirs at or below 15,000 feet, whereas ultra-deep gas occurs below 25,000 feet. From an operational point of view, ''deep'' is often thought of in a relative sense based on the geologic and engineering knowledge of gas (and oil) resources in a particular area. Deep gas can be found in either conventionally-trapped or unconventional basin-center accumulations that are essentially large single fields having spatial dimensions often exceeding those of conventional fields. Exploration for deep conventional and unconventional basin-center natural gas resources deserves special attention because these resources are widespread and occur in diverse geologic environments. In 1995, the U.S. Geological Survey estimated that 939 TCF of technically recoverable natural gas remained to be discovered or was part of reserve appreciation from known fields in the onshore areas and State waters of the United. Of this USGS resource, nearly 114 trillion cubic feet (Tcf) of technically-recoverable gas remains to be discovered from deep sedimentary basins. Worldwide estimates of deep gas are also high. The U.S. Geological Survey World Petroleum Assessment 2000 Project recently estimated a world mean undiscovered conventional gas resource outside the U.S. of 844 Tcf below 4.5 km (about 15,000 feet). Less is known about the origins of deep gas than about the origins of gas at shallower depths because fewer wells have been drilled into the deeper portions of many basins. Some of the many factors contributing to the origin of deep gas include the thermal stability of methane, the role of water and non-hydrocarbon gases in natural gas generation, porosity loss with increasing thermal maturity, the kinetics of deep gas generation, thermal cracking of oil to gas, and source rock potential based on thermal maturity and kerogen type. Recent experimental simulations

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)

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...

Jet-images — deep learning edition

Energy Technology Data Exchange (ETDEWEB)

Oliveira, Luke de [Institute for Computational and Mathematical Engineering, Stanford University,Huang Building 475 Via Ortega, Stanford, CA 94305 (United States); Kagan, Michael [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Mackey, Lester [Department of Statistics, Stanford University,390 Serra Mall, Stanford, CA 94305 (United States); Nachman, Benjamin; Schwartzman, Ariel [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States)

2016-07-13

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.

Jet-images — deep learning edition

International Nuclear Information System (INIS)

Oliveira, Luke de; Kagan, Michael; Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel

2016-01-01

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.

Distributed regularity of accompanying element and its deep prospecting significances in Guizhou 504 uranium mineral deposit

International Nuclear Information System (INIS)

Zhang Weiqian; Huang Kaiping; Cheng Guangqing

2012-01-01

In the 504 hydrotherm type mineral deposit, Mo, Hg, Ni, Re, Te, Se element (Mo, Hg are industrial mineral deposit and Ni, Re, Te, Se are scarce element) reach the industrial integrated utilization request, the scarce element widely distributed in acid orebody (upper ore zone) and alkali orebody (lower ore zone). Based on composite samples of uranium ore in the analysis, through computer processing, the linear regression and R-factor analysis, Reveals the relationship between uranium and other elements. They haven't correlation among the U, Hg, Mo. The relation- ship among the Ni, Re, Te, Se is germane. Using this correlation, deep in the deposit and surrounding exploration provides the basis for deep. (authors)

Simultaneous bilateral stereotactic procedure for deep brain stimulation implants: a significant step for reducing operation time.

Science.gov (United States)

Fonoff, Erich Talamoni; Azevedo, Angelo; Angelos, Jairo Silva Dos; Martinez, Raquel Chacon Ruiz; Navarro, Jessie; Reis, Paul Rodrigo; Sepulveda, Miguel Ernesto San Martin; Cury, Rubens Gisbert; Ghilardi, Maria Gabriela Dos Santos; Teixeira, Manoel Jacobsen; Lopez, William Omar Contreras

2016-07-01

OBJECT Currently, bilateral procedures involve 2 sequential implants in each of the hemispheres. The present report demonstrates the feasibility of simultaneous bilateral procedures during the implantation of deep brain stimulation (DBS) leads. METHODS Fifty-seven patients with movement disorders underwent bilateral DBS implantation in the same study period. The authors compared the time required for the surgical implantation of deep brain electrodes in 2 randomly assigned groups. One group of 28 patients underwent traditional sequential electrode implantation, and the other 29 patients underwent simultaneous bilateral implantation. Clinical outcomes of the patients with Parkinson's disease (PD) who had undergone DBS implantation of the subthalamic nucleus using either of the 2 techniques were compared. RESULTS Overall, a reduction of 38.51% in total operating time for the simultaneous bilateral group (136.4 ± 20.93 minutes) as compared with that for the traditional consecutive approach (220.3 ± 27.58 minutes) was observed. Regarding clinical outcomes in the PD patients who underwent subthalamic nucleus DBS implantation, comparing the preoperative off-medication condition with the off-medication/on-stimulation condition 1 year after the surgery in both procedure groups, there was a mean 47.8% ± 9.5% improvement in the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) score in the simultaneous group, while the sequential group experienced 47.5% ± 15.8% improvement (p = 0.96). Moreover, a marked reduction in the levodopa-equivalent dose from preoperatively to postoperatively was similar in these 2 groups. The simultaneous bilateral procedure presented major advantages over the traditional sequential approach, with a shorter total operating time. CONCLUSIONS A simultaneous stereotactic approach significantly reduces the operation time in bilateral DBS procedures, resulting in decreased microrecording time, contributing to the optimization of functional

Building Program Vector Representations for Deep Learning

OpenAIRE

Mou, Lili; Li, Ge; Liu, Yuxuan; Peng, Hao; Jin, Zhi; Xu, Yan; Zhang, Lu

2014-01-01

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, whi...

Clay as indicator of sediment plume movement in deep-sea environment

Digital Repository Service at National Institute of Oceanography (India)

Valsangkar, A.B.

artificially disturbed and resuspended 5 m above the seabed in 1997 during the Indian Deep-Sea Experiment. Initial studies have shown that the clay content during monitoring-1 phase significantly increased compared to post-disturbance, by 15 and 24...

Potential for a significant deep basin geothermal system in Tintic Valley, Utah

Science.gov (United States)

Hardwick, C.; Kirby, S.

2014-12-01

The combination of regionally high heat flow, deep basins, and permeable reservoir rocks in the eastern Great Basin may yield substantial new geothermal resources. We explore a deep sedimentary basin geothermal prospect beneath Tintic Valley in central Utah using new 2D and 3D models coupled with existing estimates of heat flow, geothermometry, and shallow hydrologic data. Tintic Valley is a sediment-filled basin bounded to the east and west by bedrock mountain ranges where heat-flow values vary from 85 to over 240 mW/m2. Based on modeling of new and existing gravity data, a prominent 30 mGal low indicates basin fill thickness may exceed 2 km. The insulating effect of relatively low thermal conductivity basin fill in Tintic Valley, combined with typical Great Basin heat flow, predict temperatures greater than 150 °C at 3 km depth. The potential reservoir beneath the basin fill is comprised of Paleozoic carbonate and clastic rocks. The hydrology of the Tintic Valley is characterized by a shallow, cool groundwater system that recharges along the upper reaches of the basin and discharges along the valley axis and to a series of wells. The east mountain block is warm and dry, with groundwater levels just above the basin floor and temperatures >50 °C at depth. The west mountain block contains a shallow, cool meteoric groundwater system. Fluid temperatures over 50 °C are sufficient for direct-use applications, such as greenhouses and aquaculture, while temperatures exceeding 140°C are suitable for binary geothermal power plants. The geologic setting and regionally high heat flow in Tintic Valley suggest a geothermal resource capable of supporting direct-use geothermal applications and binary power production could be present.

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.

Deep Learning in Radiology.

Science.gov (United States)

McBee, Morgan P; Awan, Omer A; Colucci, Andrew T; Ghobadi, Comeron W; Kadom, Nadja; Kansagra, Akash P; Tridandapani, Srini; Auffermann, William F

2018-03-29

As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably powerful tool for image processing in recent years. In this work, the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning provides an overview of DL for the radiologist. This article aims to present an overview of DL in a manner that is understandable to radiologists; to examine past, present, and future applications; as well as to evaluate how radiologists may benefit from this remarkable new tool. We describe several areas within radiology in which DL techniques are having the most significant impact: lesion or disease detection, classification, quantification, and segmentation. The legal and ethical hurdles to implementation are also discussed. By taking advantage of this powerful tool, radiologists can become increasingly more accurate in their interpretations with fewer errors and spend more time to focus on patient care. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

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.

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 Learning and Bayesian Methods

Directory of Open Access Journals (Sweden)

Prosper Harrison B.

2017-01-01

Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.

Fish protein hydrolysates: application in deep-fried food and food safety analysis.

Science.gov (United States)

He, Shan; Franco, Christopher; Zhang, Wei

2015-01-01

Four different processes (enzymatic, microwave-intensified enzymatic, chemical, and microwave-intensified chemical) were used to produce fish protein hydrolysates (FPH) from Yellowtail Kingfish for food applications. In this study, the production yield and oil-binding capacity of FPH produced from different processes were evaluated. Microwave intensification significantly increased the production yields of enzymatic process from 42% to 63%. It also increased the production yields of chemical process from 87% to 98%. The chemical process and microwave-intensified chemical process produced the FPH with low oil-binding capacity (8.66 g oil/g FPH and 6.25 g oil/g FPH), whereas the microwave-intensified enzymatic process produced FPH with the highest oil-binding capacity (16.4 g oil/g FPH). The FPH from the 4 processes were applied in the formulation of deep-fried battered fish and deep-fried fish cakes. The fat uptake of deep-fried battered fish can be reduced significantly from about 7% to about 4.5% by replacing 1% (w/w) batter powder with FPH, and the fat uptake of deep-fried fish cakes can be significantly reduced from about 11% to about 1% by replacing 1% (w/w) fish mince with FPH. Food safety tests of the FPH produced by these processes demonstrated that the maximum proportion of FPH that can be safely used in food formulation is 10%, due to its high content of histamine. This study demonstrates the value of FPH to the food industry and bridges the theoretical studies with the commercial applications of FPH. © 2015 Institute of Food Technologists®

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.

Software Graphics Processing Unit (sGPU) for Deep Space Applications

Science.gov (United States)

McCabe, Mary; Salazar, George; Steele, Glen

2015-01-01

A graphics processing capability will be required for deep space missions and must include a range of applications, from safety-critical vehicle health status to telemedicine for crew health. However, preliminary radiation testing of commercial graphics processing cards suggest they cannot operate in the deep space radiation environment. Investigation into an Software Graphics Processing Unit (sGPU)comprised of commercial-equivalent radiation hardened/tolerant single board computers, field programmable gate arrays, and safety-critical display software shows promising results. Preliminary performance of approximately 30 frames per second (FPS) has been achieved. Use of multi-core processors may provide a significant increase in performance.

Marked Increase in Flow Velocities During Deep Expiration: A Duplex Doppler Sign of Celiac Artery Compression Syndrome

International Nuclear Information System (INIS)

Erden, Ayse; Yurdakul, Mehmet; Cumhur, Turhan

1999-01-01

Symptoms of chronic mesenteric ischemia develop when the celiac artery is constricted by the median arcuate ligament of the diaphragm. Lateral aortography is the primary modality for diagnosing ligamentous compression of the celiac artery. However, duplex Doppler sonography performed during deep expiration can cause a marked increase in flow velocities at the compressed region of the celiac artery and suggest the diagnosis of celiac arterial constriction due to the diaphragmatic ligament. RID='''' ID='''' Correspondence to: A. Erden, M.D., Hafta sokak. 23/6, Gaziosmanpasa, 06700 Ankara, Turkey

Deep space propagation experiments at Ka-band

Science.gov (United States)

Butman, Stanley A.

1990-01-01

Propagation experiments as essential components of the general plan to develop an operational deep space telecommunications and navigation capability at Ka-band (32 to 35 GHz) by the end of the 20th century are discussed. Significant benefits of Ka-band over the current deep space standard X-band (8.4 GHz) are an improvement of 4 to 10 dB in telemetry capacity and a similar increase in radio navigation accuracy. Propagation experiments are planned on the Mars Observer Mission in 1992 in preparation for the Cassini Mission to Saturn in 1996, which will use Ka-band in the search for gravity waves as well as to enhance telemetry and navigation at Saturn in 2002. Subsequent uses of Ka-band are planned for the Solar Probe Mission and the Mars Program.

Nano-Satellite Secondary Spacecraft on Deep Space Missions

Science.gov (United States)

Klesh, Andrew T.; Castillo-Rogez, Julie C.

2012-01-01

NanoSat technology has opened Earth orbit to extremely low-cost science missions through a common interface that provides greater launch accessibility. They have also been used on interplanetary missions, but these missions have used one-off components and architectures so that the return on investment has been limited. A natural question is the role that CubeSat-derived NanoSats could play to increase the science return of deep space missions. We do not consider single instrument nano-satellites as likely to complete entire Discovery-class missions alone,but believe that nano-satellites could augment larger missions to significantly increase science return. The key advantages offered by these mini-spacecrafts over previous planetary probes is the common availability of advanced subsystems that open the door to a large variety of science experiments, including new guidance, navigation and control capabilities. In this paper, multiple NanoSat science applications are investigated, primarily for high risk/high return science areas. We also address the significant challenges and questions that remain as obstacles to the use of nano-satellites in deep space missions. Finally, we provide some thoughts on a development roadmap toward interplanetary usage of NanoSpacecraft.

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.

Neuromorphic Deep Learning Machines

OpenAIRE

Neftci, E; Augustine, C; Paul, S; Detorakis, G

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 Back Propagation (BP) rule, often relies on the immediate availability of network-wide...

Deep Crustal Melting and the Survival of Continental Crust

Science.gov (United States)

Whitney, D.; Teyssier, C. P.; Rey, P. F.; Korchinski, M.

2017-12-01

Plate convergence involving continental lithosphere leads to crustal melting, which ultimately stabilizes the crust because it drives rapid upward flow of hot deep crust, followed by rapid cooling at shallow levels. Collision drives partial melting during crustal thickening (at 40-75 km) and/or continental subduction (at 75-100 km). These depths are not typically exceeded by crustal rocks that are exhumed in each setting because partial melting significantly decreases viscosity, facilitating upward flow of deep crust. Results from numerical models and nature indicate that deep crust moves laterally and then vertically, crystallizing at depths as shallow as 2 km. Deep crust flows en masse, without significant segregation of melt into magmatic bodies, over 10s of kms of vertical transport. This is a major mechanism by which deep crust is exhumed and is therefore a significant process of heat and mass transfer in continental evolution. The result of vertical flow of deep, partially molten crust is a migmatite dome. When lithosphere is under extension or transtension, the deep crust is solicited by faulting of the brittle upper crust, and the flow of deep crust in migmatite domes traverses nearly the entire thickness of orogenic crust in Recognition of the importance of migmatite (gneiss) domes as archives of orogenic deep crust is applicable to determining the chemical and physical properties of continental crust, as well as mechanisms and timescales of crustal differentiation.

Decadal trends in deep ocean salinity and regional effects on steric sea level

Science.gov (United States)

Purkey, S. G.; Llovel, W.

2017-12-01

We present deep (below 2000 m) and abyssal (below 4000 m) global ocean salinity trends from the 1990s through the 2010s and assess the role of deep salinity in local and global sea level budgets. Deep salinity trends are assessed using all deep basins with available full-depth, high-quality hydrographic section data that have been occupied two or more times since the 1980s through either the World Ocean Circulation Experiment (WOCE) Hydrographic Program or the Global Ship-Based Hydrographic Investigations Program (GO-SHIP). All salinity data is calibrated to standard seawater and any intercruise offsets applied. While the global mean deep halosteric contribution to sea level rise is close to zero (-0.017 +/- 0.023 mm/yr below 4000 m), there is a large regional variability with the southern deep basins becoming fresher and northern deep basins becoming more saline. This meridional gradient in the deep salinity trend reflects different mechanisms driving the deep salinity variability. The deep Southern Ocean is freshening owing to a recent increased flux of freshwater to the deep ocean. Outside of the Southern Ocean, the deep salinity and temperature changes are tied to isopycnal heave associated with a falling of deep isopycnals in recent decades. Therefore, regions of the ocean with a deep salinity minimum are experiencing both a halosteric contraction with a thermosteric expansion. While the thermosteric expansion is larger in most cases, in some regions the halosteric compensates for as much as 50% of the deep thermal expansion, making a significant contribution to local sea level rise budgets.

A Modeling Study of Deep Water Renewal in the Red Sea

Science.gov (United States)

Yao, F.; Hoteit, I.

2016-02-01

Deep water renewal processes in the Red Sea are examined in this study using a 50-year numerical simulation from 1952-2001. The deep water in the Red Sea below the thermocline ( 200 m) exhibits a near-uniform vertical structure in temperature and salinity, but geochemical tracer distributions, such as 14C and 3He, and dissolved oxygen concentrations indicate that the deep water is renewed on time scales as short as 36 years. The renewal process is accomplished through a deep overturning cell that consists of a southward bottom current and a northward returning current at depths of 400-600 m. Three sources regions are proposed for the formation of the deep water, including two deep outflows from the Gulfs of Aqaba and Suez and winter deep convections in the northern Red Sea. The MITgcm (MIT general circulation model), which has been used to simulate the shallow overturning circulations in the Red Sea, is configured in this study with increased resolutions in the deep water. During the 50 years of simulation, artificial passive tracers added in the model indicate that the deep water in the Red Sea was only episodically renewed during some anomalously cold years; two significant episodes of deep water renewal are reproduced in the winters of 1983 and 1992, in accordance with reported historical hydrographic observations. During these renewal events, deep convections reaching the bottom of the basin occurred, which further facilitated deep sinking of the outflows from the Gulfs of Aqaba and Suez. Ensuing spreading of the newly formed deep water along the bottom caused upward displacements of thermocline, which may have profound effects on the water exchanges in the Strait of Bab el Mandeb between the Red Sea and the Gulf of Aden and the functioning of the ecosystem in the Red Sea by changing the vertical distributions of nutrients.

Physiological effects of hypercapnia in the deep-sea bivalve Acesta excavata (Fabricius, 1779) (Bivalvia; Limidae)

DEFF Research Database (Denmark)

Hammer, Karen M.; Kristiansen, Erlend; Zachariassen, Karl Erik

2011-01-01

The option of storing CO(2) in subsea rock formations to mitigate future increases in atmospheric CO(2) may induce problems for animals in the deep sea. In the present study the deep-sea bivalve Acesta excavata was subjected to environmental hypercapnia (pHSW 6.35, P(CO2), =33,000 mu atm...... extracellular pH remained significantly lower during recovery. Intracellular non-bicarbonate buffering capacity of the posterior adductor muscle of hypercapnic animals was significantly lower than control values, but this was not the case for the remaining tissues analyzed. Oxygen consumption initially dropped...

Deep water dissolution in Marine Isotope Stage 3 from the northern South China Sea

Science.gov (United States)

Huang, B.

2015-12-01

The production, transport, deposition, and dissolution of carbonate profoundly implicate the global carbon cycle affect the inventory and distribution of dissolved organic carbon (DIC) and alkalinity (ALK), which drive atmospheric CO2 change on glacial-interglacial timescale. the process may provide significant clues for improved understanding of the mechanisms that control the global climate system. In this study, we calculate and analyze the foraminiferal dissolution index (FDX) and the fragmentation ratios of planktonic foraminifera over 60-25 ka based on samples from 17924 and ODP 1144 in the northeastern South China Sea (SCS) to reconstruct the deep water carbonate dissolution during Marine Isotope Stage 3 (MIS 3). Result shows that the dissolution of carbonate increases gradually at 17924 but keeps stable at ODP 1144. The changes of FDX coincidence with that of fragmentation ratios at 17924 and ODP 1144 suggest both indexes can be used as reliable dissolving proxies of planktonic foraminifera. Comparing FDX and fragmentation ratios at both sites, we find the FDX and fragmentation ratios at 17924 are higher than those at 1144, indicating that carbonate dissolution is intenser in 17924 core during MIS 3. The increasing total percentage of both N. dutertrei and G. bulloides during MIS 3 reveals the rising primary productivity that may lead to deep water [CO32-] decrease. The slow down of thermohaline circulation may increase deep water residence time and accelerate carbonate dissolution. In addition, the covering of ice caps, iron supply and increased surface-water stratification also contribute to atmosphere CO2 depletion and [CO32-] decrease in deep water. In the meanwhile, regression result from colder temperature increases the input of ALK and DIC to the deep ocean and deepens the carbonate saturation depth, which makes the deep water [CO32-] rise. In ODP Site 1144, the decrease in [CO32-] caused by more CO2 restored in deep water is equal to the increase in

Marine litter on deep Arctic seafloor continues to increase and spreads to the North at the HAUSGARTEN observatory

Science.gov (United States)

Tekman, Mine B.; Krumpen, Thomas; Bergmann, Melanie

2017-02-01

The increased global production of plastics has been mirrored by greater accumulations of plastic litter in marine environments worldwide. Global plastic litter estimates based on field observations account only for 1% of the total volumes of plastic assumed to enter the marine ecosystem from land, raising again the question 'Where is all the plastic? '. Scant information exists on temporal trends on litter transport and litter accumulation on the deep seafloor. Here, we present the results of photographic time-series surveys indicating a strong increase in marine litter over the period of 2002-2014 at two stations of the HAUSGARTEN observatory in the Arctic (2500 m depth). Plastic accounted for the highest proportion (47%) of litter recorded at HAUSGARTEN for the whole study period. When the most southern station was considered separately, the proportion of plastic items was even higher (65%). Increasing quantities of small plastics raise concerns about fragmentation and future microplastic contamination. Analysis of litter types and sizes indicate temporal and spatial differences in the transport pathways to the deep sea for different categories of litter. Litter densities were positively correlated with the counts of ship entering harbour at Longyearbyen, the number of active fishing vessels and extent of summer sea ice. Sea ice may act as a transport vehicle for entrained litter, being released during periods of melting. The receding sea ice coverage associated with global change has opened hitherto largely inaccessible environments to humans and the impacts of tourism, industrial activities including shipping and fisheries, all of which are potential sources of marine litter.

The Effects of Temperature and Hydrostatic Pressure on Metal Toxicity: Insights into Toxicity in the Deep Sea.

Science.gov (United States)

Brown, Alastair; Thatje, Sven; Hauton, Chris

2017-09-05

Mineral prospecting in the deep sea is increasing, promoting concern regarding potential ecotoxicological impacts on deep-sea fauna. Technological difficulties in assessing toxicity in deep-sea species has promoted interest in developing shallow-water ecotoxicological proxy species. However, it is unclear how the low temperature and high hydrostatic pressure prevalent in the deep sea affect toxicity, and whether adaptation to deep-sea environmental conditions moderates any effects of these factors. To address these uncertainties we assessed the effects of temperature and hydrostatic pressure on lethal and sublethal (respiration rate, antioxidant enzyme activity) toxicity in acute (96 h) copper and cadmium exposures, using the shallow-water ecophysiological model organism Palaemon varians. Low temperature reduced toxicity in both metals, but reduced cadmium toxicity significantly more. In contrast, elevated hydrostatic pressure increased copper toxicity, but did not affect cadmium toxicity. The synergistic interaction between copper and cadmium was not affected by low temperature, but high hydrostatic pressure significantly enhanced the synergism. Differential environmental effects on toxicity suggest different mechanisms of action for copper and cadmium, and highlight that mechanistic understanding of toxicity is fundamental to predicting environmental effects on toxicity. Although results infer that sensitivity to toxicants differs across biogeographic ranges, shallow-water species may be suitable ecotoxicological proxies for deep-sea species, dependent on adaptation to habitats with similar environmental variability.

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

Neuropsychiatric Outcome of an Adolescent Who Received Deep Brain Stimulation for Tourette's Syndrome

Directory of Open Access Journals (Sweden)

S. J. Pullen

2011-01-01

Full Text Available This case study followed one adolescent patient who underwent bilateral deep brain stimulation of the centromedian parafascicular complex (CM-Pf for debilitating, treatment refractory Tourette's syndrome for a period of 1.5 years. Neurocognitive testing showed no significant changes between baseline and follow-up assessments. Psychiatric assessment revealed positive outcomes in overall adaptive functioning and reduction in psychotropic medication load in this patient. Furthermore, despite significant baseline psychiatric comorbidity, this patient reported no suicidal ideation following electrode implantation. Deep brain stimulation is increasingly being used in children and adolescents. This case reports on the positive neurologic and neuropsychiatric outcome of an adolescent male with bilateral CM-Pf stimulation.

Image Captioning with Deep Bidirectional LSTMs

OpenAIRE

Wang, Cheng; Yang, Haojin; Bartz, Christian; Meinel, Christoph

2016-01-01

This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. Two novel deep bidirectional variant models, in which we increase the depth of nonlinearity transition in different way, are propose...

Deep Generative Models for Molecular Science

DEFF Research Database (Denmark)

Jørgensen, Peter Bjørn; Schmidt, Mikkel Nørgaard; Winther, Ole

2018-01-01

Generative deep machine learning models now rival traditional quantum-mechanical computations in predicting properties of new structures, and they come with a significantly lower computational cost, opening new avenues in computational molecular science. In the last few years, a variety of deep...... generative models have been proposed for modeling molecules, which differ in both their model structure and choice of input features. We review these recent advances within deep generative models for predicting molecular properties, with particular focus on models based on the probabilistic autoencoder (or...

Ileus following total hip or knee arthroplasty is associated with increased risk of deep venous thrombosis and pulmonary embolism.

Science.gov (United States)

Berend, Keith R; Lombardi, Adolph V; Mallory, Thomas H; Dodds, Kathleen L; Adams, Joanne B

2004-10-01

Venous thromboembolic disease (VTD), deep venous thrombosis and pulmonary embolism, causes morbidity and mortality following total hip and total knee arthroplasties, while ileus complicates up to 4.0%. The clinical courses of 2,949 patients undergoing 3,364 consecutive primary and revision total hip and total knee arthroplasties, radical debridements, and reimplantations at one institution over a 2-year period were reviewed to examine the relationship between ileus and VTD. VTD prophylaxis consisted of aspirin and intermittent plantar pulse boots for all patients except those at high risk, who received parenteral chemical prophylaxis and boots. Ileus occurred in 62 patients (2.1%) and symptomatic DVT in 51 (1.7%). With ileus, the incidence of DVT was 8.1%: odds ratio 5.5 (P =.0036). Symptomatic pulmonary embolism occurred in 7 patients (0.24%); with ileus the incidence was 3.2%: odds ratio 19.6 (P =.0082). A significant increase was observed in rates of VTD with ileus. We recommend using parenteral chemical and mechanical prophylaxis in patients with ileus following total hip and total knee arthroplasties.

Deep-sea fungi

Digital Repository Service at National Institute of Oceanography (India)

Raghukumar, C; Damare, S.R.

significant in terms of carbon sequestration (5, 8). In light of this, the diversity, abundance, and role of fungi in deep-sea sediments may form an important link in the global C biogeochemistry. This review focuses on issues related to collection...

Sixteen-Day Bedrest Significantly Increases Plasma Colloid Osmotic Pressure

Science.gov (United States)

Hargens, Alan R.; Hsieh, S. T.; Murthy, G.; Ballard, R. E.; Convertino, V. A.; Wade, Charles E. (Technical Monitor)

1994-01-01

Upon exposure to microgravity, astronauts lose up to 10% of their total plasma volume, which may contribute to orthostatic intolerance after space flight. Because plasma colloid osmotic pressure (COP) is a primary factor maintaining plasma volume, our objective was to measure time course changes in COP during microgravity simulated by 6 deg. head-down tilt (HDT). Seven healthy male subjects (30-55 years of age) were placed in HDT for 16 days. For the purpose of another study, three of the seven subjects were chosen to exercise on a cycle ergometer on day 16. Blood samples were drawn immediately before bedrest on day 14 of bedrest, 18-24 hours following exercise while all subjects were still in HDT and 1 hour following bedrest termination. Plasma COP was measured in all 20 microliter EDTA-treated samples using an osmometer fitted with a PM 30 membrane. Data were analyzed with paired and unpaired t-tests. Plasma COP on day 14 of bedrest (29.9 +/- 0.69 mmHg) was significantly higher (p less than 0.005) than the control, pre-bedrest value (23.1 +/- 0.76 mmHg). At one hour of upright recovery after HDT, plasma COP remained significantly elevated (exercise: 26.9 +/- 0.87 mmHg; no exercise: 26.3 +/- 0.85 mmHg). Additionally, exercise had no significant effect on plasma COP 18-24 hours following exercise (exercise: 27.8 +/- 1.09 mmHg; no exercise: 27.1 +/- 0.78 mmHg). Our results demonstrate that plasma COP increases significantly with microgravity simulated by HDT. However, preliminary results indicate exercise during HDT does not significantly affect plasma COP.

Metals of Deep Ocean Water Increase the Anti-Adipogenesis Effect of Monascus-Fermented Product via Modulating the Monascin and Ankaflavin Production.

Science.gov (United States)

Lung, Tzu-Ying; Liao, Li-Ya; Wang, Jyh-Jye; Wei, Bai-Luh; Huang, Ping-Yi; Lee, Chun-Lin

2016-05-27

Deep ocean water (DOW) obtained from a depth of more than 200 m includes abundant nutrients and minerals. DOW was proven to positively increase monascin (MS) and ankaflavin (AK) production and the anti-adipogenesis effect of Monascus-fermented red mold dioscorea (RMD). However, the influences that the major metals in DOW have on Monascus secondary metabolite biosynthesis and anti-adipogenesis remain unknown. Therefore, the major metals in DOW were used as the culture water to produce RMD. The secondary metabolites production and anti-adipogenesis effect of RMD cultured with various individual metal waters were investigated. In the results, the addition of water with Mg, Ca, Zn, and Fe increased MS and AK production and inhibited mycotoxin citrinin (CT). However, the positive influence may be contributed to the regulation of pigment biosynthesis. Furthermore, in the results of cell testing, higher lipogenesis inhibition was seen in the treatments of various ethanol extracts of RMD cultured with water containing Mg, K, Zn, and Fe than in those of RMD cultured with ultra-pure water. In conclusion, various individual metals resulted in different effects on MS and AK productions as well as the anti-adipogenesis effect of RMD, but the specific metals contained in DOW may cause synergistic or comprehensive effects that increase the significantly positive influence.

Flexible deep-ultraviolet light-emitting diodes for significant improvement of quantum efficiencies by external bending

KAUST Repository

Shervin, Shahab

2018-01-26

Deep ultraviolet (DUV) light at the wavelength range of 250‒280 nm (UVC spectrum) is essential for numerous applications such as sterilization, purification, sensing, and communication. III-nitride-based DUV light-emitting diodes (DUV LEDs), like other solid-state lighting sources, offer a great potential to replace the conventional gas-discharged lamps with short lifetimes and toxic-element-bearing nature. However, unlike visible LEDs, the DUV LEDs are still suffering from low quantum efficiencies (QEs) and low optical output powers. In this work, reported is a new route to improve QEs of AlGaN-based DUV LEDs using mechanical flexibility of recently developed bendable thin-film structures. Numerical studies show that electronic band structures of AlGaN heterostructures and resulting optical and electrical characteristics of the devices can be significantly modified by external bending through active control of piezoelectric polarization. Internal quantum efficiency (IQE) is enhanced higher than three times, when the DUV LEDs are moderately bent to induce in-plane compressive strain in the heterostructure. Furthermore, efficiency droop at high injection currents is mitigated and turn-on voltage of diodes decreases with the same bending condition. The concept of bendable DUV LEDs with a controlled external strain can provide a new path for high-output-power and high-efficiency devices.

Flexible deep-ultraviolet light-emitting diodes for significant improvement of quantum efficiencies by external bending

KAUST Repository

Shervin, Shahab; Oh, Seung Kyu; Park, Hyun Jung; Lee, Keon Hwa; Asadirad, Mojtaba; Kim, Seung Hwan; Kim, Jeomoh; Pouladi, Sara; Lee, Sung-Nam; Li, Xiaohang; Kwak, Joon-Seop; Ryou, Jae-Hyun

2018-01-01

Deep ultraviolet (DUV) light at the wavelength range of 250‒280 nm (UVC spectrum) is essential for numerous applications such as sterilization, purification, sensing, and communication. III-nitride-based DUV light-emitting diodes (DUV LEDs), like other solid-state lighting sources, offer a great potential to replace the conventional gas-discharged lamps with short lifetimes and toxic-element-bearing nature. However, unlike visible LEDs, the DUV LEDs are still suffering from low quantum efficiencies (QEs) and low optical output powers. In this work, reported is a new route to improve QEs of AlGaN-based DUV LEDs using mechanical flexibility of recently developed bendable thin-film structures. Numerical studies show that electronic band structures of AlGaN heterostructures and resulting optical and electrical characteristics of the devices can be significantly modified by external bending through active control of piezoelectric polarization. Internal quantum efficiency (IQE) is enhanced higher than three times, when the DUV LEDs are moderately bent to induce in-plane compressive strain in the heterostructure. Furthermore, efficiency droop at high injection currents is mitigated and turn-on voltage of diodes decreases with the same bending condition. The concept of bendable DUV LEDs with a controlled external strain can provide a new path for high-output-power and high-efficiency devices.

Man and the last great wilderness: human impact on the deep sea.

Directory of Open Access Journals (Sweden)

Eva Ramirez-Llodra

Full Text Available The deep sea, the largest ecosystem on Earth and one of the least studied, harbours high biodiversity and provides a wealth of resources. Although humans have used the oceans for millennia, technological developments now allow exploitation of fisheries resources, hydrocarbons and minerals below 2000 m depth. The remoteness of the deep seafloor has promoted the disposal of residues and litter. Ocean acidification and climate change now bring a new dimension of global effects. Thus the challenges facing the deep sea are large and accelerating, providing a new imperative for the science community, industry and national and international organizations to work together to develop successful exploitation management and conservation of the deep-sea ecosystem. This paper provides scientific expert judgement and a semi-quantitative analysis of past, present and future impacts of human-related activities on global deep-sea habitats within three categories: disposal, exploitation and climate change. The analysis is the result of a Census of Marine Life--SYNDEEP workshop (September 2008. A detailed review of known impacts and their effects is provided. The analysis shows how, in recent decades, the most significant anthropogenic activities that affect the deep sea have evolved from mainly disposal (past to exploitation (present. We predict that from now and into the future, increases in atmospheric CO(2 and facets and consequences of climate change will have the most impact on deep-sea habitats and their fauna. Synergies between different anthropogenic pressures and associated effects are discussed, indicating that most synergies are related to increased atmospheric CO(2 and climate change effects. We identify deep-sea ecosystems we believe are at higher risk from human impacts in the near future: benthic communities on sedimentary upper slopes, cold-water corals, canyon benthic communities and seamount pelagic and benthic communities. We finalise this

Man and the Last Great Wilderness: Human Impact on the Deep Sea

Science.gov (United States)

Ramirez-Llodra, Eva; Tyler, Paul A.; Baker, Maria C.; Bergstad, Odd Aksel; Clark, Malcolm R.; Escobar, Elva; Levin, Lisa A.; Menot, Lenaick; Rowden, Ashley A.; Smith, Craig R.; Van Dover, Cindy L.

2011-01-01

The deep sea, the largest ecosystem on Earth and one of the least studied, harbours high biodiversity and provides a wealth of resources. Although humans have used the oceans for millennia, technological developments now allow exploitation of fisheries resources, hydrocarbons and minerals below 2000 m depth. The remoteness of the deep seafloor has promoted the disposal of residues and litter. Ocean acidification and climate change now bring a new dimension of global effects. Thus the challenges facing the deep sea are large and accelerating, providing a new imperative for the science community, industry and national and international organizations to work together to develop successful exploitation management and conservation of the deep-sea ecosystem. This paper provides scientific expert judgement and a semi-quantitative analysis of past, present and future impacts of human-related activities on global deep-sea habitats within three categories: disposal, exploitation and climate change. The analysis is the result of a Census of Marine Life – SYNDEEP workshop (September 2008). A detailed review of known impacts and their effects is provided. The analysis shows how, in recent decades, the most significant anthropogenic activities that affect the deep sea have evolved from mainly disposal (past) to exploitation (present). We predict that from now and into the future, increases in atmospheric CO2 and facets and consequences of climate change will have the most impact on deep-sea habitats and their fauna. Synergies between different anthropogenic pressures and associated effects are discussed, indicating that most synergies are related to increased atmospheric CO2 and climate change effects. We identify deep-sea ecosystems we believe are at higher risk from human impacts in the near future: benthic communities on sedimentary upper slopes, cold-water corals, canyon benthic communities and seamount pelagic and benthic communities. We finalise this review with a short

Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

Science.gov (United States)

Xue, Yong; Chen, Shihui; Liu, Yong

2017-01-01

Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection of cancer. In recent years, deep leaning has been widely used in medical imaging analysis, as it overcomes the limitations of visual assessment and traditional machine learning techniques by extracting hierarchical features with powerful representation capability. Research on cancer molecular images using deep learning techniques is also increasing dynamically. Hence, in this paper, we review the applications of deep learning in molecular imaging in terms of tumor lesion segmentation, tumor classification, and survival prediction. We also outline some future directions in which researchers may develop more powerful deep learning models for better performance in the applications in cancer molecular imaging. PMID:29114182

A Comparative Study of Deep Neck Abscess with Regards to Anatomical Location and Age Groups Using CT and Clinical Data

International Nuclear Information System (INIS)

Park, Chan Ho; Han, Jong Kyu; Kim, Young Tong; Shin, Hyeong Cheol; Kim, Hyung Hwan; Jou, Sung Shick

2012-01-01

To evaluate differences anatomical location and age groups on CT and clinical data in deep neck abscess. This study included 200 patients who underwent CT and were diagnosed with a deep neck abscess, from December 2005 to July 2010. Patients were divided into four groups by age (children, adolescent, adult, elderly). Next, the anatomic location, location multiplicity and clinical data regarding the deep neck abscesses were analyzed retrospectively. The deep neck abscesses observed were defined as superficial or deep and partitioned into sub-groups, with further analysis of their clinical data. The incidence of the parapharyngeal abscess was more frequent in children and elderly groups (p < 0.05). The masticator abscess was only observed among patients in the elderly group (p < 0.05). Multiple locations were observed with increased frequency in children and elderly groups (p < 0.05). Swelling in the neck was more frequently observed in children and elderly groups (p < 0.05), cervical lymphadenitis was frequently seen in children and adolescent groups (p < 0.05), and the incidence of symptoms including sore throat were significantly increased in adolescent and adult groups (p < 0.05). Location multiplicity was significantly higher in parapharyngeal, retropharyngeal, submandibular, danger, visceral and masticator spaces than other spaces (p < 0.05). With regards to anatomic location, neck swelling was more frequent in superficial group and sore throat was more frequent in deep group (p < 0.05). Deep neck abscess would show significant differences with regards to the abscess location, location multiplicity, and clinical symptoms according to age. The clinical symptoms observed are dependent on the anatomic location as defined by a superficial or deep abscess.

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

Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The

Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning

DEFF Research Database (Denmark)

Aakerberg, Andreas; Nasrollahi, Kamal; Heder, Thomas

2018-01-01

Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored...... in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments...

Pre-Treatment Deep Curettage Can Significantly Reduce Tumour Thickness in Thick Basal Cell Carcinoma While Maintaining a Favourable Cosmetic Outcome When Used in Combination with Topical Photodynamic Therapy

International Nuclear Information System (INIS)

Christensen, E.; Mork, C.; Foss, O. A.

2011-01-01

Topical photodynamic therapy (PDT) has limitations in the treatment of thick skin tumours. The aim of the study was to evaluate the effect of pre-PDT deep curettage on tumour thickness in thick (≥2 mm) basal cell carcinoma (BCC). Additionally, 3-month treatment outcome and change of tumour thickness from diagnosis to treatment were investigated. At diagnosis, mean tumour thickness was 2.3 mm (range 2.0-4.0). Pre- and post-curettage biopsies were taken from each tumour prior to PDT. Of 32 verified BCCs, tumour thickness was reduced by 50% after deep curettage (ρ≤0.001) . Mean tumour thickness was also reduced from diagnosis to treatment. At 3-month followup, complete tumour response was found in 93% and the cosmetic outcome was rated excellent or good in 100% of cases. In conclusion, deep curettage significantly reduces BCC thickness and may with topical PDT provide a favourable clinical and cosmetic short-term outcome.

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.

Identifying beneficial task relations for multi-task learning in deep neural networks

DEFF Research Database (Denmark)

Bingel, Joachim; Søgaard, Anders

2017-01-01

Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...

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.

Significance in the increase of women psychiatrists in Korea.

Science.gov (United States)

Kim, Ha Kyoung; Kim, Soo In

2008-01-01

The number of female doctors has increased in Korea; 18.9% (13,083) of the total medical doctors registered (69,097) were women in 2006, compared to 13.6% (2,216) in 1975. The proportion of female doctors will jump up by 2010 considering that nearly 40% of the medical students are women as of today. This trend has had strong influence on the field of psychiatry; the percentage of women psychiatrists rose from 1.6 (6)% to 18% (453), from 1975 to 2006 and now women residents comprise 39% (206) of all. This is not only a reflection of a social phenomenon of the increase in professional women but also attributed to some specific characteristics of the psychiatry. Psychiatric practice may come more natural to women. While clinical activities of women psychiatrists are expanding, there are few women leaders and much less women are involving in academic activities in this field as yet. Though there is less sexual discrimination in the field of psychiatry, women psychiatrists are still having a lot of difficulties in balancing work and family matters. Many women psychiatrists also report they've ever felt an implied discrimination in their careers. In this study, we are to identify the characteristics of women psychiatrists and to explore the significance of the increase in women psychiatrists in Korea and the situation in which they are.

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.

Deep Learning and Bayesian Methods

OpenAIRE

Prosper Harrison B.

2017-01-01

A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such meth...

Starvation and recovery in the deep-sea methanotroph Methyloprofundus sedimenti.

Science.gov (United States)

Tavormina, Patricia L; Kellermann, Matthias Y; Antony, Chakkiath Paul; Tocheva, Elitza I; Dalleska, Nathan F; Jensen, Ashley J; Valentine, David L; Hinrichs, Kai-Uwe; Jensen, Grant J; Dubilier, Nicole; Orphan, Victoria J

2017-01-01

In the deep ocean, the conversion of methane into derived carbon and energy drives the establishment of diverse faunal communities. Yet specific biological mechanisms underlying the introduction of methane-derived carbon into the food web remain poorly described, due to a lack of cultured representative deep-sea methanotrophic prokaryotes. Here, the response of the deep-sea aerobic methanotroph Methyloprofundus sedimenti to methane starvation and recovery was characterized. By combining lipid analysis, RNA analysis, and electron cryotomography, it was shown that M. sedimenti undergoes discrete cellular shifts in response to methane starvation, including changes in headgroup-specific fatty acid saturation levels, and reductions in cytoplasmic storage granules. Methane starvation is associated with a significant increase in the abundance of gene transcripts pertinent to methane oxidation. Methane reintroduction to starved cells stimulates a rapid, transient extracellular accumulation of methanol, revealing a way in which methane-derived carbon may be routed to community members. This study provides new understanding of methanotrophic responses to methane starvation and recovery, and lays the initial groundwork to develop Methyloprofundus as a model chemosynthesizing bacterium from the deep sea. © 2016 John Wiley & Sons Ltd.

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...

Significance of deep T-wave inversions in asymptomatic athletes with normal cardiovascular examinations: practical solutions for managing the diagnostic conundrum.

Science.gov (United States)

Wilson, M G; Sharma, S; Carré, F; Charron, P; Richard, P; O'Hanlon, R; Prasad, S K; Heidbuchel, H; Brugada, J; Salah, O; Sheppard, M; George, K P; Whyte, G; Hamilton, B; Chalabi, H

2012-11-01

Preparticipation screening programmes for underlying cardiac pathologies are now commonplace for many international sporting organisations. However, providing medical clearance for an asymptomatic athlete without a family history of sudden cardiac death (SCD) is especially challenging when the athlete demonstrates particularly abnormal repolarisation patterns, highly suggestive of an inherited cardiomyopathy or channelopathy. Deep T-wave inversions of ≥ 2 contiguous anterior or lateral leads (but not aVR, and III) are of major concern for sports cardiologists who advise referring team physicians, as these ECG alterations are a recognised manifestation of hypertrophic cardiomyopathy (HCM) and arrhythmogenic right ventricular cardiomyopathy (ARVC). Subsequently, inverted T-waves may represent the first and only sign of an inherited heart muscle disease, in the absence of any other features and before structural changes in the heart can be detected. However, to date, there remains little evidence that deep T-wave inversions are always pathognomonic of either a cardiomyopathy or an ion channel disorder in an asymptomatic athlete following long-term follow-up. This paper aims to provide a systematic review of the prevalence of T-wave inversion in athletes and examine T-wave inversion and its relationship to structural heart disease, notably HCM and ARVC with a view to identify young athletes at risk of SCD during sport. Finally, the review proposes clinical management pathways (including genetic testing) for asymptomatic athletes demonstrating significant T-wave inversion with structurally normal hearts.

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.

Fracturing Fluid Leak-off for Deep Volcanic Rock in Zhungeer Basin: Mechanism and Control Method

Directory of Open Access Journals (Sweden)

Huang Bo

2017-01-01

Full Text Available The deep volcanic reservoir in Zhungeer Basin is buried in over 4000m depth, which is characterized by complex lithology (breccia, andesite, basalt, etc., high elastic modulus and massive natural fractures. During hydraulic fracturing, hydraulic fracture will propagate and natural fractures will be triggered by the increasing net pressure. However, the extension of fractures, especially natural fractures, would aggravate the leak-off effect of fracturing fluid, and consequently decrease the fracturing success rate. 4 out of 12 fracturing wells in the field have failed to add enough proppants due to fluid loss. In order to increase the success rate and efficiency of hydraulic fracturing for deep volcanic reservoir, based on theoretical and experimental method, the mechanism of fracturing fluid leak-off is deeply studied. We propose a dualistic proppant scheme and employ the fluid loss reducer to control the fluid leak-off in macro-fractures and micro-fractures respectively. The proposed technique remarkably improved the success rate in deep volcanic rock fracturing. It bears important theoretical value and practical significance to improve the hydraulic fracturing design for deep volcanic reservoir.

Recent developments in the thermophilic microbiology of deep-sea hydrothermal vents.

Science.gov (United States)

Miroshnichenko, Margarita L; Bonch-Osmolovskaya, Elizaveta A

2006-04-01

The diversity of thermophilic prokaryotes inhabiting deep-sea hot vents was actively studied over the last two decades. The ever growing interest is reflected in the exponentially increasing number of novel thermophilic genera described. The goal of this paper is to survey the progress in this field made in the years 2000-2005. In this period, representatives of several new taxa of hyperthermophilic archaea were obtained from deep-sea environments. Two of these isolates had phenotypic features new for this group of organisms: the presence of an outer cell membrane (the genus Ignicoccus) and the ability to grow anaerobically with acetate and ferric iron (the genus Geoglobus). Also, our knowledge on the diversity of thermophilic bacteria from deep-sea thermal environments extended significantly. The new bacterial isolates represented diverse bacterial divisions: the phylum Aquificae, the subclass Epsilonproteobacteria, the order Thermotogales, the families Thermodesulfobacteriaceae, Deferribacteraceae, and Thermaceae, and a novel bacterial phylum represented by the genus Caldithrix. Most of these isolates are obligate or facultative lithotrophs, oxidizing molecular hydrogen in the course of different types of anaerobic respiration or microaerobic growth. The existence and significant ecological role of some of new bacterial thermophilic isolates was initially established by molecular methods.

Increased Levels of NF-kB-Dependent Markers in Cancer-Associated Deep Venous Thrombosis.

Science.gov (United States)

Malaponte, Grazia; Signorelli, Salvatore S; Bevelacqua, Valentina; Polesel, Jerry; Taborelli, Martina; Guarneri, Claudio; Fenga, Concettina; Umezawa, Kazou; Libra, Massimo

2015-01-01

Several studies highlight the role of inflammatory markers in thrombosis as well as in cancer. However, their combined role in cancer-associated deep vein thrombosis (DVT) and the molecular mechanisms, involved in its pathophysiology, needs further investigations. In the present study, C-reactive protein, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1β), matrix metalloproteases-9 (MMP-9), vascular endothelial growth factor (VEGF), tissue factor (TF), fibrinogen and soluble P-selectin, were analyzed in plasma and in monocyte samples from 385 cancer patients, of whom 64 were concomitantly affected by DVT (+). All these markers were higher in cancer patients DVT+ than in those DVT-. Accordingly, significantly higher NF-kB activity was observed in cancer patients DVT+ than DVT-. Significant correlation between data obtained in plasma and monocyte samples was observed. NF-kB inhibition was associated with decreased levels of all molecules in both cancer DVT+ and DVT-. To further demonstrate the involvement of NF-kB activation by the above mentioned molecules, we treated monocyte derived from healthy donors with a pool of sera from cancer patients with and without DVT. These set of experiments further suggest the significant role played by some molecules, regulated by NF-kB, and detected in cancer patients with DVT. Our data support the notion that NF-kB may be considered as a therapeutic target for cancer patients, especially those complicated by DVT. Treatment with NF-kB inhibitors may represent a possible strategy to prevent or reduce the risk of DVT in cancer patients.

Microbially-mediated fluorescent organic matter transformations in the deep ocean

DEFF Research Database (Denmark)

Aparicio, Fran L.; Nieto-Cid, Mar; Borrull, Encarna

2015-01-01

The refractory nature of marine dissolved organic matter (DOM) increases while it travels from surface waters to the deep ocean. This resistant fraction is in part composed of fluorescent humic-like material, which is relatively difficult to metabolize by deep water prokaryotes, and it can also b....... These findings contribute to the understanding of FDOM variability in deep waters and provide valuable information for studies where fluorescent compounds are used in order to track water masses and/or microbial processes.......The refractory nature of marine dissolved organic matter (DOM) increases while it travels from surface waters to the deep ocean. This resistant fraction is in part composed of fluorescent humic-like material, which is relatively difficult to metabolize by deep water prokaryotes, and it can also...

The Deep Atmospheric Boundary Layer and Its Significance to the Stratosphere and Troposphere Exchange over the Tibetan Plateau

Science.gov (United States)

Chen, Xuelong; Añel, Juan A.; Su, Zhongbo; de la Torre, Laura; Kelder, Hennie; van Peet, Jacob; Ma, Yaoming

2013-01-01

In this study the depth of the atmospheric boundary layer (ABL) over the Tibetan Plateau was measured during a regional radiosonde observation campaign in 2008 and found to be deeper than indicated by previously measurements. Results indicate that during fair weather conditions on winter days, the top of the mixed layers can be up to 5 km above the ground (9.4 km above sea level). Measurements also show that the depth of the ABL is quite distinct for three different periods (winter, monsoon-onset, and monsoon seasons). Turbulence at the top of a deep mixing layer can rise up to the upper troposphere. As a consequence, as confirmed by trajectory analysis, interaction occurs between deep ABLs and the low tropopause during winter over the Tibetan Plateau. PMID:23451108

Flexor accessorius longus: A rare variation of the deep extrinsic digital flexors of the leg and its phylogenetic significance

Directory of Open Access Journals (Sweden)

Jaijesh P

2006-01-01

Full Text Available Anomalies of the calf muscles are rare. One such anomalous muscle, known as the Muscle Flexor accessorius longus (also named accessorius ad accessorium, accessorius secondus, accessory flexor digitorum longus or pronator pedis is of morphological significance. When present, this originates in the deep fascia of the tibia or fibula and inserts in the foot either into the flexor digitorum accessorius or into the tendons of the flexor digitorum longus. In this report we present a discussion of the morphological significance and phylogenetic history of one such muscle observed. In this case report we describe an anomalous calf muscle which extends from the popliteal region, runs along the posterior compartment of the leg, reaches the sole and is inserted to the flexor digitorum longus muscle. This kind of muscle variations are considered to be the higher origin of the flexor digitorum accessorius muscle of the sole. Here we discuss the phylogenetic history of this muscle as this muscle variant is present in some primitive mammals, absent in apes and in this particular case appeared as one of the muscles of the flexor compartment of the leg.

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

Science.gov (United States)

2018-01-01

Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved. PMID:29439500

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

Directory of Open Access Journals (Sweden)

Yuexiang Li

2018-02-01

Full Text Available Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1, lesion dermoscopic feature extraction (task 2 and lesion classification (task 3. A deep learning framework consisting of two fully convolutional residual networks (FCRN is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network.

Science.gov (United States)

Li, Yuexiang; Shen, Linlin

2018-02-11

Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

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.

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.

THE SMALL BUT SIGNIFICANT AND NONTRANSITORY INCREASE IN PRICES (SSNIP TEST

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Liviana Niminet

2008-12-01

Full Text Available The Small but Significant Nontransitory Increase in Price Test was designed to define the relevant market by concepts of product, geographical area and time. This test, also called the ,,hypothetical monopolistic test” is the subject of many researches both economical and legal as it deals with economic concepts as well as with legally aspects.

Deep learning guided stroke management: a review of clinical applications.

Science.gov (United States)

Feng, Rui; Badgeley, Marcus; Mocco, J; Oermann, Eric K

2018-04-01

Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess potential benefit by identifying areas of ischemia that have not yet experienced cellular death. The perfusion-diffusion mismatch, is used as a simple metric for potential benefit with timely intervention, yet penumbral patterns provide an inaccurate predictor of clinical outcome. Machine learning research in the form of deep learning (artificial intelligence) techniques using deep neural networks (DNNs) excel at working with complex inputs. The key areas where deep learning may be imminently applied to stroke management are image segmentation, automated featurization (radiomics), and multimodal prognostication. The application of convolutional neural networks, the family of DNN architectures designed to work with images, to stroke imaging data is a perfect match between a mature deep learning technique and a data type that is naturally suited to benefit from deep learning's strengths. These powerful tools have opened up exciting opportunities for data-driven stroke management for acute intervention and for guiding prognosis. Deep learning techniques are useful for the speed and power of results they can deliver and will become an increasingly standard tool in the modern stroke specialist's arsenal for delivering personalized medicine to patients with ischemic stroke. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Increased frequency of retinopathy of prematurity over the last decade and significant regional differences.

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Holmström, Gerd; Tornqvist, Kristina; Al-Hawasi, Abbas; Nilsson, Åsa; Wallin, Agneta; Hellström, Ann

2018-03-01

Retinopathy of prematurity (ROP) causes childhood blindness globally in prematurely born infants. Although increased levels of oxygen supply lead to increased survival and reduced frequency of cerebral palsy, increased incidence of ROP is reported. With the help of a Swedish register for ROP, SWEDROP, national and regional incidences of ROP and frequencies of treatment were evaluated from 2008 to 2015 (n = 5734), as well as before and after targets of provided oxygen changed from 85-89% to 91-95% in 2014. Retinopathy of prematurity (ROP) was found in 31.9% (1829/5734) of all infants with a gestational age (GA) of <31 weeks at birth and 5.7% of the infants (329/5734) had been treated for ROP. Analyses of the national data revealed an increased incidence of ROP during the 8-year study period (p = 0.003), but there was no significant increase in the frequency of treatment. There were significant differences between the seven health regions of Sweden, regarding both incidence of ROP and frequency of treatment (p < 0.001). Comparison of regional data before and after the new oxygen targets revealed a significant increase in treated ROP in one region [OR: 2.24 (CI: 1.11-4.49), p = 0.024] and a borderline increase in one other [OR: 3.08 (CI: 0.99-9.60), p = 0.052]. The Swedish national ROP register revealed an increased incidence of ROP during an 8-year period and significant regional differences regarding the incidence of ROP and frequency of treatment. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

A novel approach reveals high zooplankton standing stock deep in the sea

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A. Vereshchaka

2016-11-01

Full Text Available In a changing ocean there is a critical need to understand global biogeochemical cycling, particularly regarding carbon. We have made strides in understanding upper ocean dynamics, but the deep ocean interior (> 1000 m is still largely unknown, despite representing the overwhelming majority of Earth's biosphere. Here we present a method for estimating deep-pelagic zooplankton biomass on an ocean-basin scale. We have made several new discoveries about the Atlantic, which likely apply to the world ocean. First, multivariate analysis showed that depth and Chl were the basic factors affecting the wet biomass of the main plankton groups. Wet biomass of all major groups was significantly correlated with Chl. Second, zooplankton biomass in the upper bathypelagic domain is higher than expected. Third, the majority of this biomass comprises macroplanktonic shrimps, which have been historically underestimated. These findings, coupled with recent findings of increased global deep-pelagic fish biomass, suggest that the contribution of the deep-ocean pelagic fauna for biogeochemical cycles may be more important than previously thought.

Effect of swift heavy ion irradiation on deep levels in Au /n-Si (100) Schottky diode studied by deep level transient spectroscopy

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Kumar, Sandeep; Katharria, Y. S.; Kumar, Sugam; Kanjilal, D.

2007-12-01

In situ deep level transient spectroscopy has been applied to investigate the influence of 100MeV Si7+ ion irradiation on the deep levels present in Au/n-Si (100) Schottky structure in a wide fluence range from 5×109to1×1012ions cm-2. The swift heavy ion irradiation introduces a deep level at Ec-0.32eV. It is found that initially, trap level concentration of the energy level at Ec-0.40eV increases with irradiation up to a fluence value of 1×1010cm-2 while the deep level concentration decreases as irradiation fluence increases beyond the fluence value of 5×1010cm-2. These results are discussed, taking into account the role of energy transfer mechanism of high energy ions in material.

Key technologies for well drilling and completion in ultra-deep sour gas reservoirs, Yuanba Gasfield, Sichuan Basin

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Jiaxiang Xia

2016-12-01

Full Text Available The Yuanba Gasfield is a large gas field discovered by Sinopec in the Sichuan Basin in recent years, and another main exploration area for natural gas reserves and production increase after the Puguang Gasfield. The ultra-deep sour gas reservoir in the Yuanba Gasfield is characterized by complicated geologic structure, deep reservoirs and complex drilled formation, especially in the continental deep strata which are highly abrasive with low ROP (rate of penetration and long drilling period. After many years of drilling practice and technical research, the following six key drilling and completion technologies for this type reservoir are established by introducing new tools and technologies, developing specialized drill bits and optimizing drilling design. They are: casing program optimization technology for ROP increasing and safe well completion; gas drilling technology for shallow continental strata and high-efficiency drilling technology for deep high-abrasion continental strata; drilling fluid support technologies of gas–liquid conversion, ultra-deep highly-deviated wells and horizontal-well lubrication and drag reduction, hole stability control and sour gas contamination prevention; well cementing technologies for gas medium, deep-well long cementing intervals and ultra-high pressure small space; horizontal-well trajectory control technologies for measuring instrument, downhole motor optimization and bottom hole assembly design; and liner completion modes and completion string optimization technologies suitable for this gas reservoir. Field application shows that these key technologies are contributive to ROP increase and efficiency improvement of 7000 m deep horizontal wells and to significant operational cycle shortening.

Deep-water oilfield development cost analysis and forecasting —— Take gulf of mexico for example

Science.gov (United States)

Shi, Mingyu; Wang, Jianjun; Yi, Chenggao; Bai, Jianhui; Wang, Jing

2017-11-01

Gulf of Mexico (GoM) is the earliest offshore oilfield which has ever been developed. It tends to breed increasingly value of efficient, secure and cheap key technology of deep-water development. Thus, the analyze of development expenditure in this area is significantly important the evaluation concept of deep-water oilfield all over the world. This article emphasizes on deep-water development concept and EPC contract value in GoM in recent 10 years in case of comparison and selection to the economic efficiency. Besides, the QUETOR has been put into use in this research processes the largest upstream cost database to simulate and calculate the calculating examples’ expenditure. By analyzing and forecasting the deep-water oilfield development expenditure, this article explores the relevance between expenditure index and oil price.

Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks

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Ubbens, Jordan R.; Stavness, Ian

2017-01-01

Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana. PMID:28736569

Deep Learning Microscopy

KAUST Repository

Rivenson, Yair

2017-05-12

We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with remarkably better resolution, matching the performance of higher numerical aperture lenses, also significantly surpassing their limited field-of-view and depth-of-field. These results are transformative for various fields that use microscopy tools, including e.g., life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, our presented approach is broadly applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better and better as they continue to image specimen and establish new transformations among different modes of imaging.

Deep cerebral microbleeds are negatively associated with HDL-C in elderly first-time ischemic stroke patients.

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Igase, Michiya; Kohara, Katsuhiko; Igase, Keiji; Yamashita, Shiro; Fujisawa, Mutsuo; Katagi, Ryosuke; Miki, Tetsuro

2013-02-15

Cerebral microbleeds (CMBs) detected on T2*-weighted MRI gradient-echo have been associated with increased risk of cerebral infarction. We evaluated risk factors for these lesions in a cohort of first-time ischemic stroke patients. Presence of CMBs in consecutive first-time ischemic stroke patients was evaluated. The location of CMBs was classified by cerebral region as strictly lobar (lobar CMBs) and deep or infratentorial (deep CMBs). Logistic regression analysis was performed to determine the contribution of lipid profile to the presence of CMBs. One hundred and sixteen patients with a mean age of 70±10years were recruited. CMBs were present in 74 patients. The deep CMBs group had significantly lower HDL-C levels than those without CMBs. In univariable analysis, advanced periventricular hyperintensity grade (PVH>2) and decreased HDL-C were significantly associated with the deep but not the lobar CMB group. On logistic regression analysis, HDL-C (beta=-0.06, p=0.002) and PVH grade >2 (beta=3.40, p=0.005) were independent determinants of deep CMBs. Low HDL-C may be a risk factor of deep CMBs, including advanced PVH status, in elderly patients with acute ischemic stroke. Management of HDL-C levels might be a therapeutic target for the prevention of recurrence of stroke. Copyright © 2012 Elsevier B.V. All rights reserved.

Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

Science.gov (United States)

Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

2016-11-01

Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features. Copyright © 2016. Published by Elsevier Inc.

Evaluation of Significance of Diffusely Increased Bilateral Renal Uptake on Bone Scan

International Nuclear Information System (INIS)

Sung, Mi Sook; Yang, Woo Jin; Byun, Jae Young; Park, Jung Mi; Shinn, Kyung Sub; Bahk, Yong Whee

1990-01-01

Unexpected renal abnormality can be detected on bone scan using 99m Tc-MDP. The purpose of the study is to evaluate the diagnostic significance of diffusely increased bilateral renal uptake on bone scan. 1,500 bone scan were reviewed and 43 scans which showed diffusely increased bilateral renal uptake were selected for analysis. Laboratory findings for renal and liver function tests including routine urinalysis were reviewed in 43 patients. 26 of 43 case showed abnormality in urinalysis and renal function study. 20 of 43 cases showed abnormal liver function study and 3 of these cases were diagnosed as hepatorenal syndrome later. 13 of those 20 cases had liver cirrhosis with or without hepatoma. 12 of 43 cases showed abnormality both in renal and liver function studies. 2 of 43 cases showed diffusely increased bilateral renal uptake after chemotherapy for cancer but not on previous scans before chemotherapy. 2 of 43 cases showed hypercalcaemia and 8 of 43 cases had multifocal bone uptake due to metastasis or benign bone lesion. But the latter showed no hypercalcaemia at all. There was no significant correlation between increased renal uptake and MDP uptake in soft tissue other than kidneys. This study raised the possibility that the impaired liver and/or renal function may result in diffuse increase of bilateral renal uptake of MDP of unknown mechanism. It seems to need further study on this correlation.

Evaluation of Significance of Diffusely Increased Bilateral Renal Uptake on Bone Scan

Energy Technology Data Exchange (ETDEWEB)

Sung, Mi Sook; Yang, Woo Jin; Byun, Jae Young; Park, Jung Mi; Shinn, Kyung Sub; Bahk, Yong Whee [Catholic University College of Medicine, Seoul (Korea, Republic of)

1990-03-15

Unexpected renal abnormality can be detected on bone scan using {sup 99m}Tc-MDP. The purpose of the study is to evaluate the diagnostic significance of diffusely increased bilateral renal uptake on bone scan. 1,500 bone scan were reviewed and 43 scans which showed diffusely increased bilateral renal uptake were selected for analysis. Laboratory findings for renal and liver function tests including routine urinalysis were reviewed in 43 patients. 26 of 43 case showed abnormality in urinalysis and renal function study. 20 of 43 cases showed abnormal liver function study and 3 of these cases were diagnosed as hepatorenal syndrome later. 13 of those 20 cases had liver cirrhosis with or without hepatoma. 12 of 43 cases showed abnormality both in renal and liver function studies. 2 of 43 cases showed diffusely increased bilateral renal uptake after chemotherapy for cancer but not on previous scans before chemotherapy. 2 of 43 cases showed hypercalcaemia and 8 of 43 cases had multifocal bone uptake due to metastasis or benign bone lesion. But the latter showed no hypercalcaemia at all. There was no significant correlation between increased renal uptake and MDP uptake in soft tissue other than kidneys. This study raised the possibility that the impaired liver and/or renal function may result in diffuse increase of bilateral renal uptake of MDP of unknown mechanism. It seems to need further study on this correlation.

Significance of the identification in the Horn of Africa of an exceptionally deep branching Mycobacterium tuberculosis clade.

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Yann Blouin

Full Text Available Molecular and phylogeographic studies have led to the definition within the Mycobacterium tuberculosis complex (MTBC of a number of geotypes and ecotypes showing a preferential geographic location or host preference. The MTBC is thought to have emerged in Africa, most likely the Horn of Africa, and to have spread worldwide with human migrations. Under this assumption, there is a possibility that unknown deep branching lineages are present in this region. We genotyped by spoligotyping and multiple locus variable number of tandem repeats (VNTR analysis (MLVA 435 MTBC isolates recovered from patients. Four hundred and eleven isolates were collected in the Republic of Djibouti over a 12 year period, with the other 24 isolates originating from neighbouring countries. All major M. tuberculosis lineages were identified, with only two M. africanum and one M. bovis isolates. Upon comparison with typing data of worldwide origin we observed that several isolates showed clustering characteristics compatible with new deep branching. Whole genome sequencing (WGS of seven isolates and comparison with available WGS data from 38 genomes distributed in the different lineages confirms the identification of ancestral nodes for several clades and most importantly of one new lineage, here referred to as lineage 7. Investigation of specific deletions confirms the novelty of this lineage, and analysis of its precise phylogenetic position indicates that the other three superlineages constituting the MTBC emerged independently but within a relatively short timeframe from the Horn of Africa. The availability of such strains compared to the predominant lineages and sharing very ancient ancestry will open new avenues for identifying some of the genetic factors responsible for the success of the modern lineages. Additional deep branching lineages may be readily and efficiently identified by large-scale MLVA screening of isolates from sub-Saharan African countries followed by

St. John's wort significantly increased the systemic exposure and toxicity of methotrexate in rats

International Nuclear Information System (INIS)

Yang, Shih-Ying; Juang, Shin-Hun; Tsai, Shang-Yuan; Chao, Pei-Dawn Lee; Hou, Yu-Chi

2012-01-01

St. John's wort (SJW, Hypericum perforatum) is one of the popular nutraceuticals for treating depression. Methotrexate (MTX) is an immunosuppressant with narrow therapeutic window. This study investigated the effect of SJW on MTX pharmacokinetics in rats. Rats were orally given MTX alone and coadministered with 300 and 150 mg/kg of SJW, and 25 mg/kg of diclofenac, respectively. Blood was withdrawn at specific time points and serum MTX concentrations were assayed by a specific monoclonal fluorescence polarization immunoassay method. The results showed that 300 mg/kg of SJW significantly increased the AUC 0−t and C max of MTX by 163% and 60%, respectively, and 150 mg/kg of SJW significantly increased the AUC 0−t of MTX by 55%. In addition, diclofenac enhanced the C max of MTX by 110%. The mortality of rats treated with SJW was higher than that of controls. In conclusion, coadministration of SJW significantly increased the systemic exposure and toxicity of MTX. The combined use of MTX with SJW would need to be with caution. -- Highlights: ► St. John's wort significantly increased the AUC 0−t and C max of methotrexate. ► Coadministration of St. John's wort increased the exposure and toxicity of methotrexate. ► The combined use of methotrexate with St. John's wort will need to be with caution.

Deep-water subsea lifting operations

Energy Technology Data Exchange (ETDEWEB)

Nestegaard, Arne; Boee, Tormod

2010-07-01

Significant costs are related to marine operations in the installation phase of deep water subsea field developments. In order to establish safe operational criteria and procedures for the installation, detailed planning is necessary, including numerical modelling and analysis of the environmental conditions and hydrodynamic loads on the installed object as well as the installation equipment. This paper presents recommendations for modelling and analysis of deep water subsea lifting operations developed for the new DNV RP-H103 [1]. During installation of subsea structures, the highest dynamic forces are most often encountered in the splash zone. Recommendations for estimation of maximum forces will be presented. For small structures and tools, installation through the moon pool of a small installation vessel is often preferred. Calculation methods for loading on structures installed through a moon pool will be presented. During intervention or installation in deep water a significant amplification of amplitude and forces can be experienced when the frequency range of vertical crane tip motion coincides with the natural vertical oscillation of the lift wire and load. Vertical resonance may reduce the operability of the operation. Simplified calculation methods for such operations are presented. (Author)

Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

Science.gov (United States)

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Objective. Brain–computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. Approach. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. Main results. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. Significance. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Deep Learning for Computer Vision: A Brief Review

Science.gov (United States)

Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. PMID:29487619

Deep Learning for Computer Vision: A Brief Review

Directory of Open Access Journals (Sweden)

Athanasios Voulodimos

2018-01-01

Full Text Available Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

Deep Learning for Computer Vision: A Brief Review.

Science.gov (United States)

Voulodimos, Athanasios; Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

Gene expression in the deep biosphere.

Science.gov (United States)

Orsi, William D; Edgcomb, Virginia P; Christman, Glenn D; Biddle, Jennifer F

2013-07-11

Scientific ocean drilling has revealed a deep biosphere of widespread microbial life in sub-seafloor sediment. Microbial metabolism in the marine subsurface probably has an important role in global biogeochemical cycles, but deep biosphere activities are not well understood. Here we describe and analyse the first sub-seafloor metatranscriptomes from anaerobic Peru Margin sediment up to 159 metres below the sea floor, represented by over 1 billion complementary DNA (cDNA) sequence reads. Anaerobic metabolism of amino acids, carbohydrates and lipids seem to be the dominant metabolic processes, and profiles of dissimilatory sulfite reductase (dsr) transcripts are consistent with pore-water sulphate concentration profiles. Moreover, transcripts involved in cell division increase as a function of microbial cell concentration, indicating that increases in sub-seafloor microbial abundance are a function of cell division across all three domains of life. These data support calculations and models of sub-seafloor microbial metabolism and represent the first holistic picture of deep biosphere activities.

The influence of tourniquet use and operative time on the incidence of deep vein thrombosis in total knee arthroplasty.

Science.gov (United States)

Hernandez, Arnaldo José; Almeida, Adriano Marques de; Fávaro, Edmar; Sguizzato, Guilherme Turola

2012-09-01

To evaluate the association between tourniquet and total operative time during total knee arthroplasty and the occurrence of deep vein thrombosis. Seventy-eight consecutive patients from our institution underwent cemented total knee arthroplasty for degenerative knee disorders. The pneumatic tourniquet time and total operative time were recorded in minutes. Four categories were established for total tourniquet time: 120 minutes. Three categories were defined for operative time: 150 minutes. Between 7 and 12 days after surgery, the patients underwent ascending venography to evaluate the presence of distal or proximal deep vein thrombosis. We evaluated the association between the tourniquet time and total operative time and the occurrence of deep vein thrombosis after total knee arthroplasty. In total, 33 cases (42.3%) were positive for deep vein thrombosis; 13 (16.7%) cases involved the proximal type. We found no statistically significant difference in tourniquet time or operative time between patients with or without deep vein thrombosis. We did observe a higher frequency of proximal deep vein thrombosis in patients who underwent surgery lasting longer than 120 minutes. The mean total operative time was also higher in patients with proximal deep vein thrombosis. The tourniquet time did not significantly differ in these patients. We concluded that surgery lasting longer than 120 minutes increases the risk of proximal deep vein thrombosis.

Auditory processing during deep propofol sedation and recovery from unconsciousness.

Science.gov (United States)

Koelsch, Stefan; Heinke, Wolfgang; Sammler, Daniela; Olthoff, Derk

2006-08-01

Using evoked potentials, this study investigated effects of deep propofol sedation, and effects of recovery from unconsciousness, on the processing of auditory information with stimuli suited to elicit a physical MMN, and a (music-syntactic) ERAN. Levels of sedation were assessed using the Bispectral Index (BIS) and the Modified Observer's Assessment of Alertness and Sedation Scale (MOAAS). EEG-measurements were performed during wakefulness, deep propofol sedation (MOAAS 2-3, mean BIS=68), and a recovery period. Between deep sedation and recovery period, the infusion rate of propofol was increased to achieve unconsciousness (MOAAS 0-1, mean BIS=35); EEG measurements of recovery period were performed after subjects regained consciousness. During deep sedation, the physical MMN was markedly reduced, but still significant. No ERAN was observed in this level. A clear P3a was elicited during deep sedation by those deviants, which were task-relevant during the awake state. As soon as subjects regained consciousness during the recovery period, a normal MMN was elicited. By contrast, the P3a was absent in the recovery period, and the P3b was markedly reduced. Results indicate that the auditory sensory memory (as indexed by the physical MMN) is still active, although strongly reduced, during deep sedation (MOAAS 2-3). The presence of the P3a indicates that attention-related processes are still operating during this level. Processes of syntactic analysis appear to be abolished during deep sedation. After propofol-induced anesthesia, the auditory sensory memory appears to operate normal as soon as subjects regain consciousness, whereas the attention-related processes indexed by P3a and P3b are markedly impaired. Results inform about effects of sedative drugs on auditory and attention-related mechanisms. The findings are important because these mechanisms are prerequisites for auditory awareness, auditory learning and memory, as well as language perception during anesthesia.

Impact of deep-sea fishery for Greenland halibut (Reinhardtius hippoglossoides) on non-commercial fish species off West Greenland

DEFF Research Database (Denmark)

Jørgensen, Ole A; Bastardie, Francois; Eigaard, Ole Ritzau

2014-01-01

Since the late 1980s, a deep-sea fishery for Greenland halibut (Reinhardtius hippoglossoides) has been developing gradually in West Greenland. Deep-sea fish species are generally long-lived and characterized by late age of maturity, low fecundity, and slow growth, features that probably cause low....... During the period 1988–2011, population abundance and size composition changed as catch and effort in the Greenland halibut fishery increased. Two species showed a significant decrease in abundance, and four populations showed a significant reduction in mean weight of individuals (p , 0.05). Correlation...... analyses show that most of the observed trends in abundance are probably not related to increasing fishing effort for Greenland halibut. The analysis did, however, show that most of the observed decreases in mean weight were significantly correlated with fishing effort during the 24-year period...

Changes of Major Antioxidant Compounds and Radical Scavenging Activity of Palm Oil and Rice Bran Oil during Deep-Frying

Directory of Open Access Journals (Sweden)

Azizah Abdul Hamid

2014-07-01

Full Text Available Changes in antioxidant properties and degradation of bioactives in palm oil (PO and rice bran oil (RBO during deep-frying were investigated. The alpha (α-tocopherol, gamma (γ-tocotrienol and γ-oryzanol contents of the deep-fried oils were monitored using high performance liquid chromatography, and antioxidant activity was determined using 2-diphenyl-1-picryl hydrazyl (DPPH radical scavenging activity. Results revealed that the antioxidant activity of PO decreased significantly (p < 0.05, while that of RBO was preserved after deep-frying of fries. As expected, the concentration of α-tocopherol in PO and γ-tocotrienol in both PO and RBO decreased significantly (p < 0.05 with increased frying. Results also showed that γ-tocotrienol was found to be more susceptible to degradation compared to that of α-tocopherol in both PO and RBO. Interestingly, no significant degradation of α-tocopherol was observed in RBO. It is suggested that the presence of γ-oryzanol and γ-tocotrienol in RBO may have a protective effect on α-tocopherol during deep-frying.

Deep Energy Retrofit

DEFF Research Database (Denmark)

Zhivov, Alexander; Lohse, Rüdiger; Rose, Jørgen

Deep Energy Retrofit – A Guide to Achieving Significant Energy User Reduction with Major Renovation Projects contains recommendations for characteristics of some of core technologies and measures that are based on studies conducted by national teams associated with the International Energy Agency...... Energy Conservation in Buildings and Communities Program (IEA-EBC) Annex 61 (Lohse et al. 2016, Case, et al. 2016, Rose et al. 2016, Yao, et al. 2016, Dake 2014, Stankevica et al. 2016, Kiatreungwattana 2014). Results of these studies provided a base for setting minimum requirements to the building...... envelope-related technologies to make Deep Energy Retrofit feasible and, in many situations, cost effective. Use of energy efficiency measures (EEMs) in addition to core technologies bundle and high-efficiency appliances will foster further energy use reduction. This Guide also provides best practice...

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...

An Ensemble of Deep Support Vector Machines for Image Categorization

NARCIS (Netherlands)

Abdullah, Azizi; Veltkamp, Remco C.; Wiering, Marco

2009-01-01

This paper presents the deep support vector machine (D-SVM) inspired by the increasing popularity of deep belief networks for image recognition. Our deep SVM trains an SVM in the standard way and then uses the kernel activations of support vectors as inputs for training another SVM at the next

Indoor air quality in a restaurant kitchen using margarine for deep-frying.

Science.gov (United States)

Sofuoglu, Sait C; Toprak, Melis; Inal, Fikret; Cimrin, Arif H

2015-10-01

Indoor air quality has a great impact on human health. Cooking, in particular frying, is one of the most important sources of indoor air pollution. Indoor air CO, CO2, particulate matter (PM), and volatile organic compound (VOC) concentrations, including aldehydes, were measured in the kitchen of a small establishment where a special deep-frying margarine was used. The objective was to assess occupational exposure concentrations for cooks of such restaurants. While individual VOC and PM2.5 concentrations were measured before, during, and after frying events using active sampling, TVOC, PM10, CO, CO2, temperature, and relative humidity were continuously monitored through the whole period. VOC and aldehyde concentrations did not increase to considerable levels with deep-frying compared to the background and public indoor environment levels, whereas PM10 increased significantly (1.85 to 6.6 folds). The average PM2.5 concentration of the whole period ranged between 76 and 249 μg/m(3). Hence, considerable PM exposures could occur during deep-frying with the special margarine, which might be sufficiently high to cause health effects on cooks considering their chronic occupational exposures.

Applications of Deep Learning in Biomedicine.

Science.gov (United States)

Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

2016-05-02

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

Short-term Memory of Deep RNN

OpenAIRE

Gallicchio, Claudio

2018-01-01

The extension of deep learning towards temporal data processing is gaining an increasing research interest. In this paper we investigate the properties of state dynamics developed in successive levels of deep recurrent neural networks (RNNs) in terms of short-term memory abilities. Our results reveal interesting insights that shed light on the nature of layering as a factor of RNN design. Noticeably, higher layers in a hierarchically organized RNN architecture results to be inherently biased ...

Biodiversity loss from deep-sea mining

OpenAIRE

C. L. Van Dover; J. A. Ardron; E. Escobar; M. Gianni; K. M. Gjerde; A. Jaeckel; D. O. B. Jones; L. A. Levin; H. Niner; L. Pendleton; C. R. Smith; T. Thiele; P. J. Turner; L. Watling; P. P. E. Weaver

2017-01-01

The emerging deep-sea mining industry is seen by some to be an engine for economic development in the maritime sector. The International Seabed Authority (ISA) – the body that regulates mining activities on the seabed beyond national jurisdiction – must also protect the marine environment from harmful effects that arise from mining. The ISA is currently drafting a regulatory framework for deep-sea mining that includes measures for environmental protection. Responsible mining increasingly stri...

Impact on demersal fish of a large-scale and deep sand extraction site with ecosystem-based landscaped sandbars

Science.gov (United States)

de Jong, Maarten F.; Baptist, Martin J.; van Hal, Ralf; de Boois, Ingeborg J.; Lindeboom, Han J.; Hoekstra, Piet

2014-06-01

For the seaward harbour extension of the Port of Rotterdam in the Netherlands, approximately 220 million m3 sand was extracted between 2009 and 2013. In order to decrease the surface area of direct impact, the authorities permitted deep sand extraction, down to 20 m below the seabed. Biological and physical impacts of large-scale and deep sand extraction are still being investigated and largely unknown. For this reason, we investigated the colonization of demersal fish in a deep sand extraction site. Two sandbars were artificially created by selective dredging, copying naturally occurring meso-scale bedforms to increase habitat heterogeneity and increasing post-dredging benthic and demersal fish species richness and biomass. Significant differences in demersal fish species assemblages in the sand extraction site were associated with variables such as water depth, median grain size, fraction of very fine sand, biomass of white furrow shell (Abra alba) and time after the cessation of sand extraction. Large quantities of undigested crushed white furrow shell fragments were found in all stomachs and intestines of plaice (Pleuronectes platessa), indicating that it is an important prey item. One and two years after cessation, a significant 20-fold increase in demersal fish biomass was observed in deep parts of the extraction site. In the troughs of a landscaped sandbar however, a significant drop in biomass down to reference levels and a significant change in species assemblage was observed two years after cessation. The fish assemblage at the crests of the sandbars differed significantly from the troughs with tub gurnard (Chelidonichthys lucerna) being a Dufrêne-Legendre indicator species of the crests. This is a first indication of the applicability of landscaping techniques to induce heterogeneity of the seabed although it remains difficult to draw a strong conclusion due the lack of replication in the experiment. A new ecological equilibrium is not reached after 2

Computed tomography in deep venous thrombosis with limb oedema

International Nuclear Information System (INIS)

Seem, E.; Stranden, E.; Stiris, M.G.; Aker Sykehus, Oslo

1985-01-01

Computed tomography was used in 12 patients to investigate the distribution of oedema in the soft tissue compartments of lower limbs with deep venous thrombosis. Oedema was evenly distributed throughout the subcutis and the muscular compartments in tomograms obtained 25 cm proximal to the ankle. Significantly less swelling in the muscular compartments was found 10 cm proximal to the ankle. Interstitial fluid hydrostatic pressure was measured in the subcutis, and in anterior and posterior muscular compartments, and was significantly increased in all cases. Except for one case, the recorded pressures were well below 30 mmHg, which is considered the limit above which compartment syndromes occur. Tissue compliance was significantly lower in muscular compartments than in the subcutis. (orig.)

Effects of shallow and deep endotracheal tube suctioning on cardiovascular indices in patients in intensive care units.

Science.gov (United States)

Irajpour, Alireza; Abbasinia, Mohammad; Hoseini, Abbas; Kashefi, Parviz

2014-07-01

Clearing the endotracheal tube through suctioning should be done to promote oxygenation. Depth of suctioning is one of the variables in this regard. In shallow suctioning method, the catheter passes to the tip of the endotracheal tube, and in deep suctioning method, it passes beyond the tip into the trachea or brunches. This study aimed to evaluate the effect of shallow and deep suctioning methods on cardiovascular indices in patients hospitalized in the intensive care units (ICUs). In this clinical trial, 74 patients were selected among those who had undergone mechanical ventilation in the ICU of Al-Zahra Hospital, Isfahan, Iran using convenience sampling method. The subjects were randomly allocated to shallow and deep suctioning groups. Heart rate (HR) and blood pressure (BP) were measured immediately before and 1, 2, and 3 min after each suctioning. Number of times of suctioning was also noted in both the groups. Data were analyzed using repeated measures analysis of variance (ANOVA), Chi-square and independent t-tests. HR and BP were significantly increased after suctioning in both the groups (P 0.05). The suctioning count was significantly higher in the shallow suctioning group than in the deep suctioning group. Shallow and deep suctioning were similar in their effects on HR and BP, but shallow suctioning caused further manipulation of patient's trachea than deep suctioning method. Therefore, in order to prevent complications, nurses are recommended to perform the endotracheal tube suctioning by the deep method.

Climate, carbon cycling, and deep-ocean ecosystems.

Science.gov (United States)

Smith, K L; Ruhl, H A; Bett, B J; Billett, D S M; Lampitt, R S; Kaufmann, R S

2009-11-17

Climate variation affects surface ocean processes and the production of organic carbon, which ultimately comprises the primary food supply to the deep-sea ecosystems that occupy approximately 60% of the Earth's surface. Warming trends in atmospheric and upper ocean temperatures, attributed to anthropogenic influence, have occurred over the past four decades. Changes in upper ocean temperature influence stratification and can affect the availability of nutrients for phytoplankton production. Global warming has been predicted to intensify stratification and reduce vertical mixing. Research also suggests that such reduced mixing will enhance variability in primary production and carbon export flux to the deep sea. The dependence of deep-sea communities on surface water production has raised important questions about how climate change will affect carbon cycling and deep-ocean ecosystem function. Recently, unprecedented time-series studies conducted over the past two decades in the North Pacific and the North Atlantic at >4,000-m depth have revealed unexpectedly large changes in deep-ocean ecosystems significantly correlated to climate-driven changes in the surface ocean that can impact the global carbon cycle. Climate-driven variation affects oceanic communities from surface waters to the much-overlooked deep sea and will have impacts on the global carbon cycle. Data from these two widely separated areas of the deep ocean provide compelling evidence that changes in climate can readily influence deep-sea processes. However, the limited geographic coverage of these existing time-series studies stresses the importance of developing a more global effort to monitor deep-sea ecosystems under modern conditions of rapidly changing climate.

Differential changes in production measures for an estuarine-resident sparid in deep and shallow waters following increases in hypoxia

Science.gov (United States)

Cottingham, Alan; Hall, Norman G.; Hesp, S. Alex; Potter, Ian C.

2018-03-01

This study determined how productivity measures for a fish species in different water depths of an estuary changed in response to the increase in hypoxia in deep waters, which had previously been shown to occur between 1993-95 and 2007-11. Annual data on length and age compositions, body mass, growth, abundance, biomass, production and production to biomass ratio (P/B) were thus determined for the estuarine-resident Acanthopagrus butcheri in nearshore shallow (compositions imply that the increase in hypoxia was accompanied by the distribution of the majority of the older and larger A. butcheri changing from deep to shallow waters, where the small fish typically reside. Annual densities, biomass and production in shallow waters of fish m-2, 2-4 g m-2 and ∼2 g m-2 y-1 in the earlier period were far lower than the 0.1-0.2 fish m-2, 8-15 g m-2 and 5-10 g m-2 y-1 in the later period, whereas the reverse trend occurred in deep waters, with values of 6-9 fish net-1, 2000-3900 g net-1, 900-1700 g net-1 y-1 in the earlier period vs fish net-1, ∼110 g net-1 and 27-45 g net-1 y-1 in the later period. Within the later period, and in contrast to the trends with annual abundance and biomass, the production in shallow waters was least during 2008/09, rather than greatest, reflecting the slow growth in that particularly cool year. The presence of substantial aggregations of both small and large fish in shallow waters accounts for the abundance, biomass and production in those waters increasing between those periods and thus, through a density-dependent effect, provide a basis for the overall reduction in growth. In marked contrast to the trends with the other three production measures, annual production to biomass ratios (P/B) in shallow waters in the two years in the earlier period, and in three of the four years of the later period, fell within the same range, i.e. 0.6-0.9 y-1, but was only 0.2 y-1 in 2008/09, reflecting the poor growth in that year. This emphasises the need

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'

An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

Science.gov (United States)

Shamwell, E Jared; Nothwang, William D; Perlis, Donald

2018-05-04

Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

Vision in the deep sea.

Science.gov (United States)

Warrant, Eric J; Locket, N Adam

2004-08-01

The deep sea is the largest habitat on earth. Its three great faunal environments--the twilight mesopelagic zone, the dark bathypelagic zone and the vast flat expanses of the benthic habitat--are home to a rich fauna of vertebrates and invertebrates. In the mesopelagic zone (150-1000 m), the down-welling daylight creates an extended scene that becomes increasingly dimmer and bluer with depth. The available daylight also originates increasingly from vertically above, and bioluminescent point-source flashes, well contrasted against the dim background daylight, become increasingly visible. In the bathypelagic zone below 1000 m no daylight remains, and the scene becomes entirely dominated by point-like bioluminescence. This changing nature of visual scenes with depth--from extended source to point source--has had a profound effect on the designs of deep-sea eyes, both optically and neurally, a fact that until recently was not fully appreciated. Recent measurements of the sensitivity and spatial resolution of deep-sea eyes--particularly from the camera eyes of fishes and cephalopods and the compound eyes of crustaceans--reveal that ocular designs are well matched to the nature of the visual scene at any given depth. This match between eye design and visual scene is the subject of this review. The greatest variation in eye design is found in the mesopelagic zone, where dim down-welling daylight and bio-luminescent point sources may be visible simultaneously. Some mesopelagic eyes rely on spatial and temporal summation to increase sensitivity to a dim extended scene, while others sacrifice this sensitivity to localise pinpoints of bright bioluminescence. Yet other eyes have retinal regions separately specialised for each type of light. In the bathypelagic zone, eyes generally get smaller and therefore less sensitive to point sources with increasing depth. In fishes, this insensitivity, combined with surprisingly high spatial resolution, is very well adapted to the

Storage tests on irradiated deep-frozen chickens

International Nuclear Information System (INIS)

Gruenewald, T.

1975-01-01

Salmonellae infections in deep-frozen roasting chicken can be dealt with by ionising radiation as this process involves hardly any heating of the product. Deep-frozen chickens irradiated with doses up to 800 krad were stored at -30 0 C for two years and were regularly submitted to sensory tests. There was no significant difference in quality between the irradiated samples and the non-irradiated controls. (orig.) [de

Ductility and performance assessment of high strength self compacting concrete (HSSCC) deep beams: An experimental investigation

International Nuclear Information System (INIS)

Mohammadhassani, Mohammad; Jumaat, Mohd Zamin; Jameel, Mohammed; Badiee, Hamid; Arumugam, Arul M.S.

2012-01-01

Highlights: ► Ductility decreased with increase in tensile reinforcement ratio. ► The width of the load point and the support point influences premature failure. ► Load–deflection relationship is linear till 85% of the ultimate load. ► The absorbed energy increases with the increase of tensile reinforcement ratios. - Abstract: The behavior of deep beams is significantly different from that of normal beams. Because of their proportions, deep beams are likely to have strength controlled by shear. This paper discusses the results of eight simply supported high strength self compacting concrete (HSSCC) deep beams having variation in ratio of web reinforcement and tensile reinforcement. The deflection at two points along the beam length, web strains, tensile bars strains and the strain at concrete surface are recorded. The results show that the strain distribution at the section height of mid span is nonlinear. Ductility decreased with increase in tensile reinforcement ratio. The effect of width of load point and the support point is more important than the effect of tensile reinforcement ratio in preventing premature failure. Load–deflection graphs confirm linear relationship up to 85% of the ultimate load for HSSCC over-reinforcement web sections. The absorbed energy index increases with the increase in tensile reinforcement ratios.

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.

Deep groundwater quantity and quality in the southwestern US

Science.gov (United States)

Kang, M.; Ayars, J. E.; Jackson, R. B.

2017-12-01

Groundwater demands are growing in many arid regions and adaptation through the use of non-traditional resources during extreme droughts is increasingly common. One such resource is deep groundwater, which we define as deeper than 300 m and up to several kilometer-depths. Although deep groundwater has been studied in the context of oil and gas, geothermal, waste disposal, and other uses, it remains poorly characterized, especially for the purposes of human consumption and irrigation uses. Therefore, we evaluate deep groundwater quantity and quality within these contexts. We compile and analyze data from water management agencies and oil and gas-based sources for the southwestern US, with a detailed look at California's Central Valley. We also use crop tolerance thresholds to evaluate deep groundwater quality for irrigation purposes. We find fresh and usable groundwater volume estimates in California's Central Valley to increase by three- and four-fold respectively when depths of up to 3 km are considered. Of the ten basins in the southwestern US with the most data, we find that the Great Basin has the greatest proportions of fresh and usable deep groundwater. Given the potentially large deep groundwater volumes, it is important to characterize the resource, guard against subsidence where extracted, and protect it for use in decades and centuries to come.

A randomised crossover trial of the acute effects of a deep-fried Mars bar or porridge on the cerebral vasculature.

Science.gov (United States)

Dunn, William G; Walters, Matthew R

2014-11-01

The deep-fried Mars bar has been cited as 'all that is wrong with the high-fat, high-sugar Scottish diet'. We investigated the effect of ingestion of a deep-fried Mars bar or porridge on cerebrovascular reactivity. We hypothesised that deep-fried Mars bar ingestion would impair cerebrovascular reactivity, which is associated with increased risk of ischaemic stroke. Twenty-four fasted volunteers were randomised to receive a deep-fried Mars bar and then porridge (control), or vice-versa. We used transcranial Doppler ultrasound to calculate Breath Holding Index as a surrogate measure of cerebrovascular reactivity. Change in Breath Holding Index post-ingestion was the primary outcome measure. Twenty-four healthy adults (mean (SD) age 21.5 (1.7) years, 14 males) completed the protocol. Deep-fried Mars bar ingestion caused a non-significant reduction in cerebrovascular reactivity relative to control (mean difference in absolute Breath Holding Index after deep-fried Mars bar versus porridge -0.11, p = 0.40). Comparison of the difference between the absolute change in Breath Holding Index between genders demonstrated a significant impairment of cerebrovascular reactivity in males (mean difference women minus men of 0.65, 95% CI 0.30 to 1.00, p = 0.0003). Ingestion of a bolus of sugar and fat caused no overall difference in cerebrovascular reactivity, but there was a modest decrease in males. Impaired cerebrovascular reactivity is associated with increased stroke risk, and therefore deep-fried Mars bar ingestion may acutely contribute to cerebral hypoperfusion in men. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

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

PRE-ACTIVITY MODULATION OF LOWER EXTREMITY MUSCLES WITHIN DIFFERENT TYPES AND HEIGHTS OF DEEP JUMP

Directory of Open Access Journals (Sweden)

Vladimir Mrdakovic

2008-06-01

Full Text Available The purpose of this study was to determine modulation of pre- activity related to different types and heights of deep jump. Sixteen male soccer players without experience in deep jumps training (the national competition; 15.0 ± 0.5yrs; weight 61.9 ± 6.1kg; height 1.77 ± 0.07m, who participated in the study, performed three types of deep jump (bounce landing, counter landing, and bounce drop jump from three different heights (40cm, 60cm, and 80cm. Surface EMG device (1000Hz was used to estimate muscle activity (maximal amplitude of EMG - AmaxEMG; integral EMG signal - iEMG of five muscles (mm.gastrocnemii, m.soleus, m.tibialis anterior, m.vastus lateralis within 150ms before touchdown. All the muscles, except m. gastrocnemius medialis, showed systematic increase in pre-activity when platform height was raised. For most of the lower extremity muscles, the most significant differences were between values of pre-activity obtained for 40 cm and 80 cm platforms. While the amount of muscle pre-activity in deep jumps from the heights above and beneath the optimal one did not differ significantly from that generated in deep jumps from the optimal drop height of 60 cm, the patterns of muscle pre-activity obtained for the heights above the optimal one did differ from those obtained for the optimal drop height. That suggests that deep jumps from the heights above the optimal one do not seem to be an adequate exercise for adjusting muscle activity for the impact. Muscle pre-activity in bounce drop jumps differed significantly from that in counter landing and bounce landing respectively, which should indicate that a higher amount of pre-activity generated during bounce drop jumps was used for performing take-offs. As this study included the subjects who were not familiar with deep jumps training, the prospective studies should reveal the results of athletes with previous experience

Strengthening of self-compacting reinforced concrete deep beams containing circular openings with CFRP

Directory of Open Access Journals (Sweden)

Al-Bayati Nabeel

2018-01-01

Full Text Available This paper shows the behavior of reinforced self-compacting concrete deep beams with circular openings strengthened in shear with various arrangements of externally bonded Carbon Fibre Reinforced Polymer (CFRP. Six simply supported deep beams were constructed and tested under two points load up to the failure for this purpose. All tested beams had same geometry, compressive strength, shear span to depth ratio, main flexural and web reinforcement. The variables considered in this study include the influence of fiber orientation, utilizing longitudinal CFRP strips with vertical strips and area of CFRP. The test results indicated that the presence of the circular openings in center of load path reduce stiffness and ultimate strength by about 50% when compared with solid one, also it was found that the externally bonded CFRP can significantly increase the ultimate load and enhance the stiffness of deep beam with openings.

Deep Learning in Gastrointestinal Endoscopy.

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Patel, Vivek; Armstrong, David; Ganguli, Malika; Roopra, Sandeep; Kantipudi, Neha; Albashir, Siwar; Kamath, Markad V

2016-01-01

Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.

Detecting atrial fibrillation by deep convolutional neural networks.

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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.

Pre-cementation of deep shaft

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Heinz, W. F.

1988-12-01

Pre-cementation or pre-grouting of deep shafts in South Africa is an established technique to improve safety and reduce water ingress during shaft sinking. The recent completion of several pre-cementation projects for shafts deeper than 1000m has once again highlighted the effectiveness of pre-grouting of shafts utilizing deep slimline boreholes and incorporating wireline technique for drilling and conventional deep borehole grouting techniques for pre-cementation. Pre-cementation of deep shaft will: (i) Increase the safety of shaft sinking operation (ii) Minimize water and gas inflow during shaft sinking (iii) Minimize the time lost due to additional grouting operations during sinking of the shaft and hence minimize costly delays and standing time of shaft sinking crews and equipment. (iv) Provide detailed information of the geology of the proposed shaft site. Informations on anomalies, dykes, faults as well as reef (gold bearing conglomerates) intersections can be obtained from the evaluation of cores of the pre-cementation boreholes. (v) Provide improved rock strength for excavations in the immediate vicinity of the shaft area. The paper describes pre-cementation techniques recently applied successfully from surface and some conclusions drawn for further considerations.

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...

Enhanced deep ocean ventilation and oxygenation with global warming

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Froelicher, T. L.; Jaccard, S.; Dunne, J. P.; Paynter, D.; Gruber, N.

2014-12-01

Twenty-first century coupled climate model simulations, observations from the recent past, and theoretical arguments suggest a consistent trend towards warmer ocean temperatures and fresher polar surface oceans in response to increased radiative forcing resulting in increased upper ocean stratification and reduced ventilation and oxygenation of the deep ocean. Paleo-proxy records of the warming at the end of the last ice age, however, suggests a different outcome, namely a better ventilated and oxygenated deep ocean with global warming. Here we use a four thousand year global warming simulation from a comprehensive Earth System Model (GFDL ESM2M) to show that this conundrum is a consequence of different rates of warming and that the deep ocean is actually better ventilated and oxygenated in a future warmer equilibrated climate consistent with paleo-proxy records. The enhanced deep ocean ventilation in the Southern Ocean occurs in spite of increased positive surface buoyancy fluxes and a constancy of the Southern Hemisphere westerly winds - circumstances that would otherwise be expected to lead to a reduction in deep ocean ventilation. This ventilation recovery occurs through a global scale interaction of the Atlantic Meridional Overturning Circulation undergoing a multi-centennial recovery after an initial century of transient decrease and transports salinity-rich waters inform the subtropical surface ocean to the Southern Ocean interior on multi-century timescales. The subsequent upwelling of salinity-rich waters in the Southern Ocean strips away the freshwater cap that maintains vertical stability and increases open ocean convection and the formation of Antarctic Bottom Waters. As a result, the global ocean oxygen content and the nutrient supply from the deep ocean to the surface are higher in a warmer ocean. The implications for past and future changes in ocean heat and carbon storage will be discussed.

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)

Moderate and deep nurse-administered propofol sedation is safe

DEFF Research Database (Denmark)

Jensen, Jeppe Thue; Møller, Ann; Hornslet, Pernille

2015-01-01

INTRODUCTION: Non-anaesthesiologist-administered propofol sedation (NAPS/NAAP) is increasingly used in many countries. Most regimens aim for light or moderate sedation. Little evidence on safety of deep NAPS sedation is available. The aim of this study was to explore the safety of intermittent deep...

Continuous background light significantly increases flashing-light enhancement of photosynthesis and growth of microalgae.

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Abu-Ghosh, Said; Fixler, Dror; Dubinsky, Zvy; Iluz, David

2015-01-01

Under specific conditions, flashing light enhances the photosynthesis rate in comparison to continuous illumination. Here we show that a combination of flashing light and continuous background light with the same integrated photon dose as continuous or flashing light alone can be used to significantly enhance photosynthesis and increase microalgae growth. To test this hypothesis, the green microalga Dunaliella salina was exposed to three different light regimes: continuous light, flashing light, and concomitant application of both. Algal growth was compared under three different integrated light quantities; low, intermediate, and moderately high. Under the combined light regime, there was a substantial increase in all algal growth parameters, with an enhanced photosynthesis rate, within 3days. Our strategy demonstrates a hitherto undescribed significant increase in photosynthesis and algal growth rates, which is beyond the increase by flashing light alone. Copyright © 2015 Elsevier Ltd. All rights reserved.

Breast-cancer-associated metastasis is significantly increased in a model of autoimmune arthritis.

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Das Roy, Lopamudra; Pathangey, Latha B; Tinder, Teresa L; Schettini, Jorge L; Gruber, Helen E; Mukherjee, Pinku

2009-01-01

Sites of chronic inflammation are often associated with the establishment and growth of various malignancies including breast cancer. A common inflammatory condition in humans is autoimmune arthritis (AA) that causes inflammation and deformity of the joints. Other systemic effects associated with arthritis include increased cellular infiltration and inflammation of the lungs. Several studies have reported statistically significant risk ratios between AA and breast cancer. Despite this knowledge, available for a decade, it has never been questioned if the site of chronic inflammation linked to AA creates a milieu that attracts tumor cells to home and grow in the inflamed bones and lungs which are frequent sites of breast cancer metastasis. To determine if chronic inflammation induced by autoimmune arthritis contributes to increased breast cancer-associated metastasis, we generated mammary gland tumors in SKG mice that were genetically prone to develop AA. Two breast cancer cell lines, one highly metastatic (4T1) and the other non-metastatic (TUBO) were used to generate the tumors in the mammary fat pad. Lung and bone metastasis and the associated inflammatory milieu were evaluated in the arthritic versus the non-arthritic mice. We report a three-fold increase in lung metastasis and a significant increase in the incidence of bone metastasis in the pro-arthritic and arthritic mice compared to non-arthritic control mice. We also report that the metastatic breast cancer cells augment the severity of arthritis resulting in a vicious cycle that increases both bone destruction and metastasis. Enhanced neutrophilic and granulocytic infiltration in lungs and bone of the pro-arthritic and arthritic mice and subsequent increase in circulating levels of proinflammatory cytokines, such as macrophage colony stimulating factor (M-CSF), interleukin-17 (IL-17), interleukin-6 (IL-6), vascular endothelial growth factor (VEGF), and tumor necrosis factor-alpha (TNF-alpha) may contribute

Breast cancer-associated metastasis is significantly increased in a model of autoimmune arthritis

Science.gov (United States)

Das Roy, Lopamudra; Pathangey, Latha B; Tinder, Teresa L; Schettini, Jorge L; Gruber, Helen E; Mukherjee, Pinku

2009-01-01

Introduction Sites of chronic inflammation are often associated with the establishment and growth of various malignancies including breast cancer. A common inflammatory condition in humans is autoimmune arthritis (AA) that causes inflammation and deformity of the joints. Other systemic effects associated with arthritis include increased cellular infiltration and inflammation of the lungs. Several studies have reported statistically significant risk ratios between AA and breast cancer. Despite this knowledge, available for a decade, it has never been questioned if the site of chronic inflammation linked to AA creates a milieu that attracts tumor cells to home and grow in the inflamed bones and lungs which are frequent sites of breast cancer metastasis. Methods To determine if chronic inflammation induced by autoimmune arthritis contributes to increased breast cancer-associated metastasis, we generated mammary gland tumors in SKG mice that were genetically prone to develop AA. Two breast cancer cell lines, one highly metastatic (4T1) and the other non-metastatic (TUBO) were used to generate the tumors in the mammary fat pad. Lung and bone metastasis and the associated inflammatory milieu were evaluated in the arthritic versus the non-arthritic mice. Results We report a three-fold increase in lung metastasis and a significant increase in the incidence of bone metastasis in the pro-arthritic and arthritic mice compared to non-arthritic control mice. We also report that the metastatic breast cancer cells augment the severity of arthritis resulting in a vicious cycle that increases both bone destruction and metastasis. Enhanced neutrophilic and granulocytic infiltration in lungs and bone of the pro-arthritic and arthritic mice and subsequent increase in circulating levels of proinflammatory cytokines, such as macrophage colony stimulating factor (M-CSF), interleukin-17 (IL-17), interleukin-6 (IL-6), vascular endothelial growth factor (VEGF), and tumor necrosis factor

A Framework for Transparently Accessing Deep Web Sources

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Dragut, Eduard Constantin

2010-01-01

An increasing number of Web sites expose their content via query interfaces, many of them offering the same type of products/services (e.g., flight tickets, car rental/purchasing). They constitute the so-called "Deep Web". Accessing the content on the Deep Web has been a long-standing challenge for the database community. For a user interested in…

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

Deep Learning: A Primer for Radiologists.

Science.gov (United States)

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Facial expression recognition based on improved deep belief networks

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Wu, Yao; Qiu, Weigen

2017-08-01

In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection.

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Shafiee, Mohammad Javad; Chung, Audrey G; Khalvati, Farzad; Haider, Masoom A; Wong, Alexander

2017-10-01

While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data. We propose an evolutionary deep radiomic sequencer discovery approach based on evolutionary deep intelligence. Motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet similarly descriptive radiomic sequences over multiple generations. As a result, this framework improves operational efficiency and enables diagnosis to be run locally at the radiologist's computer while maintaining detection accuracy. We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically proven diagnostic data from the LIDC-IDRI dataset. The EDRS shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to previous radiomics approaches.

More Far-Side Deep Moonquake Nests Discovered

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Nakamura, Y.; Jackson, John A.; Jackson, Katherine G.

2004-01-01

As reported last year, we started to reanalyze the seismic data acquired from 1969 to 1977 with a network of stations established on the Moon during the Apollo mission. The reason for the reanalysis was because recent advances in computer technology make it possible to employ much more sophisticated analysis techniques than was possible previously. The primary objective of the reanalysis was to search for deep moonquakes on the far side of the Moon and, if found, to use them to infer the structure of the Moon's deep interior, including a possible central core. The first step was to identify any new deep moonquakes that escaped our earlier search by applying a combination of waveform cross-correlation and single-link cluster analysis, and then to see if any of them are from previously unknown nests of deep moonquakes. We positively identified 7245 deep moonquakes, more than a five-fold increase from the previous 1360. We also found at least 88 previously unknown deep-moonquake nests. The question was whether any of these newly discovered nets were on the far side of the Moon, and we now report that our analysis of the data indicates that some of them are indeed on the far side.

Determination of Process Parameters in Multi-Stage Hydro-Mechanical Deep Drawing by FE Simulation

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Kumar, D. Ravi; Manohar, M.

2017-09-01

In this work, analysis has been carried to simulate manufacturing of a near hemispherical bottom part with large depth by hydro-mechanical deep drawing with an aim to reduce the number of forming steps and to reduce the extent of thinning in the dome region. Inconel 718 has been considered as the material due to its importance in aerospace industry. It is a Ni-based super alloy and it is one of the most widely used of all super alloys primarily due to large-scale applications in aircraft engines. Using Finite Element Method (FEM), numerical simulations have been carried out for multi-stage hydro-mechanical deep drawing by using the same draw ratios and design parameters as in the case of conventional deep drawing in four stages. The results showed that the minimum thickness in the final part can be increased significantly when compared to conventional deep drawing. It has been found that the part could be deep drawn to the desired height (after trimming at the final stage) without any severe wrinkling. Blank holding force (BHF) and peak counter pressure have been found to have a strong influence on thinning in the component. Decreasing the coefficient of friction has marginally increased the minimum thickness in the final component. By increasing the draw ratio and optimizing BHF, counter pressure and die corner radius in the simulations, it has been found that it is possible to draw the final part in three stages. It has been found that thinning can be further reduced by decreasing the initial blank size without any reduction in the final height. This reduced the draw ratio at every stage and optimum combination of BHF and counter pressure have been found for the 3-stage process also.

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).

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...

Effect of fertilizer application and deep rooting measures on the absorption of 137Cs by rice

International Nuclear Information System (INIS)

Zhu Yongyi; Yang Juncheng; Chen Jingjian; Liu Xuelian; Xu Yinliang; Sun Zhiming

1998-01-01

Effects of the application of phosphorus and potassium fertilizer and deep rooting on reducing the absorption of 137 Cs by rice (especially in the seed) were estimated using pot and plot experiment. The results show that the available 137 Cs in soil decreased significantly by applying potassium fertilizer, which led to the lower accumulation of 137 Cs in rice stem and the most effective measure was to apply potassium sulphate of 922.5 kg/ha. An unsteady effect with phosphate fertilizer existed. When P application was in a lower amount, the accumulation of 137 Cs in rice decreased. But following the increase of P application, the absorption of 137 Cs was promoted. The 137 Cs accumulation in rice decreased significantly by deep rooting

Human activities on the deep seafloor in the North East Atlantic: an assessment of spatial extent.

Directory of Open Access Journals (Sweden)

Angela R Benn

Full Text Available BACKGROUND: Environmental impacts of human activities on the deep seafloor are of increasing concern. While activities within waters shallower than 200 m have been the focus of previous assessments of anthropogenic impacts, no study has quantified the extent of individual activities or determined the relative severity of each type of impact in the deep sea. METHODOLOGY: The OSPAR maritime area of the North East Atlantic was chosen for the study because it is considered to be one of the most heavily impacted by human activities. In addition, it was assumed data would be accessible and comprehensive. Using the available data we map and estimate the spatial extent of five major human activities in the North East Atlantic that impact the deep seafloor: submarine communication cables, marine scientific research, oil and gas industry, bottom trawling and the historical dumping of radioactive waste, munitions and chemical weapons. It was not possible to map military activities. The extent of each activity has been quantified for a single year, 2005. PRINCIPAL FINDINGS: Human activities on the deep seafloor of the OSPAR area of the North Atlantic are significant but their footprints vary. Some activities have an immediate impact after which seafloor communities could re-establish, while others can continue to make an impact for many years and the impact could extend far beyond the physical disturbance. The spatial extent of waste disposal, telecommunication cables, the hydrocarbon industry and marine research activities is relatively small. The extent of bottom trawling is very significant and, even on the lowest possible estimates, is an order of magnitude greater than the total extent of all the other activities. CONCLUSIONS/SIGNIFICANCE: To meet future ecosystem-based management and governance objectives for the deep sea significant improvements are required in data collection and availability as well as a greater awareness of the relative impact of

Ductility and performance assessment of high strength self compacting concrete (HSSCC) deep beams: An experimental investigation

Energy Technology Data Exchange (ETDEWEB)

Mohammadhassani, Mohammad, E-mail: mmh356@yahoo.com [Department of Civil Engineering, University of Malaya, Kuala Lumpur (Malaysia); Jumaat, Mohd Zamin; Jameel, Mohammed [Department of Civil Engineering, University of Malaya, Kuala Lumpur (Malaysia); Badiee, Hamid [Department of Civil Engineering, University of Kerman (Iran, Islamic Republic of); Arumugam, Arul M.S. [Department of Civil Engineering, University of Malaya, Kuala Lumpur (Malaysia)

2012-09-15

Highlights: Black-Right-Pointing-Pointer Ductility decreased with increase in tensile reinforcement ratio. Black-Right-Pointing-Pointer The width of the load point and the support point influences premature failure. Black-Right-Pointing-Pointer Load-deflection relationship is linear till 85% of the ultimate load. Black-Right-Pointing-Pointer The absorbed energy increases with the increase of tensile reinforcement ratios. - Abstract: The behavior of deep beams is significantly different from that of normal beams. Because of their proportions, deep beams are likely to have strength controlled by shear. This paper discusses the results of eight simply supported high strength self compacting concrete (HSSCC) deep beams having variation in ratio of web reinforcement and tensile reinforcement. The deflection at two points along the beam length, web strains, tensile bars strains and the strain at concrete surface are recorded. The results show that the strain distribution at the section height of mid span is nonlinear. Ductility decreased with increase in tensile reinforcement ratio. The effect of width of load point and the support point is more important than the effect of tensile reinforcement ratio in preventing premature failure. Load-deflection graphs confirm linear relationship up to 85% of the ultimate load for HSSCC over-reinforcement web sections. The absorbed energy index increases with the increase in tensile reinforcement ratios.

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...

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...

Near Earth Architectural Options for a Future Deep Space Optical Communications Network

Science.gov (United States)

Edwards, B. L.; Liebrecht, P. E.; Fitzgerald, R. J.

2004-01-01

In the near future the National Aeronautics and Space Administration anticipates a significant increase in demand for long-haul communications services from deep space to Earth. Distances will range from 0.1 to 40 AU, with data rate requirements in the 1's to 1000's of Mbits/second. The near term demand is driven by NASA's Space Science Enterprise which wishes to deploy more capable instruments onboard spacecraft and increase the number of deep space missions. The long term demand is driven by missions with extreme communications challenges such as very high data rates from the outer planets, supporting sub-surface exploration, or supporting NASA's Human Exploration and Development of Space Enterprise beyond Earth orbit. Laser communications is a revolutionary communications technology that will dramatically increase NASA's ability to transmit information across the solar system. Lasercom sends information using beams of light and optical elements, such as telescopes and optical amplifiers, rather than RF signals, amplifiers, and antennas. This paper provides an overview of different network options at Earth to meet NASA's deep space lasercom requirements. It is based mainly on work done for the Mars Laser Communications Demonstration Project, a joint project between NASA's Goddard Space Flight Center (GSFC), the Jet Propulsion Laboratory, California Institute of Technology (JPL), and the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL). It reports preliminary conclusions from the Mars Lasercom Study conducted at MIT/LL and on additional work done for the Tracking and Data Relay Satellite System Continuation Study at GSFC. A lasercom flight terminal will be flown on the Mars Telesat Orbiter (MTO) to be launched by NASA in 2009, and will be the first high rate deep space demonstration of this revolutionary technology.

The significance and lag-time of deep through flow: an example from a small, ephemeral catchment with contrasting soil types in the Adelaide Hills, South Australia

Directory of Open Access Journals (Sweden)

J. VanLeeuwen

2009-07-01

Full Text Available The importance of deep soil-regolith through flow in a small (3.4 km² ephemeral catchment in the Adelaide Hills of South Australia was investigated by detailed hydrochemical analysis of soil water and stream flow during autumn and early winter rains. In this Mediterranean climate with strong summer moisture deficits, several significant rainfalls are required to generate soil through flow and stream flow [in ephemeral streams]. During autumn 2007, a large (127 mm drought-breaking rain occurred in April followed by significant May rains; most of this April and May precipitation occurred prior to the initiation of stream flow in late May. These early events, especially the 127 mm April event, had low stable water isotope values compared with later rains during June and July and average winter precipitation. Thus, this large early autumn rain event with low isotopic values (δ¹⁸O, δD provided an excellent natural tracer. During later June and July rainfall events, daily stream and soil water samples were collected and analysed. Results from major and trace elements, water isotopes (δ¹⁸O, δD, and dissolved organic carbon analysis clearly demonstrate that a large component of this early April and May rain was stored and later pushed out of deep soil and regolith zones. This pre-event water was identified in the stream as well as identified in deep soil horizons due to its different isotopic signature which contrasted sharply with the June–July event water. Based on this data, the soil-regolith hydrologic system for this catchment has been re-thought. The catchment area consists of about 60% sandy and 40% clayey soils. Regolith flow in the sandy soil system and not the clayey soil system is now thought to dominate the deep subsurface flow in this catchment. The clayey texture contrast soils had rapid response to rain events and saturation excess overland flow. The sandy soils had delayed soil through flow and

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.

Improving the Robustness of Deep Neural Networks via Stability Training

OpenAIRE

Zheng, Stephan; Song, Yang; Leung, Thomas; Goodfellow, Ian

2016-01-01

In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such...

Randomized Clinical Trials on Deep Carious Lesions

DEFF Research Database (Denmark)

Bjørndal, Lars; Fransson, Helena; Bruun, Gitte

2017-01-01

nonselective carious removal to hard dentin with or without pulp exposure. The aim of this article was to report the 5-y outcome on these previously treated patients having radiographically well-defined carious lesions extending into the pulpal quarter of the dentin but with a well-defined radiodense zone...... pulp exposures per se were included as failures. Pulp exposure rate was significantly lower in the stepwise carious removal group (21.2% vs. 35.5%; P = 0.014). Irrespective of pulp exposure status, the difference (13.3%) was still significant when sustained pulp vitality without apical radiolucency......) in deep carious lesions in adults. In conclusion, the stepwise carious removal group had a significantly higher proportion of pulps with sustained vitality without apical radiolucency versus nonselective carious removal of deep carious lesions in adult teeth at 5-y follow-up (ClinicalTrials.gov NCT...

Total circulating microparticle levels are increased in patients with deep infiltrating endometriosis.

Science.gov (United States)

Munrós, J; Martínez-Zamora, M A; Tàssies, D; Coloma, J L; Torrente, M A; Reverter, J C; Carmona, F; Balasch, J

2017-02-01

Are the levels of total circulating cell-derived microparticles (cMPs) and circulating tissue factor-containing microparticles (cMP-TF) increased in patients with endometriosis? The levels of total cMP, but not cMP-TF, were higher in patients with endometriosis, and these were attributed to higher levels in patients with deep infiltrating endometriosis (DIE). Previous studies have reported elevated levels of total cMP in inflammatory conditions as well as higher levels of other inflammatory biomarkers in endometriosis. Increased expression of tissue factor (a transmembrane receptor for Factor VII/VIIa) in eutopic and ectopic endometrium from patients with endometriosis has been described. There is no previous data regarding total cMP and cMP-TF levels in patients with endometriosis. A prospective case-control study including two groups of patients was carried out. The E group included 65 patients with surgically confirmed endometriosis (37 with DIE lesions) and the C group comprises 33 women without surgical findings of any form of endometriosis. Patients and controls were recruited during the same 10-month period. Controls were the next patient without endometriosis undergoing surgery, after including two patients with endometriosis. Venous blood samples for total cMP and cMP-TF determinations were obtained at the time of surgery, before anesthesia at a tertiary care center. To assess total cMP, an ELISA functional assay was used and cMP-TF activity in plasma was measured using an ELISA kit. Total cMP levels in plasma were higher in the E group compared with the C group (P < 0.0001). The subanalysis of endometriosis patients with DIE or with ovarian endometriomas without DIE showed that total cMP levels were higher in the DIE group (P = 0.001). There were no statistically significant differences in cMP-TF levels among the groups analyzed. This is a preliminary study in which the sample size was arbitrarily decided, albeit in keeping with previous studies analyzing

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

Leading particle in deep inelastic scattering

International Nuclear Information System (INIS)

Petrov, V.A.

1984-01-01

The leading particle effect in deep inelastic scattering is considered. The change of the characteris cs shape of the leading particle inclusive spectrum with Q 2 is estimated to be rather significant at very high Q 2

Distinctive Microbial Community Structure in Highly Stratified Deep-Sea Brine Water Columns

KAUST Repository

Bougouffa, Salim; Yang, J. K.; Lee, O. O.; Wang, Y.; Batang, Zenon B.; Al-Suwailem, Abdulaziz M.; Qian, P. Y.

2013-01-01

Atlantis II and Discovery are two hydrothermal and hypersaline deep-sea pools in the Red Sea rift that are characterized by strong thermohalo-stratification and temperatures steadily peaking near the bottom. We conducted comprehensive vertical profiling of the microbial populations in both pools and highlighted the influential environmental factors. Pyrosequencing of the 16S rRNA genes revealed shifts in community structures vis-à-vis depth. High diversity and low abundance were features of the deepest convective layers despite the low cell density. Surprisingly, the brine interfaces had significantly higher cell counts than the overlying deep-sea water, yet they were lowest in diversity. Vertical stratification of the bacterial populations was apparent as we moved from the Alphaproteobacteria-dominated deep sea to the Planctomycetaceae- or Deferribacteres-dominated interfaces to the Gammaproteobacteria-dominated brine layers. Archaeal marine group I was dominant in the deep-sea water and interfaces, while several euryarchaeotic groups increased in the brine. Across sites, microbial phylotypes and abundances varied substantially in the brine interface of Discovery compared with Atlantis II, despite the near-identical populations in the overlying deep-sea waters. The lowest convective layers harbored interestingly similar microbial communities, even though temperature and heavy metal concentrations were very different. Multivariate analysis indicated that temperature and salinity were the major influences shaping the communities. The harsh conditions and the low-abundance phylotypes could explain the observed correlation in the brine pools.

Distinctive Microbial Community Structure in Highly Stratified Deep-Sea Brine Water Columns

KAUST Repository

Bougouffa, Salim

2013-03-29

Atlantis II and Discovery are two hydrothermal and hypersaline deep-sea pools in the Red Sea rift that are characterized by strong thermohalo-stratification and temperatures steadily peaking near the bottom. We conducted comprehensive vertical profiling of the microbial populations in both pools and highlighted the influential environmental factors. Pyrosequencing of the 16S rRNA genes revealed shifts in community structures vis-à-vis depth. High diversity and low abundance were features of the deepest convective layers despite the low cell density. Surprisingly, the brine interfaces had significantly higher cell counts than the overlying deep-sea water, yet they were lowest in diversity. Vertical stratification of the bacterial populations was apparent as we moved from the Alphaproteobacteria-dominated deep sea to the Planctomycetaceae- or Deferribacteres-dominated interfaces to the Gammaproteobacteria-dominated brine layers. Archaeal marine group I was dominant in the deep-sea water and interfaces, while several euryarchaeotic groups increased in the brine. Across sites, microbial phylotypes and abundances varied substantially in the brine interface of Discovery compared with Atlantis II, despite the near-identical populations in the overlying deep-sea waters. The lowest convective layers harbored interestingly similar microbial communities, even though temperature and heavy metal concentrations were very different. Multivariate analysis indicated that temperature and salinity were the major influences shaping the communities. The harsh conditions and the low-abundance phylotypes could explain the observed correlation in the brine pools.

Significance of water fluxes in a deep arid-region vadose zone to waste disposal strategies

International Nuclear Information System (INIS)

Johnejack, K.R.; Blout, D.O.; Sully, M.J.; Emer, D.F.; Hammermeister, D.P.; Dever, L.G.; O'Neill, L.J.; Tyler, S.W.; Chapman, J.

1994-01-01

Recently collected subsurface site characterization data have led to the development of a conceptual model of water movement beneath the Area 5 Radioactive Waste Management Site (RWMS) at the Nevada Test Site (NTS) that differs significantly from the conceptual model of water movement inherent in Resource Conservation and Recovery Act (RCRA) regulations. At the Area 5 RWMS, water fluxes in approximately the upper 75 m (250 ft) of the vadose zone point in the upward direction (rather than downward) which effectively isolates this region from the deep (approximately 250 m (820 ft)) uppermost aquifer. Standard RCRA approaches for detection and containment (groundwater monitoring and double liners/leachate collection/leak detection systems) are not able to fulfill their intended function in this rather unique hydrogeologic environment. In order to better fulfill the waste detection and containment intentions of RCRA for mixed waste disposal at the Area 5 RWMS, the Department of Energy, Nevada Operations Office (DOE/NV) is preparing a single petition for both a waiver from groundwater monitoring and an exemption from double liners with leachate collection/leak detection. DOE/NV proposes in this petition that the containment function of liners and leachate collection is better accomplished by the natural hydrogeologic processes operating in the upper vadose zone; and the detection function of groundwater monitoring and the leak detection system in liners is better fulfilled by an alternative vadose zone monitoring system. In addition, an alternative point of compliance is proposed that will aid in early detection, as well as limit the extent of potential contamination before detection. Finally, special cell design features and operation practices will be implemented to limit leachate formation, especially while the cell is open to the atmosphere during waste emplacement

Acute and chronic changes in brain activity with deep brain stimulation for refractory depression.

Science.gov (United States)

Conen, Silke; Matthews, Julian C; Patel, Nikunj K; Anton-Rodriguez, José; Talbot, Peter S

2018-04-01

Deep brain stimulation is a potential option for patients with treatment-refractory depression. Deep brain stimulation benefits have been reported when targeting either the subgenual cingulate or ventral anterior capsule/nucleus accumbens. However, not all patients respond and optimum stimulation-site is uncertain. We compared deep brain stimulation of the subgenual cingulate and ventral anterior capsule/nucleus accumbens separately and combined in the same seven treatment-refractory depression patients, and investigated regional cerebral blood flow changes associated with acute and chronic deep brain stimulation. Deep brain stimulation-response was defined as reduction in Montgomery-Asberg Depression Rating Scale score from baseline of ≥50%, and remission as a Montgomery-Asberg Depression Rating Scale score ≤8. Changes in regional cerebral blood flow were assessed using [ 15 O]water positron emission tomography. Remitters had higher relative regional cerebral blood flow in the prefrontal cortex at baseline and all subsequent time-points compared to non-remitters and non-responders, with prefrontal cortex regional cerebral blood flow generally increasing with chronic deep brain stimulation. These effects were consistent regardless of stimulation-site. Overall, no significant regional cerebral blood flow changes were apparent when deep brain stimulation was acutely interrupted. Deep brain stimulation improved treatment-refractory depression severity in the majority of patients, with consistent changes in local and distant brain regions regardless of target stimulation. Remission of depression was reached in patients with higher baseline prefrontal regional cerebral blood flow. Because of the small sample size these results are preliminary and further evaluation is necessary to determine whether prefrontal cortex regional cerebral blood flow could be a predictive biomarker of treatment response.

Using Sentinel-2A multispectral imagery to explore for deep groundwater resources in the Ceres-Tankwa Karoo, Western Cape, South Africa: Significance for the 'water-energy(-food) nexus' in an arid region

Science.gov (United States)

Hartnady, Chris; Wise, Edward; Hartnady, Michael; Olianti, Camille; Hay, E. Rowena

2017-04-01

The Ceres-Tankwa region is an arid region in the south-western part of the main Karoo Basin, underlain by folded and faulted strata of the Cape and lower Karoo Supergroups in the syntaxis zone between the Western and Southern branches of the Cape Fold Belt. Explored for oil in the mid-1960s, with the drilling of the >3000 m deep KL1/65 borehole, the area recently attracted attention as a potential shale-gas prospect with the drilling in 2015 of the 671 m-deep KZF-1 research borehole on the farm Zandfontein (de Kock et al, 2016). KZF-1 encountered no positive indication of methane gas in the carbonaceous shale target but intersected a strong flow of deep groundwater from fractures in the basal Dwyka tillite. The accidental discovery of deep artesian groundwater, probably originating from the underlying Cape Supergroup aquifers and of significantly better quality than the shallow aquifer utilised by local farmers, has important implications for future development here. Using 13-channel multispectral data from the European Space Agency satellite Sentinel-2A, a false-colour composite image, centred about the KZF-1 location, was assembled by combination of selected spectral band-ratios. Stratigraphic layering and associated folding within the hitherto undivided, pelitic Tierberg Formation (Ecca Group), is revealed in striking new detail, together with narrow lines of stratal offset corresponding to previously unmapped faults. KZF-1 is evidently sited within an anomalous NE/SW-striking belt, unlike the general NNW/SSE strike of Cape-Karoo sequence strata in the north-western part of the image. Associated with a notable strike change of a lower Tierberg marker unit, subparallel to and aligned with a similar trend in the Swartruggens mountain foothills to the SW, a deep-seated, controlling, NE/SW-striking fault structure may continue downwards from the lower Karoo units into the underlying Cape strata, providing hydraulic connection. With the looming threat of global

Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

Science.gov (United States)

Burt, Jeremy R; Torosdagli, Neslisah; Khosravan, Naji; RaviPrakash, Harish; Mortazi, Aliasghar; Tissavirasingham, Fiona; Hussein, Sarfaraz; Bagci, Ulas

2018-04-10

Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.

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.

The dynamics of biogeographic ranges in the deep sea.

Science.gov (United States)

McClain, Craig R; Hardy, Sarah Mincks

2010-12-07

Anthropogenic disturbances such as fishing, mining, oil drilling, bioprospecting, warming, and acidification in the deep sea are increasing, yet generalities about deep-sea biogeography remain elusive. Owing to the lack of perceived environmental variability and geographical barriers, ranges of deep-sea species were traditionally assumed to be exceedingly large. In contrast, seamount and chemosynthetic habitats with reported high endemicity challenge the broad applicability of a single biogeographic paradigm for the deep sea. New research benefiting from higher resolution sampling, molecular methods and public databases can now more rigorously examine dispersal distances and species ranges on the vast ocean floor. Here, we explore the major outstanding questions in deep-sea biogeography. Based on current evidence, many taxa appear broadly distributed across the deep sea, a pattern replicated in both the abyssal plains and specialized environments such as hydrothermal vents. Cold waters may slow larval metabolism and development augmenting the great intrinsic ability for dispersal among many deep-sea species. Currents, environmental shifts, and topography can prove to be dispersal barriers but are often semipermeable. Evidence of historical events such as points of faunal origin and climatic fluctuations are also evident in contemporary biogeographic ranges. Continued synthetic analysis, database construction, theoretical advancement and field sampling will be required to further refine hypotheses regarding deep-sea biogeography.

The Evolution of Deep Ocean Chemistry and Respired Carbon in the Eastern Equatorial Pacific Over the Last Deglaciation

Science.gov (United States)

de la Fuente, Maria; Calvo, Eva; Skinner, Luke; Pelejero, Carles; Evans, David; Müller, Wolfgang; Povea, Patricia; Cacho, Isabel

2017-12-01

It has been shown that the deep Eastern Equatorial Pacific (EEP) region was poorly ventilated during the Last Glacial Maximum (LGM) relative to Holocene values. This finding suggests a more efficient biological pump, which indirectly supports the idea of increased carbon storage in the deep ocean contributing to lower atmospheric CO2 during the last glacial. However, proxies related to respired carbon are needed in order to directly test this proposition. Here we present Cibicides wuellerstorfi B/Ca ratios from Ocean Drilling Program Site 1240 measured by laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) as a proxy for deep water carbonate saturation state (Δ[CO32-], and therefore [CO32-]), along with δ13C measurements. In addition, the U/Ca ratio in foraminiferal coatings has been analyzed as an indicator of oxygenation changes. Our results show lower [CO32-], δ13C, and [O2] values during the LGM, which would be consistent with higher respired carbon levels in the deep EEP driven, at least in part, by reduced deep water ventilation. However, the difference between LGM and Holocene [CO32-] observed at our site is relatively small, in accordance with other records from across the Pacific, suggesting that a "counteracting" mechanism, such as seafloor carbonate dissolution, also played a role. If so, this mechanism would have increased average ocean alkalinity, allowing even more atmospheric CO2 to be "sequestered" by the ocean. Therefore, the deep Pacific Ocean very likely stored a significant amount of atmospheric CO2 during the LGM, specifically due to a more efficient biological carbon pump and also an increase in average ocean alkalinity.

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

Directory of Open Access Journals (Sweden)

Peter Christiansen

2016-11-01

Full Text Available Convolutional neural network (CNN-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN. In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m than RCNN. RCNN has a similar performance at a short range (0–30 m. However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms = a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit.

Challenging oil bioremediation at deep-sea hydrostatic pressure

Directory of Open Access Journals (Sweden)

Alberto Scoma

2016-08-01

Full Text Available The Deepwater Horizon (DWH accident has brought oil contamination of deep-sea environments to worldwide attention. The risk for new deep-sea spills is not expected to decrease in the future, as political pressure mounts to access deep-water fossil reserves, and poorly tested technologies are used to access oil. This also applies to the response to oil-contamination events, with bioremediation the only (biotechnology presently available to combat deep-sea spills. Many questions about the fate of petroleum-hydrocarbons at deep-sea remain unanswered, as much as the main constraints limiting bioremediation under increased hydrostatic pressures and low temperatures. The microbial pathways fueling oil take up are unclear, and the mild upregulation observed for beta-oxidation-related genes in both water and sediments contrasts with the high amount of alkanes present in the spilled-oil. The fate of solid alkanes (tar and that of hydrocarbons degradation rates was largely overlooked, as the reason why the most predominant hydrocarbonoclastic genera were not enriched at deep-sea, despite being present at hydrocarbon seeps at the Gulf of Mexico. This mini-review aims at highlighting the missing information in the field, proposing a holistic approach where in situ and ex situ studies are integrated to reveal the principal mechanisms accounting for deep-sea oil bioremediation.

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...

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.

The Next Era: Deep Learning in Pharmaceutical Research.

Science.gov (United States)

Ekins, Sean

2016-11-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Significant increase of surface ozone at a rural site, north of eastern China

Directory of Open Access Journals (Sweden)

Z. Ma

2016-03-01

Full Text Available Ozone pollution in eastern China has become one of the top environmental issues. Quantifying the temporal trend of surface ozone helps to assess the impacts of the anthropogenic precursor reductions and the likely effects of emission control strategies implemented. In this paper, ozone data collected at the Shangdianzi (SDZ regional atmospheric background station from 2003 to 2015 are presented and analyzed to obtain the variation in the trend of surface ozone in the most polluted region of China, north of eastern China or the North China Plain. A modified Kolmogorov–Zurbenko (KZ filter method was performed on the maximum daily average 8 h (MDA8 concentrations of ozone to separate the contributions of different factors from the variation of surface ozone and remove the influence of meteorological fluctuations on surface ozone. Results reveal that the short-term, seasonal and long-term components of ozone account for 36.4, 57.6 and 2.2 % of the total variance, respectively. The long-term trend indicates that the MDA8 has undergone a significant increase in the period of 2003–2015, with an average rate of 1.13 ± 0.01 ppb year−1 (R2 = 0.92. It is found that meteorological factors did not significantly influence the long-term variation of ozone and the increase may be completely attributed to changes in emissions. Furthermore, there is no significant correlation between the long-term O3 and NO2 trends. This study suggests that emission changes in VOCs might have played a more important role in the observed increase of surface ozone at SDZ.

Deep Visual Attention Prediction

Science.gov (United States)

Wang, Wenguan; Shen, Jianbing

2018-05-01

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

Genetic homogeneity in the deep-sea grenadier Macrourus berglax across the North Atlantic Ocean

Science.gov (United States)

Coscia, Ilaria; Castilho, Rita; Massa-Gallucci, Alexia; Sacchi, Carlotta; Cunha, Regina L.; Stefanni, Sergio; Helyar, Sarah J.; Knutsen, Halvor; Mariani, Stefano

2018-02-01

Paucity of data on population structure and connectivity in deep sea species remains a major obstacle to their sustainable management and conservation in the face of ever increasing fisheries pressure and other forms of impacts on deep sea ecosystems. The roughhead grenadier Macrourus berglax presents all the classical characteristics of a deep sea species, such as slow growth and low fecundity, which make them particularly vulnerable to anthropogenic impact, due to their low resilience to change. In this study, the population structure of the roughhead grenadier is investigated throughout its geographic distribution using two sets of molecular markers: a partial sequence of the Control Region of mitochondrial DNA and species-specific microsatellites. No evidence of significant structure was found throughout the North Atlantic, with both sets of molecular markers yielding the same results of overall homogeneity. We posit two non-mutually exclusive scenarios that can explain such outcome: i) substantial high gene flow among locations, possibly maintained by larval stages, ii) very large effective size of post-glacially expanded populations. The results can inform management strategies in this by-caught species, and contribute to the broader issue of biological connectivity in the deep ocean.

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

Environmental challenges of deep water activities

International Nuclear Information System (INIS)

Sande, Arvid

1998-01-01

In this presentation there are discussed the experiences of petroleum industry, and the projects that have been conducted in connection with the planning and drilling of the first deep water wells in Norway. There are also presented views on where to put more effort in the years to come, so as to increase the knowledge of deep water areas. Attention is laid on exploration drilling as this is the only activity with environmental potential that will take place during the next five years or so. The challenges for future field developments in these water depths are briefly discussed. 7 refs

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 ...

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.

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.

Studies of the reproductive biology of deep sea megabenthos VIII. Biochemical and calorific content of the reproductive organs of deep sea holothurians

International Nuclear Information System (INIS)

Tyler, P.A.; Walker, M.

1987-01-01

The data for protein, lipid, carbohydrate and ash content of the ovary, testes, gut and body wall of a variety of deep sea holothurians are presented. The dominant biochemical is insoluble protein in all tissues followed by lipid in the ovary. The ash content was lowest in the gonads and highest in the body wall of most species. The mean calorific content of the species studied is 25.08Jmg -1 thus representing a significant energy store in the deep sea. The data suggest active metabolic pathways in these species which may pass radionuclides to the developing gametes and after spawning to dispersal in deep waters. (author)

Effect of Extracorporeal Shock Wave Treatment on Deep Partial-Thickness Burn Injury in Rats: A Pilot Study

Directory of Open Access Journals (Sweden)

Gabriel Djedovic

2014-01-01

Full Text Available Extracorporeal shock wave therapy (ESWT enhances tissue vascularization and neoangiogenesis. Recent animal studies showed improved soft tissue regeneration using ESWT. In most cases, deep partial-thickness burns require skin grafting; the outcome is often unsatisfactory in function and aesthetic appearance. The aim of this study was to demonstrate the effect of ESWT on skin regeneration after deep partial-thickness burns. Under general anesthesia, two standardized deep partial-thickness burns were induced on the back of 30 male Wistar rats. Immediately after the burn, ESWT was given to rats of group 1 (N=15, but not to group 2 (N=15. On days 5, 10, and 15, five rats of each group were analyzed. Reepithelialization rate was defined, perfusion units were measured, and histological analysis was performed. Digital photography was used for visual documentation. A wound score system was used. ESWT enhanced the percentage of wound closure in group 1 as compared to group 2 (P<0.05. The reepithelialization rate was improved significantly on day 15 (P<0.05. The wound score showed a significant increase in the ESWT group. ESWT improves skin regeneration of deep partial-thickness burns in rats. It may be a suitable and cost effective treatment alternative in this type of burn wounds in the future.

Seasonal variation of deep-sea bioluminescence in the Ionian Sea

International Nuclear Information System (INIS)

Craig, Jessica; Jamieson, Alan J.; Bagley, Philip M.; Priede, Imants G.

2011-01-01

The ICDeep (Image Intensified Charge Coupled Device for Deep sea research) profiler was used to measure the density of deep bioluminescent animals (BL) through the water column in the east, west and mid-Ionian Sea and in the Algerian Basin. A west to east decrease in BL density was found. Generalized additive modelling was used to investigate seasonal variation in the east and west Ionian Sea (NESTOR and NEMO neutrino telescope sites, respectively) from BL measurements in autumn 2008 and spring 2009. A significant seasonal effect was found in the west Ionian Sea (p<0.001), where a deep autumnal peak in BL density occurred between 500 and 2400 m. No significant seasonal variation in BL density was found in the east Ionian Sea (p=0.07). In both spring and autumn, significant differences in BL density were found through the water column between the east and west Ionian Sea (p<0.001).

Seasonal variation of deep-sea bioluminescence in the Ionian Sea

Energy Technology Data Exchange (ETDEWEB)

Craig, Jessica, E-mail: j.craig@abdn.ac.u [University of Aberdeen, Oceanlab, Main Street, Newburgh, Aberdeenshire, AB41 6AA (United Kingdom); Jamieson, Alan J.; Bagley, Philip M.; Priede, Imants G. [University of Aberdeen, Oceanlab, Main Street, Newburgh, Aberdeenshire, AB41 6AA (United Kingdom)

2011-01-21

The ICDeep (Image Intensified Charge Coupled Device for Deep sea research) profiler was used to measure the density of deep bioluminescent animals (BL) through the water column in the east, west and mid-Ionian Sea and in the Algerian Basin. A west to east decrease in BL density was found. Generalized additive modelling was used to investigate seasonal variation in the east and west Ionian Sea (NESTOR and NEMO neutrino telescope sites, respectively) from BL measurements in autumn 2008 and spring 2009. A significant seasonal effect was found in the west Ionian Sea (p<0.001), where a deep autumnal peak in BL density occurred between 500 and 2400 m. No significant seasonal variation in BL density was found in the east Ionian Sea (p=0.07). In both spring and autumn, significant differences in BL density were found through the water column between the east and west Ionian Sea (p<0.001).

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

Evaluation of Deep Discount Fare Strategies

Science.gov (United States)

1995-08-01

This report evaluates the success of a fare pricing strategy known as deep discounting, that entails the bulk sale of transit tickets or tokens to customers at a significant discount compared to the full fare single ticket price. This market-driven s...

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/.

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 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 ...

Impact of proton irradiation on deep level states in n-GaN

International Nuclear Information System (INIS)

Zhang, Z.; Arehart, A. R.; Cinkilic, E.; Ringel, S. A.; Chen, J.; Zhang, E. X.; Fleetwood, D. M.; Schrimpf, R. D.; McSkimming, B.; Speck, J. S.

2013-01-01

Deep levels in 1.8 MeV proton irradiated n-type GaN were systematically characterized using deep level transient spectroscopies and deep level optical spectroscopies. The impacts of proton irradiation on the introduction and evolution of those deep states were revealed as a function of proton fluences up to 1.1 × 10 13 cm −2 . The proton irradiation introduced two traps with activation energies of E C - 0.13 eV and 0.16 eV, and a monotonic increase in the concentration for most of the pre-existing traps, though the increase rates were different for each trap, suggesting different physical sources and/or configurations for these states. Through lighted capacitance voltage measurements, the deep levels at E C - 1.25 eV, 2.50 eV, and 3.25 eV were identified as being the source of systematic carrier removal in proton-damaged n-GaN as a function of proton fluence

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 ...

Average multiplications in deep inelastic processes and their interpretation

International Nuclear Information System (INIS)

Kiselev, A.V.; Petrov, V.A.

1983-01-01

Inclusive production of hadrons in deep inelastic proceseseus is considered. It is shown that at high energies the jet evolution in deep inelastic processes is mainly of nonperturbative character. With the increase of a final hadron state energy the leading contribution to an average multiplicity comes from a parton subprocess due to production of massive quark and gluon jets and their further fragmentation as diquark contribution becomes less and less essential. The ratio of the total average multiplicity in deep inelastic processes to the average multiplicity in e + e - -annihilation at high energies tends to unity

An OSSE Study for Deep Argo Array using the GFDL Ensemble Coupled Data Assimilation System

Science.gov (United States)

Chang, You-Soon; Zhang, Shaoqing; Rosati, Anthony; Vecchi, Gabriel A.; Yang, Xiaosong

2018-03-01

An observing system simulation experiment (OSSE) using an ensemble coupled data assimilation system was designed to investigate the impact of deep ocean Argo profile assimilation in a biased numerical climate system. Based on the modern Argo observational array and an artificial extension to full depth, "observations" drawn from one coupled general circulation model (CM2.0) were assimilated into another model (CM2.1). Our results showed that coupled data assimilation with simultaneous atmospheric and oceanic constraints plays a significant role in preventing deep ocean drift. However, the extension of the Argo array to full depth did not significantly improve the quality of the oceanic climate estimation within the bias magnitude in the twin experiment. Even in the "identical" twin experiment for the deep Argo array from the same model (CM2.1) with the assimilation model, no significant changes were shown in the deep ocean, such as in the Atlantic meridional overturning circulation and the Antarctic bottom water cell. The small ensemble spread and corresponding weak constraints by the deep Argo profiles with medium spatial and temporal resolution may explain why the deep Argo profiles did not improve the deep ocean features in the assimilation system. Additional studies using different assimilation methods with improved spatial and temporal resolution of the deep Argo array are necessary in order to more thoroughly understand the impact of the deep Argo array on the assimilation system.

Assessment of pelvic floor muscles in women with deep endometriosis.

Science.gov (United States)

Dos Bispo, Ana Paula Santos; Ploger, Christine; Loureiro, Alessandra Fernandes; Sato, Hélio; Kolpeman, Alexander; Girão, Manoel João Batista Castello; Schor, Eduardo

2016-09-01

To assess function and prevalence of spasms and trigger points of the pelvic floor muscles in women with deep endometriosis. One hundred and four (104) patients were assessed. Group 1 (G1) was composed of 52 subjects diagnosed with deep endometriosis proven by magnetic resonance imaging (MRI); Group 2 (G2) was composed of 52 women with no signs of endometriosis. Subjects from both G1 and G2 were seen at the Division of Pelvic Pain and Endometriosis and at Center for Prevention of Sexually Transmitted Diseases, both at Federal University of São Paulo (UNIFESP), respectively. A full physical therapy evaluation was carried out, including medical history, presence of dyspareunia and physical examination, which included detailed evaluation of pelvic floor muscles and occurrence of muscle spasm, trigger point and muscle function. The average age of the subjects in the study group was 36.4 and 30.9 years in the control group (p = 0.002). A greater prevalence of deep dyspareunia was found in the subjects in the endometriosis group when compared to the control group (p = 0.010). Women in G1 had higher prevalence of muscle spasms. In this group, 53.9 % had spasms-compared to only 17.3 % of women in G2 (p < 0.001). On the other hand, no significant difference between the groups (p = 0.153) was found while searching for the presence of trigger points. Women with deep endometriosis have increased prevalence of pelvic floor muscle spasms when compared to the control group.

A study of ion implanted gallium arsenide using deep level transient spectroscopy

International Nuclear Information System (INIS)

Emerson, N.G.

1981-03-01

This thesis is concerned with the study of deep energy levels in ion implanted gallium arsenide (GaAs) using deep level transient spectroscopy (D.L.T.S.). The D.L.T.S. technique is used to characterise deep levels in terms of their activation energies and capture cross-sections and to determine their concentration profiles. The main objective is to characterise the effects on deep levels, of ion implantation and the related annealing processes. In the majority of cases assessment is carried out using Schottky barrier diodes. Low doses of selenium ions 1 to 3 x 10 12 cm -2 are implanted into vapour phase epitaxial (V.P.E.) GaAs and the effects of post-implantation thermal and pulsed laser annealing are compared. The process of oxygen implantation with doses in the range 1 x 10 12 to 5 x 10 13 cm -2 followed by thermal annealing at about 750 deg C, introduces a deep level at 0.79 eV from the conduction band. Oxygen implantation, at doses of 5 x 10 13 cm -2 , into V.P.E. GaAs produces a significant increase in the concentration of the A-centre (0.83 eV). High doses of zinc (10 15 cm -2 ) are implanted into n-type V.P.E. GaAs to form shallow p-type layers. The D.L.T.S. system described in the text is used to measure levels in the range 0.16 to 1.1 eV (for GaAs) with a sensitivity of the order 1:10 3 . (U.K.)

Redistribution of Decompression Stop Time from Shallow to Deep Stops Increases Incidence of Decompression Sickness in Air Decompression Dives

Science.gov (United States)

2011-07-22

year old active duty male diver surfaced from a 170/30 air dive at <corr>12:11<corr> on 24AUG06 using MK 20 FFM and following the A-2 “deep stops...effort, and this episode responded immediately to pressure. AGE is unlikely due to the experience of the diver, the MK 20 FFM characteristics, and...from a 170/30 air dive at <corr>12:11<corr> on 24AUG06 using MK 20 FFM and following the A-2 “deep stops” experimental decompression profile

Impact of operative time on early joint infection and deep vein thrombosis in primary total hip arthroplasty.

Science.gov (United States)

Wills, B W; Sheppard, E D; Smith, W R; Staggers, J R; Li, P; Shah, A; Lee, S R; Naranje, S M

2018-03-22

Infections and deep vein thrombosis (DVT) after total hip arthroplasty (THA) are challenging problems for both the patient and surgeon. Previous studies have identified numerous risk factors for infections and DVT after THA but have often been limited by sample size. We aimed to evaluate the effect of operative time on early postoperative infection as well as DVT rates following THA. We hypothesized that an increase in operative time would result in increased odds of acquiring an infection as well as a DVT. We conducted a retrospective analysis of prospectively collected data using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database from 2006 to 2015 for all patients undergoing primary THA. Associations between operative time and infection or DVT were evaluated with multivariable logistic regressions controlling for demographics and several known risks factors for infection. Three different types of infections were evaluated: (1) superficial surgical site infection (SSI), an infection involving the skin or subcutaneous tissue, (2) deep SSI, an infection involving the muscle or fascial layers beneath the subcutaneous tissue, and (3) organ/space infection, an infection involving any part of the anatomy manipulated during surgery other than the incisional components. In total, 103,044 patients who underwent THA were included in our study. Our results suggested a significant association between superficial SSIs and operative time. Specifically, the adjusted odds of suffering a superficial SSI increased by 6% (CI=1.04-1.08, ptime. When using dichotomized operative time (90minutes), the adjusted odds of suffering a superficial SSI was 56% higher for patients with prolonged operative time (CI=1.05-2.32, p=0.0277). The adjusted odds of suffering a deep SSI increased by 7% for every 10-minute increase in operative time (CI=1.01-1.14, p=0.0335). No significant associations were detected between organ/space infection, wound

Deep Ocean Contribution to Sea Level Rise

Science.gov (United States)

Chang, L.; Sun, W.; Tang, H.; Wang, Q.

2017-12-01

The ocean temperature and salinity change in the upper 2000m can be detected by Argo floats, so we can know the steric height change of the ocean. But the ocean layers above 2000m represent only 50% of the total ocean volume. Although the temperature and salinity change are small compared to the upper ocean, the deep ocean contribution to sea level might be significant because of its large volume. There has been some research on the deep ocean rely on the very sparse situ observation and are limited to decadal and longer-term rates of change. The available observational data in the deep ocean are too spares to determine the temporal variability, and the long-term changes may have a bias. We will use the Argo date and combine the situ data and topographic data to estimate the temperature and salinity of the sea water below 2000m, so we can obtain a monthly data. We will analyze the seasonal and annual change of the steric height change due to the deep ocean between 2005 and 2016. And we will evaluate the result combination the present-day satellite and in situ observing systems. The deep ocean contribution can be inferred indirectly as the difference between the altimetry minus GRACE and Argo-based steric sea level.

Effect of nano-silver hydrogel coating film on deep partial thickness scald model of rabbit.

Science.gov (United States)

Xi, Peng; Li, Yan; Ge, Xiaojin; Liu, Dandan; Miao, Mingsan

2018-05-01

Observing the effect of nano-silver hydrogel coating film on deep partial thickness scald model of rabbit. We prepared boiling water scalded rabbits with deep II degree scald models and applied high, medium and low doses of nano-silver hydrogel coating film for different time and area. Then we compared the difference of burned paper weight before administration and after administration model burns, burn local skin irritation points infection, skin crusting and scabs from the time, and the impact of local skin tissue morphology. Rabbits deep II degree burn model successful modeling; on day 12, 18, high, medium and low doses of nano-silver hydrogel coating film significantly reduced skin irritation of rabbits infected with the integral value ( P  film group significantly decreased skin irritation, infection integral value ( P  film significantly reduced film rabbits' scalded skin crusting time ( P  film on the deep partial thickness burns has a significant therapeutic effect; external use has a significant role in wound healing.

Theoretical basis for convective invigoration due to increased aerosol concentration

Directory of Open Access Journals (Sweden)

Z. J. Lebo

2011-06-01

Full Text Available The potential effects of increased aerosol loading on the development of deep convective clouds and resulting precipitation amounts are studied by employing the Weather Research and Forecasting (WRF model as a detailed high-resolution cloud resolving model (CRM with both detailed bulk and bin microphysics schemes. Both models include a physically-based activation scheme that incorporates a size-resolved aerosol population. We demonstrate that the aerosol-induced effect is controlled by the balance between latent heating and the increase in condensed water aloft, each having opposing effects on buoyancy. It is also shown that under polluted conditions, increases in the CCN number concentration reduce the cumulative precipitation due to the competition between the sedimentation and evaporation/sublimation timescales. The effect of an increase in the IN number concentration on the dynamics of deep convective clouds is small and the resulting decrease in domain-averaged cumulative precipitation is shown not to be statistically significant, but may act to suppress precipitation. It is also shown that even in the presence of a decrease in the domain-averaged cumulative precipitation, an increase in the precipitation variance, or in other words, andincrease in rainfall intensity, may be expected in more polluted environments, especially in moist environments.

A significant difference exists between the predictions based on the bin and bulk microphysics schemes of precipitation and the influence of aerosol perturbations on updraft velocity within the convective core. The bulk microphysics scheme shows little change in the latent heating rates due to an increase in the CCN number concentration, while the bin microphysics scheme demonstrates significant increases in the latent heating aloft with increasing CCN number concentration. This suggests that even a detailed two-bulk microphysics scheme, coupled to a detailed activation scheme, may not be

St. John's wort significantly increased the systemic exposure and toxicity of methotrexate in rats

Energy Technology Data Exchange (ETDEWEB)

Yang, Shih-Ying [Graduate Institute of Pharmaceutical Chemistry, China Medical University, Taichung, Taiwan (China); Juang, Shin-Hun [Graduate Institute of Pharmaceutical Chemistry, China Medical University, Taichung, Taiwan (China); Department of Medical Research, China Medical University Hospital, Taichung, Taiwan (China); Tsai, Shang-Yuan; Chao, Pei-Dawn Lee [School of Pharmacy, China Medical University, Taichung, Taiwan (China); Hou, Yu-Chi, E-mail: hou5133@gmail.com [School of Pharmacy, China Medical University, Taichung, Taiwan (China); Department of Medical Research, China Medical University Hospital, Taichung, Taiwan (China)

2012-08-15

St. John's wort (SJW, Hypericum perforatum) is one of the popular nutraceuticals for treating depression. Methotrexate (MTX) is an immunosuppressant with narrow therapeutic window. This study investigated the effect of SJW on MTX pharmacokinetics in rats. Rats were orally given MTX alone and coadministered with 300 and 150 mg/kg of SJW, and 25 mg/kg of diclofenac, respectively. Blood was withdrawn at specific time points and serum MTX concentrations were assayed by a specific monoclonal fluorescence polarization immunoassay method. The results showed that 300 mg/kg of SJW significantly increased the AUC{sub 0−t} and C{sub max} of MTX by 163% and 60%, respectively, and 150 mg/kg of SJW significantly increased the AUC{sub 0−t} of MTX by 55%. In addition, diclofenac enhanced the C{sub max} of MTX by 110%. The mortality of rats treated with SJW was higher than that of controls. In conclusion, coadministration of SJW significantly increased the systemic exposure and toxicity of MTX. The combined use of MTX with SJW would need to be with caution. -- Highlights: ► St. John's wort significantly increased the AUC{sub 0−t} and C{sub max} of methotrexate. ► Coadministration of St. John's wort increased the exposure and toxicity of methotrexate. ► The combined use of methotrexate with St. John's wort will need to be with caution.

Meso- and small-scale vertical motions in the deep Western Mediterranean

Energy Technology Data Exchange (ETDEWEB)

Haren, Hans van, E-mail: hans.van.haren@nioz.n [Royal Netherlands Institute for Sea Research (NIOZ), P.O. Box 59, 1790 AB Den Burg (Netherlands)

2011-01-21

Acoustic reflections on particles larger than a few mm are compared with optical background data of bioluminescence at the ANTARES neutrino telescope site in the deep North-western Mediterranean Sea. Periodic increases of these data are associated with increases in horizontal and downward vertical currents. The observations provide unique knowledge of some oceanographic processes in the Mediterranean. Several periodicities are distinguished: seasonal, with large increase during spring, 20-day, which is associated with a meandering continental boundary current, 1-17.6 h, evidencing deep internal waves.

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.

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

[Soil organic carbon mineralization of Black Locust forest in the deep soil layer of the hilly region of the Loess Plateau, China].

Science.gov (United States)

Ma, Xin-Xin; Xu, Ming-Xiang; Yang, Kai

2012-11-01

The deep soil layer (below 100 cm) stores considerable soil organic carbon (SOC). We can reveal its stability and provide the basis for certification of the deep soil carbon sinks by studying the SOC mineralization in the deep soil layer. With the shallow soil layer (0-100 cm) as control, the SOC mineralization under the condition (temperature 15 degrees C, the soil water content 8%) of Black Locust forest in the deep soil layer (100-400 cm) of the hilly region of the Loess Plateau was studied. The results showed that: (1) There was a downward trend in the total SOC mineralization with the increase of soil depth. The total SOC mineralization in the sub-deep soil (100-200 cm) and deep soil (200-400 cm) were equivalent to approximately 88.1% and 67.8% of that in the shallow layer (0-100 cm). (2) Throughout the carbon mineralization process, the same as the shallow soil, the sub-deep and deep soil can be divided into 3 stages. In the rapid decomposition phase, the ratio of the mineralization or organic carbon to the total mineralization in the sub-deep and deep layer (0-10 d) was approximately 50% of that in the shallow layer (0-17 d). In the slow decomposition phase, the ratio of organic carbon mineralization to total mineralization in the sub-deep, deep layer (11-45 d) was 150% of that in the shallow layer (18-45 d). There was no significant difference in this ratio among these three layers (46-62 d) in the relatively stable stage. (3) There was no significant difference (P > 0.05) in the mineralization rate of SOC among the shallow, sub-deep, deep layers. The stability of SOC in the deep soil layer (100-400 cm) was similar to that in the shallow soil layer and the SOC in the deep soil layer was also involved in the global carbon cycle. The change of SOC in the deep soil layer should be taken into account when estimating the effects of soil carbon sequestration in the Hilly Region of the Loess Plateau, China.

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.

Constraints on sea level during the Pliocene: Records from the deep Pacific Ocean

Science.gov (United States)

Woodard, S. C.; Rosenthal, Y.; Miller, K. G.; Wright, J. D.; Chiu, B. K.

2013-12-01

To reconstruct sea level during the transition from peak late Pliocene warmth (~3.15 Ma) to the onset of N. Hemisphere glaciation (~2.75 Ma), we generated high resolution stable isotope (δ18O, δ13C) and trace metal (Mg/Ca) records using benthic foraminifera, Uvigerina sp., from northwest Pacific ODP Site 1208 (3350 m water depth). During the peak late Pliocene warmth Mg/Ca-derived temperature records indicate deep Pacific interglacial temperatures were not significantly warmer (+0.6 ×0.8°C) than modern and glacial temperatures were near freezing similar to the LGM. In contrast, the deep N. Atlantic (Site 607) was apparently ~3°C warmer than the modern during both Pliocene glacial and interglacial periods (Sosdian and Rosenthal, 2009), based on the Mg/Ca of P. wuellerstorfi, which may be influenced by carbonate ion effect (Elderfield et al., 2009 and refs therein). δ18O records indicate a significant long-term increase in benthic δ18O in both the N. Atlantic and N. Pacific, although the rate of increase (Δδ18O) in the N. Atlantic is approximately 3x that of the N. Pacific (Site 1208), based on least squares regressions of all glacial-interglacial data. The discrepancy in the Δδ18O between the two basins is explained by Mg/Ca-derived temperature records. Results from Site 1208 show that the deep Pacific experienced no long-term cooling over the period 3.15-2.7 Ma when the deep N. Atlantic cooled by ~2.5°C on average. The relatively stable Pacific deep-water record provides the more reliable reconstructions of sea-level changes. From 3.15-2.7 Ma, Pacific δ18O data records an average increase of ~0.19× 0.08 per mil implying a sea level drop of 19 m × 8 m. After correcting the N. Atlantic record for temperature, we find the long term δ18O change from 3.15-2.7 Ma is ~0.23×0.1 per mil which equates to a peak of 23 m × 10 m. Our estimates are further corroborated by foraminiferal calcite δ18O recorded during Pliocene peak interglacials KM3 and G17. The �

Seizure Induced by Deep Transcranial Magnetic Stimulation in an Adolescent with Depression.

Science.gov (United States)

Cullen, Kathryn R; Jasberg, Suzanne; Nelson, Brent; Klimes-Dougan, Bonnie; Lim, Kelvin O; Croarkin, Paul E

2016-09-01

Deep transcranial magnetic stimulation (TMS) with an H-1 coil was recently approved by the U.S. Food and Drug Administration (U.S. FDA) for treatment-resistant depression (TRD) in adults. Studies assessing the safety and effectiveness of deep TMS in adolescent TRD are lacking. The purpose of this brief report is to provide a case history of an adolescent enrolled in an investigational deep TMS protocol. A case history is described of the first participant of a sham-controlled clinical trial who had a seizure in the course of deep TMS with parameter settings extrapolated from the adult studies that led to US FDA approval (H-1 coil, 120% target stimulation intensity, 18 Hz, 55 trains of 2-second duration, total 1980 pulses). The participant was a 17-year-old unmedicated female, with no significant medical history and no history of seizures or of drug or alcohol use. Brain magnetic resonance imaging showed no structural abnormalities. She initially received sham, which was well tolerated. During active treatment sessions, titration began at 85% of motor threshold (MT) and increased by 5% per day. Her weekly MT measurements were stable. On her first day of 120% MT (8th active treatment), during the 48th train, the participant had a generalized, tonic-clonic seizure that lasted 90 seconds and resolved spontaneously. She had an emergency medicine evaluation and was discharged home without anticonvulsant medications. There were no further seizures reported at a 6-month follow-up. We report a deep TMS-induced generalized tonic-clonic seizure in an adolescent with TRD participating in a clinical trial. Given the demonstrated benefits of deep TMS for adult TRD, research investigating its use in adolescents with TRD is an important area. However, in light of this experience, additional precautions for adolescents should be considered. We propose that further dose-finding investigations are needed to refine adolescent-specific parameters that may be safe and effective for

The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson's disease.

Science.gov (United States)

Tinkhauser, Gerd; Pogosyan, Alek; Little, Simon; Beudel, Martijn; Herz, Damian M; Tan, Huiling; Brown, Peter

2017-04-01

Adaptive deep brain stimulation uses feedback about the state of neural circuits to control stimulation rather than delivering fixed stimulation all the time, as currently performed. In patients with Parkinson's disease, elevations in beta activity (13-35 Hz) in the subthalamic nucleus have been demonstrated to correlate with clinical impairment and have provided the basis for feedback control in trials of adaptive deep brain stimulation. These pilot studies have suggested that adaptive deep brain stimulation may potentially be more effective, efficient and selective than conventional deep brain stimulation, implying mechanistic differences between the two approaches. Here we test the hypothesis that such differences arise through differential effects on the temporal dynamics of beta activity. The latter is not constantly increased in Parkinson's disease, but comes in bursts of different durations and amplitudes. We demonstrate that the amplitude of beta activity in the subthalamic nucleus increases in proportion to burst duration, consistent with progressively increasing synchronization. Effective adaptive deep brain stimulation truncated long beta bursts shifting the distribution of burst duration away from long duration with large amplitude towards short duration, lower amplitude bursts. Critically, bursts with shorter duration are negatively and bursts with longer duration positively correlated with the motor impairment off stimulation. Conventional deep brain stimulation did not change the distribution of burst durations. Although both adaptive and conventional deep brain stimulation suppressed mean beta activity amplitude compared to the unstimulated state, this was achieved by a selective effect on burst duration during adaptive deep brain stimulation, whereas conventional deep brain stimulation globally suppressed beta activity. We posit that the relatively selective effect of adaptive deep brain stimulation provides a rationale for why this approach could

The biomass of the deep-sea benthopelagic plankton

Science.gov (United States)

Wishner, K. F.

1980-04-01

Deep-sea benthopelagic plankton samples were collected with a specially designed opening-closing net system 10 to 100 m above the bottom in five different oceanic regions at depths from 1000 to 4700 m. Benthopelagic plankton biomasses decrease exponentially with depth. At 1000 m the biomass is about 1% that of the surface zooplankton, at 5000 m about 0.1%. Effects of differences in surface primary productivity on deep-sea plankton biomass are much less than the effect of depth and are detectable only in a few comparisons of extreme oceanic regions. The biomass at 10 m above the bottom is greater than that at 100 m above the bottom (in a three-sample comparison), which could be a consequence of an enriched near-bottom environment. The deep-sea plankton biomass in the Red Sea is anomalously low. This may be due to increased decomposition rates in the warm (22°C) deep Red Sea water, which prevent much detritus from reaching the deep sea. A model of organic carbon utilization in the benthic boundary layer (bottom 100 m), incorporating results from deep-sea sediment trap and respiration studies, indicates that the benthopelagic plankton use only a small amount of the organic carbon flux. A large fraction of the flux is unaccounted for by present estimates of benthic and benthopelagic respiration.

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.)

Advances in technology for the construction of deep-underground facilities

Energy Technology Data Exchange (ETDEWEB)

1987-12-31

The workshop was organized in order to address technological issues important to decisions regarding the feasibility of strategic options. The objectives of the workshop were to establish the current technological capabilities for deep-underground construction, to project those capabilities through the compressed schedule proposed for construction, and to identify promising directions for timely allocation of existing research and development resources. The earth has been used as a means of protection and safekeeping for many centuries. Recently, the thickness of the earth cover required for this purpose has been extended to the 2,000- to 3,000-ft range in structures contemplated for nuclear-waste disposal, energy storage, and strategic systems. For defensive missile basing, it is now perceived that the magnitude of the threat has increased through better delivery systems, larger payloads, and variable tactics of attack. Thus, depths of 3,000 to 8,000 ft are being considered seriously for such facilities. Moreover, it appears desirable that the facilities be operational (if not totally complete) for defensive purposes within a five-year construction schedule. Deep excavations such as mines are similar in many respects to nearsurface tunnels and caverns for transit, rail, sewer, water, hydroelectric, and highway projects. But the differences that do exist are significant. Major distinctions between shallow and deep construction derive from the stress fields and behavior of earth materials around the openings. Different methodologies are required to accommodate other variations resulting from increased depth, such as elevated temperatures, reduced capability for site exploration, and limited access during project execution. This report addresses these and other questions devoted to geotechnical characterization, design, construction, and excavation equipment.

Influence of growth temperature and temperature ramps on deep level defect incorporation in m-plane GaN

International Nuclear Information System (INIS)

Armstrong, A. M.; Kelchner, K.; Nakamura, S.; DenBaars, S. P.; Speck, J. S.

2013-01-01

The dependence of deep level defect incorporation in m-plane GaN films grown by metal-organic chemical vapor deposition on bulk m-plane GaN substrates as a function of growth temperature (T g ) and T g ramping method was investigated using deep level optical spectroscopy. Understanding the influence of T g on GaN deep level incorporation is important for InGaN/GaN multi-quantum well (MQW) light emitting diodes (LEDs) and laser diodes (LDs) because GaN quantum barrier (QB) layers are grown much colder than thin film GaN to accommodate InGaN QW growth. Deep level spectra of low T g (800 °C) GaN films grown under QB conditions were compared to deep level spectra of high T g (1150 °C) GaN. Reducing T g , increased the defect density significantly (>50×) through introduction of emergent deep level defects at 2.09 eV and 2.9 eV below the conduction band minimum. However, optimizing growth conditions during the temperature ramp when transitioning from high to low T g substantially reduced the density of these emergent deep levels by approximately 40%. The results suggest that it is important to consider the potential for non-radiative recombination in QBs of LED or LD active regions, and tailoring the transition from high T g GaN growth to active layer growth can mitigate such non-radiative channels

Computational analysis of transcranial magnetic stimulation in the presence of deep brain stimulation probes

Science.gov (United States)

Syeda, F.; Holloway, K.; El-Gendy, A. A.; Hadimani, R. L.

2017-05-01

Transcranial Magnetic Stimulation is an emerging non-invasive treatment for depression, Parkinson's disease, and a variety of other neurological disorders. Many Parkinson's patients receive the treatment known as Deep Brain Stimulation, but often require additional therapy for speech and swallowing impairment. Transcranial Magnetic Stimulation has been explored as a possible treatment by stimulating the mouth motor area of the brain. We have calculated induced electric field, magnetic field, and temperature distributions in the brain using finite element analysis and anatomically realistic heterogeneous head models fitted with Deep Brain Stimulation leads. A Figure of 8 coil, current of 5000 A, and frequency of 2.5 kHz are used as simulation parameters. Results suggest that Deep Brain Stimulation leads cause surrounding tissues to experience slightly increased E-field (Δ Emax =30 V/m), but not exceeding the nominal values induced in brain tissue by Transcranial Magnetic Stimulation without leads (215 V/m). The maximum temperature in the brain tissues surrounding leads did not change significantly from the normal human body temperature of 37 °C. Therefore, we ascertain that Transcranial Magnetic Stimulation in the mouth motor area may stimulate brain tissue surrounding Deep Brain Stimulation leads, but will not cause tissue damage.

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.

Deep levels in silicon–oxygen superlattices

International Nuclear Information System (INIS)

Simoen, E; Jayachandran, S; Delabie, A; Caymax, M; Heyns, M

2016-01-01

This work reports on the deep levels observed in Pt/Al 2 O 3 /p-type Si metal-oxide-semiconductor capacitors containing a silicon–oxygen superlattice (SL) by deep-level transient spectroscopy. It is shown that the presence of the SL gives rise to a broad band of hole traps occurring around the silicon mid gap, which is absent in reference samples with a silicon epitaxial layer. In addition, the density of states of the deep layers roughly scales with the number of SL periods for the as-deposited samples. Annealing in a forming gas atmosphere reduces the maximum concentration significantly, while the peak energy position shifts from close-to mid-gap towards the valence band edge. Based on the flat-band voltage shift of the Capacitance–Voltage characteristics it is inferred that positive charge is introduced by the oxygen atomic layers in the SL, indicating the donor nature of the underlying hole traps. In some cases, a minor peak associated with P b dangling bond centers at the Si/SiO 2 interface has been observed as well. (paper)

Surface Improvement of Shafts by Turn-Assisted Deep Cold Rolling Process

Directory of Open Access Journals (Sweden)

Prabhu Raghavendra

2016-01-01

Full Text Available It is well recognized that mechanical surface enhancement methods can significantly improve the characteristics of highly-stressed metallic components. Deep cold rolling is one of such technique which is particularly attractive since it is possible to generate, near the surface, deep compressive residual stresses and work hardened layers while retaining a relatively smooth surface finish. In this paper, the effect of turn-assisted deep cold rolling on AISI 4140 steel is examined, with emphasis on the residual stress state. Based on the X-ray diffraction measurements, it is found that turn-assisted deep cold rolling can be quite effective in retarding the initiation and initial propagation of fatigue cracks in AISI 4140 steel.

Biomagnification of persistent organic pollutants in a deep-sea, temperate food web.

Science.gov (United States)

Romero-Romero, Sonia; Herrero, Laura; Fernández, Mario; Gómara, Belén; Acuña, José Luis

2017-12-15

Polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs) and polychlorinated dibenzo-p-dioxins and -furans (PCDD/Fs) were measured in a temperate, deep-sea ecosystem, the Avilés submarine Canyon (AC; Cantabrian Sea, Southern Bay of Biscay). There was an increase of contaminant concentration with the trophic level of the organisms, as calculated from stable nitrogen isotope data (δ 15 N). Such biomagnification was only significant for the pelagic food web and its magnitude was highly dependent on the type of top predators included in the analysis. The trophic magnification factor (TMF) for PCB-153 in the pelagic food web (spanning four trophic levels) was 6.2 or 2.2, depending on whether homeotherm top predators (cetaceans and seabirds) were included or not in the analysis, respectively. Since body size is significantly correlated with δ 15 N, it can be used as a proxy to estimate trophic magnification, what can potentially lead to a simple and convenient method to calculate the TMF. In spite of their lower biomagnification, deep-sea fishes showed higher concentrations than their shallower counterparts, although those differences were not significant. In summary, the AC fauna exhibits contaminant levels comparable or lower than those reported in other systems. Copyright © 2017 Elsevier B.V. All rights reserved.

The potential significance of permafrost to the behaviour of a deep radioactive waste repository

International Nuclear Information System (INIS)

McEwen, T.; Marsily, G.de

1991-02-01

Permafrost is one of the scenarios that is being considered as part of the groundwater flow and transport modelling for the Project-90 assessment. It is included as one of the primary Features, Events and Processes (FEPs) which are being kept outside the Process System in the SKB/SKI scenario development project. There is a large amount of evidence that Sweden has suffered several cycles of permafrost development over the Quaternary, approximately the last 2My, and climatic predictions for the next hundred thousand years suggest that similar climatic cycling is likely to occur. The presence of permafrost could have important effects on the hydrogeological regime and could therefore be important in modifying the release and dispersion of radionuclides from a repository. The climatic conditions of permafrost would also influence radionuclide migration and accumulation in the biosphere and the associated radiation exposure of man. These biosphere aspects are not considered here but the implications for discharge into the biosphere are examined, including the abstraction of groundwater by man in permafrost regions. This report reviews the evidence relating to permafrost development and discusses the possible implications for the long-term safety of a deep repository. (78 refs.) (au)

Assessing Deep Sea Communities Through Seabed Imagery

Science.gov (United States)

Matkin, A. G.; Cross, K.; Milititsky, M.

2016-02-01

The deep sea still remains virtually unexplored. Human activity, such as oil and gas exploration and deep sea mining, is expanding further into the deep sea, increasing the need to survey and map extensive areas of this habitat in order to assess ecosystem health and value. The technology needed to explore this remote environment has been advancing. Seabed imagery can cover extensive areas of the seafloor and investigate areas where sampling with traditional coring methodologies is just not possible (e.g. cold water coral reefs). Remotely operated vehicles (ROVs) are an expensive option, so drop or towed camera systems can provide a more viable and affordable alternative, while still allowing for real-time control. Assessment of seabed imagery in terms of presence, abundance and density of particular species can be conducted by bringing together a variety of analytical tools for a holistic approach. Sixteen deep sea transects located offshore West Africa were investigated with a towed digital video telemetry system (DTS). Both digital stills and video footage were acquired. An extensive data set was obtained from over 13,000 usable photographs, allowing for characterisation of the different habitats present in terms of community composition and abundance. All observed fauna were identified to the lowest taxonomic level and enumerated when possible, with densities derived after the seabed area was calculated for each suitable photograph. This methodology allowed for consistent assessment of the different habitat types present, overcoming constraints, such as specific taxa that cannot be enumerated, such as sponges, corals or bryozoans, the presence of mobile and sessile species, or the level of taxonomic detail. Although this methodology will not enable a full characterisation of a deep sea community, in terms of species composition for instance, itt will allow a robust assessment of large areas of the deep sea in terms of sensitive habitats present and community

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 sedation during pneumatic reduction of intussusception.

Science.gov (United States)

Ilivitzki, Anat; Shtark, Luda Glozman; Arish, Karin; Engel, Ahuva

2012-05-01

Pneumatic reduction of intussusception under fluoroscopic guidance is a routine procedure. The unsedated child may resist the procedure, which may lengthen its duration and increase the radiation dose. We use deep sedation during the procedure to overcome these difficulties. The purpose of this study was to summarize our experience with deep sedation during fluoroscopic reduction of intussusception and assess the added value and complication rate of deep sedation. All children with intussusception who underwent pneumatic reduction in our hospital between January 2004 and June 2011 were included in this retrospective study. Anesthetists sedated the children using propofol. The fluoroscopic studies, ultrasound (US) studies and the childrens' charts were reviewed. One hundred thirty-one attempted reductions were performed in 119 children, of which 121 (92%) were successful and 10 (8%) failed. Two perforations (1.5%) occurred during attempted reduction. Average fluoroscopic time was 1.5 minutes. No complication to sedation was recorded. Deep sedation with propofol did not add any complication to the pneumatic reduction. The fluoroscopic time was short. The success rate of reduction was high,raising the possibility that sedation is beneficial, possibly by smooth muscle relaxation.

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....

Deep Space Control Challenges of the New Millennium

Science.gov (United States)

Bayard, David S.; Burdick, Garry M.

1999-01-01

The exploration of deep space presents a variety of significant control challenges. Long communication delays coupled with challenging new science objectives require high levels of system autonomy and increasingly demanding pointing and control capabilities. Historically, missions based on the use of a large single spacecraft have been successful and popular since the early days of NASA. However, these large spacecraft missions are currently being displaced by more frequent and more focused missions based on the use of smaller and less expensive spacecraft designs. This trend drives the need to design smart software and good algorithms which together with the miniaturization of control components will improve performance while replacing the heavier and more expensive hardware used in the past. NASA's future space exploration will also include mission types that have never been attempted before, posing significant challenges to the underlying control system. This includes controlled landing on small bodies (e.g., asteroids and comets), sample return missions (where samples are brought back from other planets), robotic exploration of planetary surfaces (e.g., intelligent rovers), high precision formation flying, and deep space optical interferometry, While the control of planetary spacecraft for traditional flyby and orbiter missions are based on well-understood methodologies, control approaches for many future missions will be fundamentally different. This paradigm shift will require completely new control system development approaches, system architectures, and much greater levels of system autonomy to meet expected performance in the presence of significant environmental disturbances, and plant uncertainties. This paper will trace the motivation for these changes and will layout the approach taken to meet the new challenges. Emerging missions will be used to explain and illustrate the need for these changes.

Molecular dynamics simulations of the Nip7 proteins from the marine deep- and shallow-water Pyrococcus species.

Science.gov (United States)

Medvedev, Kirill E; Alemasov, Nikolay A; Vorobjev, Yuri N; Boldyreva, Elena V; Kolchanov, Nikolay A; Afonnikov, Dmitry A

2014-10-15

The identification of the mechanisms of adaptation of protein structures to extreme environmental conditions is a challenging task of structural biology. We performed molecular dynamics (MD) simulations of the Nip7 protein involved in RNA processing from the shallow-water (P. furiosus) and the deep-water (P. abyssi) marine hyperthermophylic archaea at different temperatures (300 and 373 K) and pressures (0.1, 50 and 100 MPa). The aim was to disclose similarities and differences between the deep- and shallow-sea protein models at different temperatures and pressures. The current results demonstrate that the 3D models of the two proteins at all the examined values of pressures and temperatures are compact, stable and similar to the known crystal structure of the P. abyssi Nip7. The structural deviations and fluctuations in the polypeptide chain during the MD simulations were the most pronounced in the loop regions, their magnitude being larger for the C-terminal domain in both proteins. A number of highly mobile segments the protein globule presumably involved in protein-protein interactions were identified. Regions of the polypeptide chain with significant difference in conformational dynamics between the deep- and shallow-water proteins were identified. The results of our analysis demonstrated that in the examined ranges of temperatures and pressures, increase in temperature has a stronger effect on change in the dynamic properties of the protein globule than the increase in pressure. The conformational changes of both the deep- and shallow-sea protein models under increasing temperature and pressure are non-uniform. Our current results indicate that amino acid substitutions between shallow- and deep-water proteins only slightly affect overall stability of two proteins. Rather, they may affect the interactions of the Nip7 protein with its protein or RNA partners.

Should deep seabed mining be allowed?

NARCIS (Netherlands)

Kim, Rak

2017-01-01

Abstract Commercial interest in deep sea minerals in the area beyond the limits of national jurisdiction has rapidly increased in recent years. The International Seabed Authority has already given out 26 exploration contracts and it is currently in the process of developing the Mining Code for

Staging of cortical and deep grey matter functional connectivity changes in multiple sclerosis.

Science.gov (United States)

Meijer, Kim A; Eijlers, Anand J C; Geurts, Jeroen J G; Schoonheim, Menno M

2018-02-01

Functional connectivity is known to increase as well as decrease throughout the brain in multiple sclerosis (MS), which could represent different stages of the disease. In addition, functional connectivity changes could follow the atrophy pattern observed with disease progression, that is, moving from the deep grey matter towards the cortex. This study investigated when and where connectivity changes develop and explored their clinical and cognitive relevance across different MS stages. A cohort of 121 patients with early relapsing-remitting MS (RRMS), 122 with late RRMS and 53 with secondary progressive MS (SPMS) as well as 96 healthy controls underwent MRI and neuropsychological testing. Functional connectivity changes were investigated for (1) within deep grey matter connectivity, (2) connectivity between the deep grey matter and cortex and (3) within-cortex connectivity. A post hoc regional analysis was performed to identify which regions were driving the connectivity changes. Patients with late RRMS and SPMS showed increased connectivity of the deep grey matter, especially of the putamen and palladium, with other deep grey matter structures and with the cortex. Within-cortex connectivity was decreased, especially for temporal, occipital and frontal regions, but only in SPMS relative to early RRMS. Deep grey matter connectivity alterations were related to cognition and disability, whereas within-cortex connectivity was only related to disability. Increased connectivity of the deep grey matter became apparent in late RRMS and further increased in SPMS. The additive effect of cortical network degeneration, which was only seen in SPMS, may explain the sudden clinical deterioration characteristic to this phase of the disease. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Dispersion of deep-sea hydrothermal vent effluents and larvae by submesoscale and tidal currents

Science.gov (United States)

Vic, Clément; Gula, Jonathan; Roullet, Guillaume; Pradillon, Florence

2018-03-01

Deep-sea hydrothermal vents provide sources of geochemical materials that impact the global ocean heat and chemical budgets, and support complex biological communities. Vent effluents and larvae are dispersed and transported long distances by deep ocean currents, but these currents are largely undersampled and little is known about their variability. Submesoscale (0.1-10 km) currents are known to play an important role for the dispersion of biogeochemical materials in the ocean surface layer, but their impact for the dispersion in the deep ocean is unknown. Here, we use a series of nested regional oceanic numerical simulations with increasing resolution (from δx = 6 km to δx = 0.75 km) to investigate the structure and variability of highly-resolved deep currents over the Mid-Atlantic Ridge (MAR) and their role on the dispersion of the Lucky Strike hydrothermal vent effluents and larvae. We shed light on a submesoscale regime of oceanic turbulence over the MAR at 1500 m depth, contrasting with open-ocean - i.e., far from topographic features - regimes of turbulence, dominated by mesoscales. Impacts of submesoscale and tidal currents on larval dispersion and connectivity among vent populations are investigated by releasing neutrally buoyant Lagrangian particles at the Lucky Strike hydrothermal vent. Although the absolute dispersion is overall not sensitive to the model resolution, submesoscale currents are found to significantly increase both the horizontal and vertical relative dispersion of particles at O(1-10) km and O(1-10) days, resulting in an increased mixing of the cloud of particles. A fraction of particles are trapped in submesoscale coherent vortices, which enable transport over long time and distances. Tidal currents and internal tides do not significantly impact the horizontal relative dispersion. However, they roughly double the vertical dispersion. Specifically, particles undergo strong tidally-induced mixing close to rough topographic features

Ecosystem function and services provided by the deep sea

Science.gov (United States)

Thurber, A. R.; Sweetman, A. K.; Narayanaswamy, B. E.; Jones, D. O. B.; Ingels, J.; Hansman, R. L.

2014-07-01

The deep sea is often viewed as a vast, dark, remote, and inhospitable environment, yet the deep ocean and seafloor are crucial to our lives through the services that they provide. Our understanding of how the deep sea functions remains limited, but when treated synoptically, a diversity of supporting, provisioning, regulating and cultural services becomes apparent. The biological pump transports carbon from the atmosphere into deep-ocean water masses that are separated over prolonged periods, reducing the impact of anthropogenic carbon release. Microbial oxidation of methane keeps another potent greenhouse gas out of the atmosphere while trapping carbon in authigenic carbonates. Nutrient regeneration by all faunal size classes provides the elements necessary for fueling surface productivity and fisheries, and microbial processes detoxify a diversity of compounds. Each of these processes occur on a very small scale, yet considering the vast area over which they occur they become important for the global functioning of the ocean. The deep sea also provides a wealth of resources, including fish stocks, enormous bioprospecting potential, and elements and energy reserves that are currently being extracted and will be increasingly important in the near future. Society benefits from the intrigue and mystery, the strange life forms, and the great unknown that has acted as a muse for inspiration and imagination since near the beginning of civilization. While many functions occur on the scale of microns to meters and timescales up to years, the derived services that result are only useful after centuries of integrated activity. This vast dark habitat, which covers the majority of the globe, harbors processes that directly impact humans in a variety of ways; however, the same traits that differentiate it from terrestrial or shallow marine systems also result in a greater need for integrated spatial and temporal understanding as it experiences increased use by society. In

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

Science.gov (United States)

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

How safe is deep sedation or general anesthesia while providing dental care?

Science.gov (United States)

Bennett, Jeffrey D; Kramer, Kyle J; Bosack, Robert C

2015-09-01

Deep sedation and general anesthesia are administered daily in dental offices, most commonly by oral and maxillofacial surgeons and dentist anesthesiologists. The goal of deep sedation or general anesthesia is to establish a safe environment in which the patient is comfortable and cooperative. This requires meticulous care in which the practitioner balances the patient's depth of sedation and level of responsiveness while maintaining airway integrity, ventilation, and cardiovascular hemodynamics. Using the available data and informational reports, the authors estimate that the incidence of death and brain injury associated with deep sedation or general anesthesia administered by all dentists most likely exceeds 1 per month. Airway compromise is a significant contributing factor to anesthetic complications. The American Society of Anesthesiology closed claim analysis also concluded that human error contributed highly to anesthetic mishaps. The establishment of a patient safety database for anesthetic management in dentistry would allow for a more complete assessment of morbidity and mortality that could direct efforts to further increase safe anesthetic care. Deep sedation and general anesthesia can be safely administered in the dental office. Optimization of patient care requires appropriate patient selection, selection of appropriate anesthetic agents, utilization of appropriate monitoring, and a highly trained anesthetic team. Achieving a highly trained anesthetic team requires emergency management preparation that can foster decision making, leadership, communication, and task management. Copyright © 2015 American Dental Association. Published by Elsevier Inc. All rights reserved.

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 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.

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.

Studies on the deep-level defects in CdZnTe crystals grown by travelling heater method

Energy Technology Data Exchange (ETDEWEB)

Zhou, Boru; Jie, Wanqi; Wang, Tao; Xu, Lingyan; Yang, Fan; Yin, Liying; Fu, Xu [State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi' an (China); Key Laboratory of Radiation Detection Materials and Devices, Ministry of Industry and Information Technology, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi' an, Shaanxi (China); Nan, Ruihua [State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi' an (China); Shaanxi Key Laboratory of Optoelectronic Functional Materials and Devices, School of Materials and Chemical Engineering, Xi' an Technological University, Xi' an (China)

2017-05-15

The variation of deep level defects along the axis of CZT:In ingots grown by Travelling Heater Method was investigated by the means of thermally stimulated current (TSC) spectra. Models for the reaction among different defects In, Te{sub i}, and V{sub Cd} were used to analyze the variation of deep level defects along the growth direction. It was found that the density of In dopant-related defects is lower in the tip, but those of Te antisites and Te interstitials are higher in the tip. The density of cadmium vacancy exhibits an initial increase followed by a decrease from the tip to tail of the ingot. In PL spectra, the intensities of (D{sub 0}, X), (DAP) and D{sub complex} peaks obviously increase from the tip to the tail, due to the increase of the density of In dopant-related defects (IN{sup +}{sub CD}), Cd vacancies, and impurities. The low concentration of net free holes was found by Hall measurements, and high resistivity with p-type conduction was demonstrated from I-V analysis. The mobility for electrons was found to increase significantly from 634 ± 26 cm{sup 2} V{sup -1} s{sup -1} in the tip to 860 ± 10 cm{sup 2} V{sup -1} s{sup -1} in the tail, due to the decrease of the deep level defect densities. (copyright 2017 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

Deep neural networks to enable real-time multimessenger astrophysics

Science.gov (United States)

George, Daniel; Huerta, E. A.

2018-02-01

Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field of research, there is a pressing need to increase the depth and speed of the algorithms used to enable these ground-breaking discoveries. We introduce Deep Filtering—a new scalable machine learning method for end-to-end time-series signal processing. Deep Filtering is based on deep learning with two deep convolutional neural networks, which are designed for classification and regression, to detect gravitational wave signals in highly noisy time-series data streams and also estimate the parameters of their sources in real time. Acknowledging that some of the most sensitive algorithms for the detection of gravitational waves are based on implementations of matched filtering, and that a matched filter is the optimal linear filter in Gaussian noise, the application of Deep Filtering using whitened signals in Gaussian noise is investigated in this foundational article. The results indicate that Deep Filtering outperforms conventional machine learning techniques, achieves similar performance compared to matched filtering, while being several orders of magnitude faster, allowing real-time signal processing with minimal resources. Furthermore, we demonstrate that Deep Filtering can detect and characterize waveform signals emitted from new classes of eccentric or spin-precessing binary black holes, even when trained with data sets of only quasicircular binary black hole waveforms. The results presented in this article, and the recent use of deep neural networks for the identification of optical transients in telescope data, suggests that deep learning can facilitate real-time searches of gravitational wave sources and their electromagnetic and astroparticle counterparts. In the subsequent article, the framework introduced herein is directly applied to identify and characterize gravitational wave events in real LIGO data.

Simulation of deep ventilation in Crater Lake, Oregon, 1951–2099

Science.gov (United States)

Wood, Tamara M.; Wherry, Susan A.; Piccolroaz, Sebastiano; Girdner, Scott F

2016-05-04

The frequency of deep ventilation events in Crater Lake, a caldera lake in the Oregon Cascade Mountains, was simulated in six future climate scenarios, using a 1-dimensional deep ventilation model (1DDV) that was developed to simulate the ventilation of deep water initiated by reverse stratification and subsequent thermobaric instability. The model was calibrated and validated with lake temperature data collected from 1994 to 2011. Wind and air temperature data from three general circulation models and two representative concentration pathways were used to simulate the change in lake temperature and the frequency of deep ventilation events in possible future climates. The lumped model air2water was used to project lake surface temperature, a required boundary condition for the lake model, based on air temperature in the future climates.The 1DDV model was used to simulate daily water temperature profiles through 2099. All future climate scenarios projected increased water temperature throughout the water column and a substantive reduction in the frequency of deep ventilation events. The least extreme scenario projected the frequency of deep ventilation events to decrease from about 1 in 2 years in current conditions to about 1 in 3 years by 2100. The most extreme scenario considered projected the frequency of deep ventilation events to be about 1 in 7.7 years by 2100. All scenarios predicted that the temperature of the entire water column will be greater than 4 °C for increasing lengths of time in the future and that the conditions required for thermobaric instability induced mixing will become rare or non-existent.The disruption of deep ventilation by itself does not provide a complete picture of the potential ecological and water quality consequences of warming climate to Crater Lake. Estimating the effect of warming climate on deep water oxygen depletion and water clarity will require careful modeling studies to combine the physical mixing processes affected by

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.

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

Nile damming as plausible cause of extinction and drop in abundance of deep-sea shrimp in the western Mediterranean over broad spatial scales

Science.gov (United States)

Cartes, J. E.; Maynou, F.; Fanelli, E.

2011-11-01

Greatly increased retention of flow in Nile River reservoirs was initiated in 1964, after completion of the Aswan High Dam, which induced important oceanographic changes in the Mediterranean Sea, including deep waters (below a depth of 150 m). Based on an analysis of data series starting in the 1940s/1950s, the giant red shrimp Aristaeomorpha foliacea has become locally extinct off of the Catalonian coasts (and elsewhere in the northwestern Mediterranean) at depths of 400-900 m, with a simultaneous and significant drop in the catches of red shrimp, Aristeus antennatus, in the second half of the 1960s. The extinction and sharp decline of deep-shrimp populations off Catalonian coast (at ca. 3200 km westwards from Nile Delta) followed the 1964 drop in Nile discharge with a delay of ca. 3-5 yrs (breakpoint analysis applied to data series). The breakpoints detected in the second half of 1960s both in Nile runoff and shrimps’ abundance were independent of climatic events in the study area (e.g. changes in NAO) and occurred before the increase in fishing effort off Catalonian coasts (breakpoint in 1973-1974). The Levantine Intermediate Water (LIW), inhabited by A. foliacea in the western Basin, had significant temperature (T) and salinity (S) increases in the 1950-1970 period, and Nile damming has contributed about 45% of the total S increase of Western Mediterranean deep-water masses from the 1960s to the late 1990s (Skliris and Lascaratos, 2004). This had to increase, for instance, LIW salinity at its formation site in the eastern Mediterranean. Nile damming was probably a triggering factor for the extinction/drop in abundance of deep-sea shrimp off Catalonian coasts.

The effect of 8 weeks deep-aquatic exercises on static balance and lower body strength among elderly men

Directory of Open Access Journals (Sweden)

Ehsan Seyed jafari

2017-04-01

Full Text Available Back ground: The purpose of this study was to investigate the effect of deep aquatic exercises on lower body strength and balance among elderly men. Methods: Thirty elderly men over 65 years old were randomly divided into two equal groups including experimental and control groups. Experimental group participated in a deep aquatic exercise program that consisted of 60-minute sessions three times a week for 8 weeks while control group had no plan of exercise.  Muscle strength and balance was assessed before and after the program as pre and post-test by HHD (Hand-Held Dynamometer and BBS (Biodex Balance System respectively. Repeated measures two-way analysis of variance (ANOVA was performed on outcome variables.(p≥0.05.  Results: deep aquatic exercises promoted significant increases in the elderly men's muscle strength and balance, as assessed using HHD (p< 0.001 and the BBS (p< 0.001. Conclusion: The present deep-aquatic exercise training for the elderly are able to improve the muscle strength and static balance.

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.

The Deep Structure of Organizational Online Networking

DEFF Research Database (Denmark)

Trier, Matthias; Richter, Alexander

2015-01-01

While research on organizational online networking recently increased significantly, most studies adopt quantitative research designs with a focus on the consequences of social network configurations. Very limited attention is paid to comprehensive theoretical conceptions of the complex phenomenon...... of organizational online networking. We address this gap by adopting a theoretical framework of the deep structure of organizational online networking with a focus on their emerging meaning for the employees. We apply and assess the framework in a qualitative case study of a large-scale implementation...... of a corporate social network site (SNS) in a global organization. We reveal organizational online networking as a multi-dimensional phenomenon with multiplex relationships that are unbalanced, primarily consist of weak ties and are subject to temporal change. Further, we identify discourse drivers...

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 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.

Focused Crawling of the Deep Web Using Service Class Descriptions

Energy Technology Data Exchange (ETDEWEB)

Rocco, D; Liu, L; Critchlow, T

2004-06-21

Dynamic Web data sources--sometimes known collectively as the Deep Web--increase the utility of the Web by providing intuitive access to data repositories anywhere that Web access is available. Deep Web services provide access to real-time information, like entertainment event listings, or present a Web interface to large databases or other data repositories. Recent studies suggest that the size and growth rate of the dynamic Web greatly exceed that of the static Web, yet dynamic content is often ignored by existing search engine indexers owing to the technical challenges that arise when attempting to search the Deep Web. To address these challenges, we present DynaBot, a service-centric crawler for discovering and clustering Deep Web sources offering dynamic content. DynaBot has three unique characteristics. First, DynaBot utilizes a service class model of the Web implemented through the construction of service class descriptions (SCDs). Second, DynaBot employs a modular, self-tuning system architecture for focused crawling of the DeepWeb using service class descriptions. Third, DynaBot incorporates methods and algorithms for efficient probing of the Deep Web and for discovering and clustering Deep Web sources and services through SCD-based service matching analysis. Our experimental results demonstrate the effectiveness of the service class discovery, probing, and matching algorithms and suggest techniques for efficiently managing service discovery in the face of the immense scale of the Deep Web.

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)

TREATMENT OF DEEP PERIPROSTHETIC INFECTION OF KNEE JOINT

Directory of Open Access Journals (Sweden)

Ivantsov V. A.

2018-03-01

Full Text Available In connection with the increase in arthroplasty of joints, the problem of infectious complications becomes topical. The aim of the study was to increase the effectiveness of purulent complications treatment after total knee arthroplasty. Material and methods. Treatment of patients with deep periprosthetic infection of the knee joint dealt with surgical debridement with the preservation of endoprosthesis, its removal and placement of a cement spacer or with the removal of endoprosthesis and arthrodesis of the knee joint. Surgical debridement was related to the radical excision of necrotic tissues and the remains of the synovial membrane. To prepare the cement spacer bone cement "CEMFIX" or "GENTAFIX" impregnated with antibiotic was used. For arthrodesis of the knee joint, Medbiotech core apparatus (the Republic of Belarus superimposed on the limb in the frontal and sagittal planes was applied. Results. The use of a differentiated and individual approach to the treatment of deep periprosthetic infection of the knee joint enabled to obtain positive results in 85.6% of cases. Conclusions. The two-stage method of treatment of deep periprosthetic infection of the knee joint is preferred, as compared to a one-stage method, which enables to obtain better results.

Immediate versus delayed intramedullary nailing for open fractures of the tibial shaft: a multivariate analysis of factors affecting deep infection and fracture healing.

Science.gov (United States)

Yokoyama, Kazuhiko; Itoman, Moritoshi; Uchino, Masataka; Fukushima, Kensuke; Nitta, Hiroshi; Kojima, Yoshiaki

2008-10-01

The purpose of this study was to evaluate contributing factors affecting deep infection and fracture healing of open tibia fractures treated with locked intramedullary nailing (IMN) by multivariate analysis. We examined 99 open tibial fractures (98 patients) treated with immediate or delayed locked IMN in static fashion from 1991 to 2002. Multivariate analyses following univariate analyses were derived to determine predictors of deep infection, nonunion, and healing time to union. The following predictive variables of deep infection were selected for analysis: age, sex, Gustilo type, fracture grade by AO type, fracture location, timing or method of IMN, reamed or unreamed nailing, debridement time (6 h), method of soft-tissue management, skin closure time (1 week), existence of polytrauma (ISS or =18), existence of floating knee injury, and existence of superficial/pin site infection. The predictive variables of nonunion selected for analysis was the same as those for deep infection, with the addition of deep infection for exchange of pin site infection. The predictive variables of union time selected for analysis was the same as those for nonunion, excluding of location, debridement time, and existence of floating knee and superficial infection. Six (6.1%; type II Gustilo n=1, type IIIB Gustilo n=5) of the 99 open tibial fractures developed deep infections. Multivariate analysis revealed that timing or method of IMN, debridement time, method of soft-tissue management, and existence of superficial or pin site infection significantly correlated with the occurrence of deep infection (Prate in type IIIB + IIIC was significantly higher than those in type I + II and IIIA (P = 0.016). Nonunion occurred in 17 fractures (20.3%, 17/84). Multivariate analysis revealed that Gustilo type, skin closure time, and existence of deep infection significantly correlated with occurrence of nonunion (P < 0.05). Gustilo type and existence of deep infection were significantly correlated

Evaluation of the effects of honey on acute-phase deep burn wounds.

Science.gov (United States)

Nakajima, Yukari; Mukai, Kanae; Nasruddin; Komatsu, Emi; Iuchi, Terumi; Kitayama, Yukie; Sugama, Junko; Nakatani, Toshio

2013-01-01

This study aimed to clarify the effects of honey on acute-phase deep burn wounds. Two deep burn wounds were created on mice which were divided into four groups: no treatment, silver sulfadiazine, manuka honey, and Japanese acacia honey. Wound sizes were calculated as expanded wound areas and sampled 30 minutes and 1-4 days after wounding for histological observation. The wound sections were subjected to hematoxylin and eosin and immunohistological staining to detect necrotic cells, apoptotic cells, neutrophils, and macrophages. The no treatment group formed a scar. The redness around the wound edges in the silver sulfadiazine group was the most intense. All groups exhibited increased wound areas after wounding. The proportions of necrotic cells and the numbers of neutrophils in the manuka and acacia honey groups were lower than those in the no treatment and silver sulfadiazine groups until day 3; however, there were no significant differences between all groups on day 4. These results show that honey treatment on deep burn wounds cannot prevent wound progression. Moreover, comparing our observations with those of Jackson, there are some differences between humans and animals in this regard, and the zone of hyperemia and its surrounding area fall into necrosis, which contributes to burn wound progression.

Evaluation of the Effects of Honey on Acute-Phase Deep Burn Wounds

Directory of Open Access Journals (Sweden)

Yukari Nakajima

2013-01-01

Full Text Available This study aimed to clarify the effects of honey on acute-phase deep burn wounds. Two deep burn wounds were created on mice which were divided into four groups: no treatment, silver sulfadiazine, manuka honey, and Japanese acacia honey. Wound sizes were calculated as expanded wound areas and sampled 30 minutes and 1–4 days after wounding for histological observation. The wound sections were subjected to hematoxylin and eosin and immunohistological staining to detect necrotic cells, apoptotic cells, neutrophils, and macrophages. The no treatment group formed a scar. The redness around the wound edges in the silver sulfadiazine group was the most intense. All groups exhibited increased wound areas after wounding. The proportions of necrotic cells and the numbers of neutrophils in the manuka and acacia honey groups were lower than those in the no treatment and silver sulfadiazine groups until day 3; however, there were no significant differences between all groups on day 4. These results show that honey treatment on deep burn wounds cannot prevent wound progression. Moreover, comparing our observations with those of Jackson, there are some differences between humans and animals in this regard, and the zone of hyperemia and its surrounding area fall into necrosis, which contributes to burn wound progression.

Deep and shallow water effects on developing preschoolers' aquatic skills.

Science.gov (United States)

Costa, Aldo M; Marinho, Daniel A; Rocha, Helena; Silva, António J; Barbosa, Tiago M; Ferreira, Sandra S; Martins, Marta

2012-05-01

The aim of the study was to assess deep and shallow water teaching methods in swimming lessons for preschool children and identify variations in the basic aquatic skills acquired. The study sample included 32 swimming instructors (16 from deep water programs and 16 from shallow water programs) and 98 preschool children (50 from deep water swimming pool and 48 from shallow water swimming pool). The children were also studied regarding their previous experience in swimming (6, 12 and 18 months or practice). Chi-Square test and Fisher's exact test were used to compare the teaching methodology. A discriminant analysis was conducted with Λ wilk's method to predict under what conditions students are better or worse (aquatic competence). Results suggest that regardless of the non-significant variations found in teaching methods, the water depth can affect aquatic skill acquisition - shallow water lessons seem to impose greater water competence particularly after 6 months of practice. The discriminant function revealed a significant association between groups and all predictors for 6 months of swimming practice (pdeep and shallow water programs for preschoolers is not significantly different. However, shallow water lessons could be preferable for the development of basic aquatic skills.

Deep-brain-stimulation does not impair deglutition in Parkinson's disease.

Science.gov (United States)

Lengerer, Sabrina; Kipping, Judy; Rommel, Natalie; Weiss, Daniel; Breit, Sorin; Gasser, Thomas; Plewnia, Christian; Krüger, Rejko; Wächter, Tobias

2012-08-01

A large proportion of patients with Parkinson's disease develop dysphagia during the course of the disease. Dysphagia in Parkinson's disease affects different phases of deglutition, has a strong impact on quality of life and may cause severe complications, i.e., aspirational pneumonia. So far, little is known on how deep-brain-stimulation of the subthalamic nucleus influences deglutition in PD. Videofluoroscopic swallowing studies on 18 patients with Parkinson's disease, which had been performed preoperatively, and postoperatively with deep-brain-stimulation-on and deep-brain-stimulation-off, were analyzed retrospectively. The patients were examined in each condition with three consistencies (viscous, fluid and solid). The 'New Zealand index for multidisciplinary evaluation of swallowing (NZIMES) Subscale One' for qualitative and 'Logemann-MBS-Parameters' for quantitative evaluation were assessed. Preoperatively, none of the patients presented with clinically relevant signs of dysphagia. While postoperatively, the mean daily levodopa equivalent dosage was reduced by 50% and deep-brain-stimulation led to a 50% improvement in motor symptoms measured by the UPDRS III, no clinically relevant influence of deep-brain-stimulation-on swallowing was observed using qualitative parameters (NZIMES). However quantitative parameters (Logemann scale) found significant changes of pharyngeal parameters with deep-brain-stimulation-on as compared to preoperative condition and deep-brain-stimulation-off mostly with fluid consistency. In Parkinson patients without dysphagia deep-brain-stimulation of the subthalamic nucleus modulates the pharyngeal deglutition phase but has no clinically relevant influence on deglutition. Further studies are needed to test if deep-brain-stimulation is a therapeutic option for patients with swallowing disorders. Copyright © 2012 Elsevier Ltd. All rights reserved.

Effect of nano-silver hydrogel coating film on deep partial thickness scald model of rabbit

Directory of Open Access Journals (Sweden)

Peng Xi

2018-05-01

Full Text Available Objective: Observing the effect of nano-silver hydrogel coating film on deep partial thickness scald model of rabbit. Method: We prepared boiling water scalded rabbits with deep II degree scald models and applied high, medium and low doses of nano-silver hydrogel coating film for different time and area. Then we compared the difference of burned paper weight before administration and after administration model burns, burn local skin irritation points infection, skin crusting and scabs from the time, and the impact of local skin tissue morphology. Result: Rabbits deep II degree burn model successful modeling; on day 12, 18, high, medium and low doses of nano-silver hydrogel coating film significantly reduced skin irritation of rabbits infected with the integral value (P < 0.01, P < 0.05; high, medium and low doses of nano-silver hydrogel coating film group significantly decreased skin irritation, infection integral value (P < 0.01, P < 0.05; high, medium and low doses of nano-silver hydrogel coating film significantly reduced film rabbits’ scalded skin crusting time (P < 0.01, significantly shortened the rabbit skin burns from the scab time (P < 0.01, and significantly improved the treatment of skin diseases in rabbits scald model change (P < 0.01, P < 0.05. Conclusion: The nano-silver hydrogel coating film on the deep partial thickness burns has a significant therapeutic effect; external use has a significant role in wound healing. Keywords: Nano-silver hydrogel coating film, Deep degree burns, Topical, Rabbits

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.

Resting state cortical oscillations of patients with Parkinson disease and with and without subthalamic deep brain stimulation: a magnetoencephalography study.

Science.gov (United States)

Cao, Chunyan; Li, Dianyou; Jiang, Tianxiao; Ince, Nuri Firat; Zhan, Shikun; Zhang, Jing; Sha, Zhiyi; Sun, Bomin

2015-04-01

In this study, we investigate the modification to cortical oscillations of patients with Parkinson disease (PD) by subthalamic deep brain stimulation (STN-DBS). Spontaneous cortical oscillations of patients with PD were recorded with magnetoencephalography during on and off subthalamic nucleus deep brain stimulation states. Several features such as average frequency, average power, and relative subband power in regions of interest were extracted in the frequency domain, and these features were correlated with Unified Parkinson Disease Rating Scale III evaluation. The same features were also investigated in patients with PD without surgery and healthy controls. Patients with Parkinson disease without surgery compared with healthy controls had a significantly lower average frequency and an increased average power in 1 to 48 Hz range in whole cortex. Higher relative power in theta and simultaneous decrease in beta and gamma over temporal and occipital were also observed in patients with PD. The Unified Parkinson Disease Rating Scale III rigidity score correlated with the average frequency and with the relative power of beta and gamma in frontal areas. During subthalamic nucleus deep brain stimulation, the average frequency increased significantly when stimulation was on compared with off state. In addition, the relative power dropped in delta, whereas it rose in beta over the whole cortex. Through the course of stimulation, the Unified Parkinson Disease Rating Scale III rigidity and tremor scores correlated with the relative power of alpha over left parietal. Subthalamic nucleus deep brain stimulation improves the symptoms of PD by suppressing the synchronization of alpha rhythm in somatomotor region.

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/ .

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.

Treatment of deep mycoses with liposomal amphotericin B.

Science.gov (United States)

Berenguer, J; Muñoz, P; Parras, F; Fernández-Baca, V; Hernández-Sampelayo, T; Bouza, E

1994-06-01

Amphotericin B is the mainstay of therapy of many deep mycoses, but its use is seriously hampered by dose-limiting nephrotoxicity. In this study a liposomal formulation of amphotericin B was administered to ten patients with proven deep mycoses: invasive aspergillosis (n = 4), deep candidiasis (n = 4) and zygomycosis (n = 2). The mean daily dosage of liposomal amphotericin B was 3.0 mg/kg (range 2.5 to 4 mg/kg), the mean total dosage of liposomal amphotericin B 2,781 mg (range 87 to 5,220 mg) and the mean duration of treatment 17 days (range 3 to 33 days). Treatment with liposomal amphotericin B was associated with little nephrotoxicity and an overall survival rate of 50%. The median increase of serum creatinine from baseline levels was 0.38 mg/dl (-1.2 to 2.6 mg/dl).

Increased radiation resistance in lithium-counterdoped silicon solar cells

Science.gov (United States)

Weinberg, I.; Swartz, C. K.; Mehta, S.

1984-01-01

Lithium-counterdoped n(+)p silicon solar cells are found to exhibit significantly increased radiation resistance to 1-MeV electron irradiation when compared to boron-doped n(+)p silicon solar cells. In addition to improved radiation resistance, considerable damage recovery by annealing is observed in the counterdoped cells at T less than or equal to 100 C. Deep level transient spectroscopy measurements are used to identify the defect whose removal results in the low-temperature aneal. It is suggested that the increased radiation resistance of the counterdoped cells is primarily due to interaction of the lithium with interstitial oxygen.

miRBase: annotating high confidence microRNAs using deep sequencing data.

Science.gov (United States)

Kozomara, Ana; Griffiths-Jones, Sam

2014-01-01

We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.

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.

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/.

Patterns and trends of macrobenthic abundance, biomass and production in the deep Arctic Ocean

Directory of Open Access Journals (Sweden)

Renate Degen

2015-08-01

Full Text Available Little is known about the distribution and dynamics of macrobenthic communities of the deep Arctic Ocean. The few previous studies report low standing stocks and confirm a gradient with declining biomass from the slopes down to the basins, as commonly reported for deep-sea benthos. In this study, we investigated regional differences of faunal abundance and biomass, and made for the first time ever estimates of deep Arctic community production by using a multi-parameter artificial neural network model. The underlying data set combines data from recent field studies with published and unpublished data from the past 20 years, to analyse the influence of water depth, geographical latitude and sea-ice concentration on Arctic benthic communities. We were able to confirm the previously described negative relationship of macrofauna standing stock with water depth in the Arctic deep sea, while also detecting substantial regional differences. Furthermore, abundance, biomass and production decreased significantly with increasing sea-ice extent (towards higher latitudes down to values <200 ind m−2, <65 mg C m−2 and <73 mg C m−2 y−1, respectively. In contrast, stations under the seasonal ice zone regime showed much higher standing stock and production (up to 2500 mg C m−2 y−1, even at depths down to 3700 m. We conclude that particle flux is the key factor structuring benthic communities in the deep Arctic Ocean as it explains both the low values in the ice-covered Arctic basins and the higher values in the seasonal ice zone.

State of HIV in the US Deep South.

Science.gov (United States)

Reif, Susan; Safley, Donna; McAllaster, Carolyn; Wilson, Elena; Whetten, Kathryn

2017-10-01

The Southern United States has been disproportionately affected by HIV diagnoses and mortality. To inform efforts to effectively address HIV in the South, this manuscript synthesizes recent data on HIV epidemiology, care financing, and current research literature on factors that predispose this region to experience a greater impact of HIV. The manuscript focuses on a specific Southern region, the Deep South, which has been particularly affected by HIV. Epidemiologic data from the Centers from Disease Control and Prevention indicate that the Deep South had the highest HIV diagnosis rate and the highest number of individuals diagnosed with HIV (18,087) in 2014. The percentage of new HIV diagnoses that were female has decreased over time (2008-2014) while increasing among minority MSM. The Deep South also had the highest death rates with HIV as an underlying cause of any US region in 2014. Despite higher diagnosis and death rates, the Deep South received less federal government and private foundation funding per person living with HIV than the US overall. Factors that have been identified as contributors to the disproportionate effects of HIV in the Deep South include pervasive HIV-related stigma, poverty, higher levels of sexually transmitted infections, racial inequality and bias, and laws that further HIV-related stigma and fear. Interventions that address and abate the contributors to the spread of HIV disease and the poorer HIV-related outcomes in the Deep South are warranted. Funding inequalities by region must also be examined and addressed to reduce the regional disparities in HIV incidence and mortality.

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

From deep to superficial categorization with increasing expertise.

OpenAIRE

Ormerod, Thomas C.; Fritz, Catherine O.; Ridgway, James

1999-01-01

An experimental study of task design expertise is reported wherein a set of 12 mathematics tasks were sorted by specialist designers of mathematics tasks and by experienced mathematics teachers without specialist design experience. Contrary to the frequent finding of increasing conceptual depth with increasing expertise, conceptual depth did not differ between groups. Teachers sorted on the basis of mathematical content earlier than designers, and were more specific in their content-based cat...

The role of deep level traps in barrier height of 4H-SiC Schottky diode

Energy Technology Data Exchange (ETDEWEB)

Zaremba, G., E-mail: gzaremba@ite.waw.pl [Institute of Electron Technology, Al. Lotnikow 32/46, 02-668 Warsaw (Poland); Adamus, Z. [Institute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw (Poland); Jung, W.; Kaminska, E.; Borysiewicz, M.A.; Korwin-Mikke, K. [Institute of Electron Technology, Al. Lotnikow 32/46, 02-668 Warsaw (Poland)

2012-09-01

This paper presents a discussion about the influence of deep level defects on the height of Ni-Si based Schottky barriers to 4H-SiC. The defects were characterized by deep level transient spectroscopy (DLTS) in a wide range of temperatures (78-750 K). The numerical simulation of barrier height value as a function of dominant defect concentration was carried out to estimate concentration, necessary to 'pin' Fermi level and thus significantly influence the barrier height. From comparison of the results of simulation with barrier height values obtained by capacitance-voltage (C-V) measurements it seems that dominant defect in measured concentration has a very small impact on the barrier height and on the increase of reverse current.

Application of Deep Cryogenic Treatment to Uncoated Tungsten Carbide Inserts in the Turning of AISI 304 Stainless Steel

Science.gov (United States)

Özbek, Nursel Altan; Çİçek, Adem; Gülesİn, Mahmut; Özbek, Onur

2016-12-01

This study investigated the effects of deep cryogenic treatment (DCT) on the wear performance of uncoated tungsten carbide inserts. AISI 304 austenitic stainless steel, widely used in industry, was selected as the workpiece material. Cutting experiments showed that the amount of wear significantly increased with increasing cutting speed. In addition, it was found that DCT contributed to the wear resistance of the turning inserts. The treated turning inserts were less worn by 48 and 38 pct in terms of crater wear and notch wear, respectively, whereas they exhibited up to 18 pct superior wear performance in terms of flank wear. This was attributed to the precipitation of new and finer η-carbides and their homogeneous distribution in the microstructure of the tungsten carbide material after deep cryogenic treatment. Analyses via image processing, hardness measurements, and SEM observations confirmed these findings.

Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model

KAUST Repository

Hamam, Alwaleed A.

2017-03-13

Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it\\'s time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.

Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model

KAUST Repository

Hamam, Alwaleed A.; Khan, Ayaz H.

2017-01-01

Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it's time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.

Flood frequency matters: Why climate change degrades deep-water quality of peri-alpine lakes

Science.gov (United States)

Fink, Gabriel; Wessels, Martin; Wüest, Alfred

2016-09-01

Sediment-laden riverine floods transport large quantities of dissolved oxygen into the receiving deep layers of lakes. Hence, the water quality of deep lakes is strongly influenced by the frequency of riverine floods. Although flood frequency reflects climate conditions, the effects of climate variability on the water quality of deep lakes is largely unknown. We quantified the effects of climate variability on the potential shifts in the flood regime of the Alpine Rhine, the main catchment of Lake Constance, and determined the intrusion depths of riverine density-driven underflows and the subsequent effects on water exchange rates in the lake. A simplified hydrodynamic underflow model was developed and validated with observed river inflow and underflow events. The model was implemented to estimate underflow statistics for different river inflow scenarios. Using this approach, we integrated present and possible future flood frequencies to underflow occurrences and intrusion depths in Lake Constance. The results indicate that more floods will increase the number of underflows and the intensity of deep-water renewal - and consequently will cause higher deep-water dissolved oxygen concentrations. Vice versa, fewer floods weaken deep-water renewal and lead to lower deep-water dissolved oxygen concentrations. Meanwhile, a change from glacial nival regime (present) to a nival pluvial regime (future) is expected to decrease deep-water renewal. While flood frequencies are not expected to change noticeably for the next decades, it is most likely that increased winter discharge and decreased summer discharge will reduce the number of deep density-driven underflows by 10% and favour shallower riverine interflows in the upper hypolimnion. The renewal in the deepest layers is expected to be reduced by nearly 27%. This study underlines potential consequences of climate change on the occurrence of deep river underflows and water residence times in deep lakes.

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.

Permeability Surface of Deep Middle Cerebral Artery Territory on Computed Tomographic Perfusion Predicts Hemorrhagic Transformation After Stroke.

Science.gov (United States)

Li, Qiao; Gao, Xinyi; Yao, Zhenwei; Feng, Xiaoyuan; He, Huijin; Xue, Jing; Gao, Peiyi; Yang, Lumeng; Cheng, Xin; Chen, Weijian; Yang, Yunjun

2017-09-01

Permeability surface (PS) on computed tomographic perfusion reflects blood-brain barrier permeability and is related to hemorrhagic transformation (HT). HT of deep middle cerebral artery (MCA) territory can occur after recanalization of proximal large-vessel occlusion. We aimed to determine the relationship between HT and PS of deep MCA territory. We retrospectively reviewed 70 consecutive acute ischemic stroke patients presenting with occlusion of the distal internal carotid artery or M1 segment of the MCA. All patients underwent computed tomographic perfusion within 6 hours after symptom onset. Computed tomographic perfusion data were postprocessed to generate maps of different perfusion parameters. Risk factors were identified for increased deep MCA territory PS. Receiver operating characteristic curve analysis was performed to calculate the optimal PS threshold to predict HT of deep MCA territory. Increased PS was associated with HT of deep MCA territory. After adjustments for age, sex, onset time to computed tomographic perfusion, and baseline National Institutes of Health Stroke Scale, poor collateral status (odds ratio, 7.8; 95% confidence interval, 1.67-37.14; P =0.009) and proximal MCA-M1 occlusion (odds ratio, 4.12; 95% confidence interval, 1.03-16.52; P =0.045) were independently associated with increased deep MCA territory PS. Relative PS most accurately predicted HT of deep MCA territory (area under curve, 0.94; optimal threshold, 2.89). Increased PS can predict HT of deep MCA territory after recanalization therapy for cerebral proximal large-vessel occlusion. Proximal MCA-M1 complete occlusion and distal internal carotid artery occlusion in conjunction with poor collaterals elevate deep MCA territory PS. © 2017 American Heart Association, Inc.

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.

Late Eocene impact events recorded in deep-sea sediments

Science.gov (United States)

Glass, B. P.

1988-01-01

Raup and Sepkoski proposed that mass extinctions have occurred every 26 Myr during the last 250 Myr. In order to explain this 26 Myr periodicity, it was proposed that the mass extinctions were caused by periodic increases in cometary impacts. One method to test this hypothesis is to determine if there were periodic increases in impact events (based on crater ages) that correlate with mass extinctions. A way to test the hypothesis that mass extinctions were caused by periodic increases in impact cratering is to look for evidence of impact events in deep-sea deposits. This method allows direct observation of the temporal relationship between impact events and extinctions as recorded in the sedimentary record. There is evidence in the deep-sea record for two (possibly three) impact events in the late Eocene. The younger event, represented by the North American microtektite layer, is not associated with an Ir anomaly. The older event, defined by the cpx spherule layer, is associated with an Ir anomaly. However, neither of the two impact events recorded in late Eocene deposits appears to be associated with an unusual number of extinctions. Thus there is little evidence in the deep-sea record for an impact-related mass extinction in the late Eocene.

Recurrent deep venous thrombosis during optimal anticoagulation and overt hyperthyroidism: a case report

NARCIS (Netherlands)

Squizzato, Alessandro; Vitale, Josè; Gerdes, Victor Ea; Romualdi, Erica; Büller, Harry R.; Ageno, Walter

2007-01-01

Recurrent deep venous thrombosis despite well conducted anticoagulant treatment is an uncommon, but possible, event. It has been hypothesized that overt hyperthyroidism may increase thromboembolic risk. We present the case of an elderly man with a recurrent episode of deep venous thrombosis during

Acoustic emission localization on ship hull structures using a deep learning approach

DEFF Research Database (Denmark)

Georgoulas, George; Kappatos, Vassilios; Nikolakopoulos, George

2016-01-01

In this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimension......In this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high...

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.

Immediate versus delayed intramedullary nailing for open fractures of the tibial shaft: A multivariate analysis of factors affecting deep infection and fracture healing

Directory of Open Access Journals (Sweden)

Yokoyama Kazuhiko

2008-01-01

existence of deep infection significantly correlated with occurrence of nonunion ( P < 0.05. Gustilo type and existence of deep infection were significantly correlated with healing time to union on multivariate analysis (r 2 = 0.263, P = 0.0001. Conclusion: Multivariate analyses for open tibial fractures treated with IMN showed that IMN after EF (especially in existence of pin site infection was at high risk of deep infection, and that debridement within 6 h and appropriate soft-tissue managements were also important factor in preventing deep infections. These analyses postulated that both the Gustilo type and the existence of deep infection is related with fracture healing in open fractures treated with IMN. In addition, immediate IMN for type IIIB and IIIC is potentially risky, and canal reaming did not increase the risk of complication for open tibial fractures treated with IMN.

Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning

Science.gov (United States)

Shi, Bibo; Hou, Rui; Mazurowski, Maciej A.; Grimm, Lars J.; Ren, Yinhao; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2018-02-01

Purpose: To determine whether domain transfer learning can improve the performance of deep features extracted from digital mammograms using a pre-trained deep convolutional neural network (CNN) in the prediction of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. Method: In this study, we collected digital mammography magnification views for 140 patients with DCIS at biopsy, 35 of which were subsequently upstaged to invasive cancer. We utilized a deep CNN model that was pre-trained on two natural image data sets (ImageNet and DTD) and one mammographic data set (INbreast) as the feature extractor, hypothesizing that these data sets are increasingly more similar to our target task and will lead to better representations of deep features to describe DCIS lesions. Through a statistical pooling strategy, three sets of deep features were extracted using the CNNs at different levels of convolutional layers from the lesion areas. A logistic regression classifier was then trained to predict which tumors contain occult invasive disease. The generalization performance was assessed and compared using repeated random sub-sampling validation and receiver operating characteristic (ROC) curve analysis. Result: The best performance of deep features was from CNN model pre-trained on INbreast, and the proposed classifier using this set of deep features was able to achieve a median classification performance of ROC-AUC equal to 0.75, which is significantly better (p<=0.05) than the performance of deep features extracted using ImageNet data set (ROCAUC = 0.68). Conclusion: Transfer learning is helpful for learning a better representation of deep features, and improves the prediction of occult invasive disease in DCIS.

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.

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.

Seasonal Deep Aquifer Thermal Energy Storage in the Gassum Sandstone Formation

DEFF Research Database (Denmark)

Holmslykke, H.D.H.; Kjøller, C.; Fabricius, Ida Lykke

Seasonal storage of excess heat in hot deep aquifers is considered to optimise the usage of commonly available energy sources. The potential chemical reactions caused by heating the Gassum Sandstone Formation to up to 150°C is investigated by core flooding experiments combined with petrographic...... analysis and geochemical modelling. Synthetic formation water is injected into two sets of Gassum Formation samples at 25°C, 50°C (reservoir temperature), 100°C and 150°C with a velocity of 0.05 PV/hr and 0.1 PV/hr, respectively. A significant increase in the aqueous concentration of silicium and iron...

Deep borehole disposal of high-level radioactive waste.

Energy Technology Data Exchange (ETDEWEB)

Stein, Joshua S.; Freeze, Geoffrey A.; Brady, Patrick Vane; Swift, Peter N.; Rechard, Robert Paul; Arnold, Bill Walter; Kanney, Joseph F.; Bauer, Stephen J.

2009-07-01

Preliminary evaluation of deep borehole disposal of high-level radioactive waste and spent nuclear fuel indicates the potential for excellent long-term safety performance at costs competitive with mined repositories. Significant fluid flow through basement rock is prevented, in part, by low permeabilities, poorly connected transport pathways, and overburden self-sealing. Deep fluids also resist vertical movement because they are density stratified. Thermal hydrologic calculations estimate the thermal pulse from emplaced waste to be small (less than 20 C at 10 meters from the borehole, for less than a few hundred years), and to result in maximum total vertical fluid movement of {approx}100 m. Reducing conditions will sharply limit solubilities of most dose-critical radionuclides at depth, and high ionic strengths of deep fluids will prevent colloidal transport. For the bounding analysis of this report, waste is envisioned to be emplaced as fuel assemblies stacked inside drill casing that are lowered, and emplaced using off-the-shelf oilfield and geothermal drilling techniques, into the lower 1-2 km portion of a vertical borehole {approx}45 cm in diameter and 3-5 km deep, followed by borehole sealing. Deep borehole disposal of radioactive waste in the United States would require modifications to the Nuclear Waste Policy Act and to applicable regulatory standards for long-term performance set by the US Environmental Protection Agency (40 CFR part 191) and US Nuclear Regulatory Commission (10 CFR part 60). The performance analysis described here is based on the assumption that long-term standards for deep borehole disposal would be identical in the key regards to those prescribed for existing repositories (40 CFR part 197 and 10 CFR part 63).

Exploring the Earth Using Deep Learning Techniques

Science.gov (United States)

Larraondo, P. R.; Evans, B. J. K.; Antony, J.

2016-12-01

Research using deep neural networks have significantly matured in recent times, and there is now a surge in interest to apply such methods to Earth systems science and the geosciences. When combined with Big Data, we believe there are opportunities for significantly transforming a number of areas relevant to researchers and policy makers. In particular, by using a combination of data from a range of satellite Earth observations as well as computer simulations from climate models and reanalysis, we can gain new insights into the information that is locked within the data. Global geospatial datasets describe a wide range of physical and chemical parameters, which are mostly available using regular grids covering large spatial and temporal extents. This makes them perfect candidates to apply deep learning methods. So far, these techniques have been successfully applied to image analysis through the use of convolutional neural networks. However, this is only one field of interest, and there is potential for many more use cases to be explored. The deep learning algorithms require fast access to large amounts of data in the form of tensors and make intensive use of CPU in order to train its models. The Australian National Computational Infrastructure (NCI) has recently augmented its Raijin 1.2 PFlop supercomputer with hardware accelerators. Together with NCI's 3000 core high performance OpenStack cloud, these computational systems have direct access to NCI's 10+ PBytes of datasets and associated Big Data software technologies (see http://geonetwork.nci.org.au/ and http://nci.org.au/systems-services/national-facility/nerdip/). To effectively use these computing infrastructures requires that both the data and software are organised in a way that readily supports the deep learning software ecosystem. Deep learning software, such as the open source TensorFlow library, has allowed us to demonstrate the possibility of generating geospatial models by combining information from

An introduction to Deep learning on biological sequence data - Examples and solutions

DEFF Research Database (Denmark)

Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten

2017-01-01

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use....... Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively...

Regulatory issues for deep borehole plutonium disposition

International Nuclear Information System (INIS)

Halsey, W.G.

1995-03-01

As a result of recent changes throughout the world, a substantial inventory of excess separated plutonium is expected to result from dismantlement of US nuclear weapons. The safe and secure management and eventual disposition of this plutonium, and of a similar inventory in Russia, is a high priority. A variety of options (both interim and permanent) are under consideration to manage this material. The permanent solutions can be categorized into two broad groups: direct disposal and utilization. The deep borehole disposition concept involves placing excess plutonium deep into old stable rock formations with little free water present. Issues of concern include the regulatory, statutory and policy status of such a facility, the availability of sites with desirable characteristics and the technologies required for drilling deep holes, characterizing them, emplacing excess plutonium and sealing the holes. This white paper discusses the regulatory issues. Regulatory issues concerning construction, operation and decommissioning of the surface facility do not appear to be controversial, with existing regulations providing adequate coverage. It is in the areas of siting, licensing and long term environmental protection that current regulations may be inappropriate. This is because many current regulations are by intent or by default specific to waste forms, facilities or missions significantly different from deep borehole disposition of excess weapons usable fissile material. It is expected that custom regulations can be evolved in the context of this mission

Introducing extra NADPH consumption ability significantly increases the photosynthetic efficiency and biomass production of cyanobacteria.

Science.gov (United States)

Zhou, Jie; Zhang, Fuliang; Meng, Hengkai; Zhang, Yanping; Li, Yin

2016-11-01

Increasing photosynthetic efficiency is crucial to increasing biomass production to meet the growing demands for food and energy. Previous theoretical arithmetic analysis suggests that the light reactions and dark reactions are imperfectly coupled due to shortage of ATP supply, or accumulation of NADPH. Here we hypothesized that solely increasing NADPH consumption might improve the coupling of light reactions and dark reactions, thereby increasing the photosynthetic efficiency and biomass production. To test this hypothesis, an NADPH consumption pathway was constructed in cyanobacterium Synechocystis sp. PCC 6803. The resulting extra NADPH-consuming mutant grew much faster and achieved a higher biomass concentration. Analyses of photosynthesis characteristics showed the activities of photosystem II and photosystem I and the light saturation point of the NADPH-consuming mutant all significantly increased. Thus, we demonstrated that introducing extra NADPH consumption ability is a promising strategy to increase photosynthetic efficiency and to enable utilization of high-intensity lights. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

submitter Accelerating high-energy physics exploration with deep learning

CERN Document Server

Ojika, Dave; Gordon-Ross, Ann; Carnes, Andrew; Gleyzer, Sergei

2017-01-01

In this work, we present our approach to using deep learning for identification of rarely produced physics particles (such as the Higgs Boson) out of a majority of uninteresting, background or noise-dominated data. A fast and efficient system to eliminate uninteresting data would result in much less data being stored, thus significantly reducing processing time and storage requirements. In this paper, we present a generalized preliminary version of our approach to motivate research interest in advancing the state-of-the-art in deep learning networks for other applications that can benefit from learning systems.

Phenomenology of deep-inelastic processes

International Nuclear Information System (INIS)

Moretto, L.G.

1983-03-01

The field of heavy-ion deep-inelastic reactions is reviewed with particular attention to the experimental picture. The most important degrees of freedom involved in the process are identified and illustrated with relevant experiments. Energy dissipation and mass transfer are discussed in terms of particles and/or phonons exchanged in the process. The equilibration of the fragment neutron-to-proton ratios is inspected for evidence of giant isovector resonances. The angular momentum effects are observed in the fragment angular distributions and the angular momentum transfer is inferred from the magnitude and alignment of the fragments spins. The possible sources of light particles accompanying the deep-inelastic reactions are discussed. The use of the sequentially emitted particles as angular momentum probes is illustrated. The significance and uses of a thermalized component emitted by the dinucleus is reviewed. The possible presence of Fermi jets in the prompt component is shown to be critical to the justification of the one-body theories

Deep groundwater flow at Palmottu

International Nuclear Information System (INIS)

Niini, H.; Vesterinen, M.; Tuokko, T.

1993-01-01

Further observations, measurements, and calculations aimed at determining the groundwater flow regimes and periodical variations in flow at deeper levels were carried out in the Lake Palmottu (a natural analogue study site for radioactive waste disposal in southwestern Finland) drainage basin. These water movements affect the migration of radionuclides from the Palmottu U-Th deposit. The deep water flow is essentially restricted to the bedrock fractures which developed under, and are still affected by, the stress state of the bedrock. Determination of the detailed variations was based on fracture-tectonic modelling of the 12 most significant underground water-flow channels that cross the surficial water of the Palmottu area. According to the direction of the hydraulic gradient the deep water flow is mostly outwards from the Palmottu catchment but in the westernmost section it is partly towards the centre. Estimation of the water flow through the U-Th deposit by the water-balance method is still only approximate and needs continued observation series and improved field measurements

Conceptual study on deep-underground energy generation base

International Nuclear Information System (INIS)

Hayano, M.; Okawa, T.

1992-01-01

Mitsubishi Atomic Power Industries, Inc. (MAPI) and Taisei Corporation have started a conceptual study on a deep-underground energy generation base for future cities in the 21st century around the metropolitan area, which will be increasingly important from viewpoints of the autonomy and sharing of the energy supply to the future cities. The energy generation base consists of a gas cooled reactor with naturally safety features as the energy source, an electric generation base using the Alkali Metal Thermo-electric Converter (AMTEC), a hydrogen production plant with the Solid Polymer Electrolyte (SPE), a hydrogen storage plant with the Metal Hydride (MH), and a desalination plant. This paper describes a concept of the energy generation base and the structure in the deep-underground, in soft soil, then the basic system of each plant, and finally discusses the feasibility of the deep-underground energy generation base. (author)

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

OpenAIRE

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 net...

Reduced deep regional cerebral venous oxygen saturation in hemodialysis patients using quantitative susceptibility mapping.

Science.gov (United States)

Chai, Chao; Liu, Saifeng; Fan, Linlin; Liu, Lei; Li, Jinping; Zuo, Chao; Qian, Tianyi; Haacke, E Mark; Shen, Wen; Xia, Shuang

2018-02-01

Cerebral venous oxygen saturation (SvO 2 ) is an important indicator of brain function. There was debate about lower cerebral oxygen metabolism in hemodialysis patients and there were no reports about the changes of deep regional cerebral SvO 2 in hemodialysis patients. In this study, we aim to explore the deep regional cerebral SvO 2 from straight sinus using quantitative susceptibility mapping (QSM) and the correlation with clinical risk factors and neuropsychiatric testing . 52 hemodialysis patients and 54 age-and gender-matched healthy controls were enrolled. QSM reconstructed from original phase data of 3.0 T susceptibility-weighted imaging was used to measure the susceptibility of straight sinus. The susceptibility was used to calculate the deep regional cerebral SvO 2 and compare with healthy individuals. Correlation analysis was performed to investigate the correlation between deep regional cerebral SvO 2 , clinical risk factors and neuropsychiatric testing. The deep regional cerebral SvO 2 of hemodialysis patients (72.5 ± 3.7%) was significantly lower than healthy controls (76.0 ± 2.1%) (P deep regional cerebral SvO 2 in patients. The Mini-Mental State Examination (MMSE) scores of hemodialysis patients were significantly lower than healthy controls (P deep regional cerebral SvO 2 did not correlate with MMSE scores (P = 0.630). In summary, the decreased deep regional cerebral SvO 2 occurred in hemodialysis patients and dialysis duration, parathyroid hormone, hematocrit, hemoglobin and red blood cell may be clinical risk factors.

An adaptive deep learning approach for PPG-based identification.

Science.gov (United States)

Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

2016-08-01

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

Neuromodulation of Attentional Control in Major Depression: A Pilot DeepTMS Study

Directory of Open Access Journals (Sweden)

Jodie Naim-Feil

2016-01-01

Full Text Available While Major Depressive Disorder (MDD is primarily characterized by mood disturbances, impaired attentional control is increasingly identified as a critical feature of depression. Deep transcranial magnetic stimulation (deepTMS, a noninvasive neuromodulatory technique, can modulate neural activity and induce neuroplasticity changes in brain regions recruited by attentional processes. This study examined whether acute and long-term high-frequency repetitive deepTMS to the dorsolateral prefrontal cortex (DLPFC can attenuate attentional deficits associated with MDD. Twenty-one MDD patients and 26 matched control subjects (CS were administered the Beck Depression Inventory and the Sustained Attention to Response Task (SART at baseline. MDD patients were readministered the SART and depressive assessments following a single session (n=21 and after 4 weeks (n=13 of high-frequency (20 Hz repetitive deepTMS applied to the DLPFC. To control for the practice effect, CS (n=26 were readministered the SART a further two times. The MDD group exhibited deficits in sustained attention and cognitive inhibition. Both acute and long-term high-frequency repetitive frontal deepTMS ameliorated sustained attention deficits in the MDD group. Improvement after acute deepTMS was related to attentional recovery after long-term deepTMS. Longer-term improvement in sustained attention was not related to antidepressant effects of deepTMS treatment.

Neuromodulation of Attentional Control in Major Depression: A Pilot DeepTMS Study.

Science.gov (United States)

Naim-Feil, Jodie; Bradshaw, John L; Sheppard, Dianne M; Rosenberg, Oded; Levkovitz, Yechiel; Dannon, Pinhas; Fitzgerald, Paul B; Isserles, Moshe; Zangen, Abraham

2016-01-01

While Major Depressive Disorder (MDD) is primarily characterized by mood disturbances, impaired attentional control is increasingly identified as a critical feature of depression. Deep transcranial magnetic stimulation (deepTMS), a noninvasive neuromodulatory technique, can modulate neural activity and induce neuroplasticity changes in brain regions recruited by attentional processes. This study examined whether acute and long-term high-frequency repetitive deepTMS to the dorsolateral prefrontal cortex (DLPFC) can attenuate attentional deficits associated with MDD. Twenty-one MDD patients and 26 matched control subjects (CS) were administered the Beck Depression Inventory and the Sustained Attention to Response Task (SART) at baseline. MDD patients were readministered the SART and depressive assessments following a single session (n = 21) and after 4 weeks (n = 13) of high-frequency (20 Hz) repetitive deepTMS applied to the DLPFC. To control for the practice effect, CS (n = 26) were readministered the SART a further two times. The MDD group exhibited deficits in sustained attention and cognitive inhibition. Both acute and long-term high-frequency repetitive frontal deepTMS ameliorated sustained attention deficits in the MDD group. Improvement after acute deepTMS was related to attentional recovery after long-term deepTMS. Longer-term improvement in sustained attention was not related to antidepressant effects of deepTMS treatment.

Comparison the Effects of Shallow and Deep Endotracheal Tube Suctioning on Respiratory Rate, Arterial Blood Oxygen Saturation and Number of Suctioning in Patients Hospitalized in the Intensive Care Unit: A Randomized Controlled Trial

Directory of Open Access Journals (Sweden)

Mohammad Abbasinia

2014-09-01

Full Text Available Introduction: Endotracheal tube suctioning is essential for improve oxygenation in the patients undergoing mechanical ventilation. There are two types of shallow and deep endotracheal tube suctioning. This study aimed to evaluate the effect of shallow and deep suctioning methods on respiratory rate (RR, arterial blood oxygen saturation (SpO2 and number of suctioning in patients hospitalized in the intensive care units of Al-Zahra Hospital, Isfahan, Iran. Methods: In this randomized controlled trial, 74 patients who hospitalized in the intensive care units of Isfahan Al-Zahra Hospital were randomly allocated to the shallow and deep suctioning groups. RR and SpO2 were measured immediately before, immediately after, 1 and 3 minute after each suctioning. Number of suctioning was also noted in each groups. Data were analyzed using repeated measures analysis of variance (RMANOVA, chi-square and independent t-tests. Results: RR was significantly increased and SpO2 was significantly decreased after each suctioning in the both groups. However, these changes were not significant between the two groups. The numbers of suctioning was significantly higher in the shallow suctioning group than in the deep suctioning group. Conclusion: Shallow and deep suctioning had a similar effect on RR and SpO2. However, shallow suctioning caused further manipulation of patient’s trachea than deep suctioning method. Therefore, it seems that deep endotracheal tube suctioning method can be used to clean the airway with lesser manipulation of the trachea.

Comparison the effects of shallow and deep endotracheal tube suctioning on respiratory rate, arterial blood oxygen saturation and number of suctioning in patients hospitalized in the intensive care unit: a randomized controlled trial.

Science.gov (United States)

Abbasinia, Mohammad; Irajpour, Alireza; Babaii, Atye; Shamali, Mehdi; Vahdatnezhad, Jahanbakhsh

2014-09-01

Endotracheal tube suctioning is essential for improve oxygenation in the patients undergoing mechanical ventilation. There are two types of shallow and deep endotracheal tube suctioning. This study aimed to evaluate the effect of shallow and deep suctioning methods on respiratory rate (RR), arterial blood oxygen saturation (SpO2) and number of suctioning in patients hospitalized in the intensive care units of Al-Zahra Hospital, Isfahan, Iran. In this randomized controlled trial, 74 patients who hospitalized in the intensive care units of Isfahan Al-Zahra Hospital were randomly allocated to the shallow and deep suctioning groups. RR and SpO2 were measured immediately before, immediately after, 1 and 3 minute after each suctioning. Number of suctioning was also noted in each groups. Data were analyzed using repeated measures analysis of variance (RMANOVA), chi-square and independent t-tests. RR was significantly increased and SpO2 was significantly decreased after each suctioning in the both groups. However, these changes were not significant between the two groups. The numbers of suctioning was significantly higher in the shallow suctioning group than in the deep suctioning group. Conclusion : Shallow and deep suctioning had a similar effect on RR and SpO2. However, shallow suctioning caused further manipulation of patient's trachea than deep suctioning method. Therefore, it seems that deep endotracheal tube suctioning method can be used to clean the airway with lesser manipulation of the trachea.

Deep brain stimulation as a functional scalpel.

Science.gov (United States)

Broggi, G; Franzini, A; Tringali, G; Ferroli, P; Marras, C; Romito, L; Maccagnano, E

2006-01-01

Since 1995, at the Istituto Nazionale Neurologico "Carlo Besta" in Milan (INNCB,) 401 deep brain electrodes were implanted to treat several drug-resistant neurological syndromes (Fig. 1). More than 200 patients are still available for follow-up and therapeutical considerations. In this paper our experience is reviewed and pioneered fields are highlighted. The reported series of patients extends the use of deep brain stimulation beyond the field of Parkinson's disease to new fields such as cluster headache, disruptive behaviour, SUNCt, epilepsy and tardive dystonia. The low complication rate, the reversibility of the procedure and the available image guided surgery tools will further increase the therapeutic applications of DBS. New therapeutical applications are expected for this functional scalpel.

Evaluation of persistence of resistant variants with ultra-deep pyrosequencing in chronic hepatitis C patients treated with telaprevir.

Directory of Open Access Journals (Sweden)

Xiomara V Thomas

Full Text Available BACKGROUND & AIMS: Telaprevir, a hepatitis C virus NS3/4A protease inhibitor has significantly improved sustained viral response rates when given in combination with pegylated interferon alfa-2a and ribavirin, compared with current standard of care in hepatitis C virus genotype 1 infected patients. In patients with a failed sustained response, the emergence of drug-resistant variants during treatment has been reported. It is unclear to what extent these variants persist in untreated patients. The aim of this study was to assess using ultra-deep pyrosequencing, whether after 4 years follow-up, the frequency of resistant variants is increased compared to pre-treatment frequencies following 14 days of telaprevir treatment. METHODS: Fifteen patients from 2 previous telaprevir phase 1 clinical studies (VX04-950-101 and VX05-950-103 were included. These patients all received telaprevir monotherapy for 14 days, and 2 patients subsequently received standard of care. Variants at previously well-characterized NS3 protease positions V36, T54, R155 and A156 were assessed at baseline and after a follow-up of 4±1.2 years by ultra-deep pyrosequencing. The prevalence of resistant variants at follow-up was compared to baseline. RESULTS: Resistance associated mutations were detectable at low frequency at baseline. In general, prevalence of resistance mutations at follow-up was not increased compared to baseline. Only one patient had a small, but statistically significant, increase in the number of V36M and T54S variants 4 years after telaprevir-dosing. CONCLUSION: In patients treated for 14 days with telaprevir monotherapy, ultra-deep pyrosequencing indicates that long-term persistence of resistant variants is rare.

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.

A Stratification Boomerang: Nonlinear Dependence of Deep Southern Ocean Ventilation on PCO2

Science.gov (United States)

Galbraith, E. D.; Merlis, T. M.

2014-12-01

Strong correlations between atmospheric CO2, Antarctic temperatures, and marine proxy records have hinted that ventilation of the deep Southern Ocean may have played a central role in the variations of CO2 over glacial-interglacial cycles. One proposition is that, in general, the Southern Ocean ventilates the deep more strongly under higher CO2, due to a change in winds and/or the dominance of thermal stratification in a warm ocean, which weakens ocean biological carbon storage. Here, we explore this idea with a suite of multi-millennial simulations using the GFDL CM2Mc global coupled model. The results are, indeed, consistent with increasing ventilation of the Southern Ocean as pCO2 increases above modern. However, they reveal a surprising twist under low pCO2: increased salinity of the Southern Ocean, due in part to weakening atmospheric moisture transport, actually increases ventilation rate of the deep ocean under low pCO2 as well. This implies that a nadir of Southern Ocean ventilation occurs at intermediate pCO2, which the model estimates as being close to that of the present-day. This is at odds with the interpretation that weak ventilation of the deep Southern Ocean was the unifying coupled mechanism for the glacial pCO2 cycles. Rather, it suggests that factors other than the ventilation rate of the deep Southern Ocean, such as iron fertilization, ecosystem changes, water mass distributions, and sea ice cover, were key players in the glacial-interglacial CO2 changes.

Hydrologic effects of large southwestern USA wildfires significantly increase regional water supply: fact or fiction?

Science.gov (United States)

Wine, M. L.; Cadol, D.

2016-08-01

In recent years climate change and historic fire suppression have increased the frequency of large wildfires in the southwestern USA, motivating study of the hydrological consequences of these wildfires at point and watershed scales, typically over short periods of time. These studies have revealed that reduced soil infiltration capacity and reduced transpiration due to tree canopy combustion increase streamflow at the watershed scale. However, the degree to which these local increases in runoff propagate to larger scales—relevant to urban and agricultural water supply—remains largely unknown, particularly in semi-arid mountainous watersheds co-dominated by winter snowmelt and the North American monsoon. To address this question, we selected three New Mexico watersheds—the Jemez (1223 km2), Mogollon (191 km2), and Gila (4807 km2)—that together have been affected by over 100 wildfires since 1982. We then applied climate-driven linear models to test for effects of fire on streamflow metrics after controlling for climatic variability. Here we show that, after controlling for climatic and snowpack variability, significantly more streamflow discharged from the Gila watershed for three to five years following wildfires, consistent with increased regional water yield due to enhanced infiltration-excess overland flow and groundwater recharge at the large watershed scale. In contrast, we observed no such increase in discharge from the Jemez watershed following wildfires. Fire regimes represent a key difference between the contrasting responses of the Jemez and Gila watersheds with the latter experiencing more frequent wildfires, many caused by lightning strikes. While hydrologic dynamics at the scale of large watersheds were previously thought to be climatically dominated, these results suggest that if one fifth or more of a large watershed has been burned in the previous three to five years, significant increases in water yield can be expected.

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.

Electronic structure properties of deep defects in hBN

Science.gov (United States)

Dev, Pratibha; Prdm Collaboration

In recent years, the search for room-temperature solid-state qubit (quantum bit) candidates has revived interest in the study of deep-defect centers in semiconductors. The charged NV-center in diamond is the best known amongst these defects. However, as a host material, diamond poses several challenges and so, increasingly, there is an interest in exploring deep defects in alternative semiconductors such as hBN. The layered structure of hBN makes it a scalable platform for quantum applications, as there is a greater potential for controlling the location of the deep defect in the 2D-matrix through careful experiments. Using density functional theory-based methods, we have studied the electronic and structural properties of several deep defects in hBN. Native defects within hBN layers are shown to have high spin ground states that should survive even at room temperature, making them interesting solid-state qubit candidates in a 2D matrix. Partnership for Reduced Dimensional Material (PRDM) is part of the NSF sponsored Partnerships for Research and Education in Materials (PREM).

Deep-sea Lebensspuren of the Australian continental margins

Science.gov (United States)

Przeslawski, Rachel; Dundas, Kate; Radke, Lynda; Anderson, Tara J.

Much of the deep sea comprises soft-sediment habitats dominated by comparatively low abundances of species-rich macrofauna and meiofauna. Although often not observed, these animals bioturbate the sediment during feeding and burrowing, leaving signs of their activities called Lebensspuren ('life traces'). In this study, we use still images to quantify Lebensspuren from the eastern (1921 images, 13 stations, 1300-2200 m depth) and western (1008 images, 11 stations, 1500-4400 m depth) Australian margins using a univariate measure of trace richness and a multivariate measure of Lebensspuren assemblages. A total of 46 Lebensspuren types were identified, including those matching named trace fossils and modern Lebensspuren found elsewhere in the world. Most traces could be associated with waste, crawling, dwellings, organism tests, feeding, or resting, but the origin of 15% of trace types remains unknown. Assemblages were significantly different between the two regions and depth profiles, with five Lebensspuren types accounting for over 95% of the differentiation (ovoid pinnate trace, crater row, spider trace, matchstick trace, mesh trace). Lebensspuren richness showed no strong relationships with depth, total organic carbon, or mud, although there was a positive correlation to chlorin index (i.e., organic freshness) in the eastern margin, with richness increasing with organic freshness. Lebensspuren richness was not related to epifauna either, indicating that epifauna may not be the primary source of Lebensspuren. Despite the abundance and distinctiveness of several traces both in the current and previous studies (e.g., ovoid pinnate, mesh, spider), their origin and distribution remains a mystery. We discuss this and several other considerations in the identification and quantification of Lebensspuren. This study represents the first comprehensive catalogue of deep-sea Lebensspuren in Australian waters and highlights the potential of Lebensspuren as valuable and often

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.

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.

Seawater Carbonate Chemistry of Deep-sea Coral Beds off the Northwestern Hawaiian Islands

Science.gov (United States)

Brooks, J.; Shamberger, K.; Roark, E. B.; Miller, K.; Baco-Taylor, A.

2016-02-01

Many species of deep-sea octocorals produce calcium carbonate (CaCO3) skeletons and form coral beds that support diverse ecosystems crucial to fisheries. The geochemistry of deep-sea coral skeletons can provide valuable paleoceanographic information on ocean circulation and nutrient cycling. Deep-sea corals in the older bottom waters of the Pacific are naturally exposed to higher carbon dioxide (CO2) concentrations and lower pH than in the Atlantic where much of the previous deep-sea coral work has occurred. Therefore, some Pacific deep-sea corals may live and calcify in waters that are corrosive to their skeletons, but there have been few current seawater carbonate chemistry measurements of the waters surrounding deep-sea coral beds to assess this. The input of anthropogenic atmospheric CO2 known as ocean acidification (OA) lowers ocean pH and causes an expansion of these corrosive waters. Seawater carbonate chemistry must be characterized before accurate predictions can be made for the effects of OA on these important ecosystems. Total Alkalinity (TA) and Dissolved Inorganic Carbon (DIC) samples were collected in the fall of 2014 and 2015 from the surface to 1450 m depth off the Northwestern Hawaiian Island chain where deep-sea octocorals are found. The partial pressure of CO2 increased and pH, calcite saturation state (Ωca) and aragonite saturation state (Ωar) decreased with increasing latitude and depth. Notably, waters were undersaturated with respect to calcite and aragonite (Ωca and Ωar less than 1) below 800 m and 500 m, respectively. Therefore, deep-sea corals below these depths must calcify in waters that are thermodynamically favorable for CaCO3 dissolution. How deep-sea octocorals cope with such adverse seawater chemistry is critical to understanding future effects of OA. It is not known whether OA is currently negatively impacting deep-sea octocorals, but their naturally acidified environments could make them particularly susceptible to OA.

[Observation on changes of oxygen partial pressure in the deep tissues along the large intestine meridian during acupuncture in healthy subjects].

Science.gov (United States)

Chen, Ming; Hu, Xiang-long; Wu, Zu-xing

2010-06-01

To observe changes of the partial oxygen pressure in the deep tissues along the Large Intestine Meridian (LIM) during acupuncture stimulation, so as to reveal the characteristics of energy metabolism in the tissues along the LIM. Thirty-one healthy volunteer subjects were enlisted in the present study. Partial oxygen pressure (POP) in the tissues (at a depth of about 1.5 cm) of acupoints Binao (LI 14), Shouwuli (LI 13), Shousanli (LI 10), 2 non-acupoints [the midpoints between Quchi (LI 11) and LI 14, and between Yangxi (LI 5) and LI 11) of the LIM, and 10 non-meridian points, 1.5-2.0 cm lateral and medial to each of the tested points of the LIM was detected before, during and after electroacupuncture (EA) stimulation of Hegu (LI 4) by using a tissue oxygen tension needle-like sensor. In normal condition, the POP values in the deep tissues along the LIM were significantly higher than those of the non-meridian control points on its bilateral sides. During and after EA of Hegu (LI 4), the POP levels decreased significantly in the deep tissues along the LIM in comparison with pre-EA (P 0.05). POP is significantly higher in the deep tissues along the LIM of healthy subjects under normal conditions, which can be downregulated by EA of Hegu (LI 4), suggesting an increase of both the utilization rate of oxygen and energy metabolism after EA.

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...

Deep Space CubeSat Prototype Platform Design and Testing

Data.gov (United States)

National Aeronautics and Space Administration — This IRAD will significantly advance a GSFC Deep Space CubeSat prototype effort in almost all subsystems.  Because it represents a “tall pole” for lunar orbiters, as...

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 decision fusion for the classification of urban remote sensing data

Science.gov (United States)

Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter

2018-01-01

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

Biogeochemical malfunctioning in sediments beneath a deep-water fish farm.

Science.gov (United States)

Valdemarsen, Thomas; Bannister, Raymond J; Hansen, Pia K; Holmer, Marianne; Ervik, Arne

2012-11-01

We investigated the environmental impact of a deep water fish farm (190 m). Despite deep water and low water currents, sediments underneath the farm were heavily enriched with organic matter, resulting in stimulated biogeochemical cycling. During the first 7 months of the production cycle benthic fluxes were stimulated >29 times for CO(2) and O(2) and >2000 times for NH(4)(+), when compared to the reference site. During the final 11 months, however, benthic fluxes decreased despite increasing sedimentation. Investigations of microbial mineralization revealed that the sediment metabolic capacity was exceeded, which resulted in inhibited microbial mineralization due to negative feed-backs from accumulation of various solutes in pore water. Conclusions are that (1) deep water sediments at 8 °C can metabolize fish farm waste corresponding to 407 and 29 mmol m(-2) d(-1) POC and TN, respectively, and (2) siting fish farms at deep water sites is not a universal solution for reducing benthic impacts. Copyright © 2012 Elsevier Ltd. All rights reserved.

Low-cost, high-precision micro-lensed optical fiber providing deep-micrometer to deep-nanometer-level light focusing.

Science.gov (United States)

Wen, Sy-Bor; Sundaram, Vijay M; McBride, Daniel; Yang, Yu

2016-04-15

A new type of micro-lensed optical fiber through stacking appropriate high-refractive microspheres at designed locations with respect to the cleaved end of an optical fiber is numerically and experimentally demonstrated. This new type of micro-lensed optical fiber can be precisely constructed with low cost and high speed. Deep micrometer-scale and submicrometer-scale far-field light spots can be achieved when the optical fibers are multimode and single mode, respectively. By placing an appropriate teardrop dielectric nanoscale scatterer at the far-field spot of this new type of micro-lensed optical fiber, a deep-nanometer near-field spot can also be generated with high intensity and minimum joule heating, which is valuable in high-speed, high-resolution, and high-power nanoscale detection compared with traditional near-field optical fibers containing a significant portion of metallic material.

Deep primary production in coastal pelagic systems

DEFF Research Database (Denmark)

Lyngsgaard, Maren Moltke; Richardson, Katherine; Markager, Stiig

2014-01-01

produced. The primary production (PP) occurring below the surface layer, i.e. in the pycnocline-bottom layer (PBL), is shown to contribute significantly to total PP. Oxygen concentrations in the PBL are shown to correlate significantly with the deep primary production (DPP) as well as with salinity...... that eutrophication effects may include changes in the structure of planktonic food webs and element cycling in the water column, both brought about through an altered vertical distribution of PP....

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

Science.gov (United States)

Fang, Chao; Shang, Yi; Xu, Dong

2018-05-01

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

The impact of a grain of sand: increasing production speed in flexible risers generates significant savings in gas production

NARCIS (Netherlands)

Bokhorst, E. van; Blokland, H.

2012-01-01

Deep-sea oil and gas production normally involves the use of flexible risers that comprise a metal carcass with a large number of enveloping layers that safeguard the integrity of the pipe system. The flexible risers are hung from a floating platform and may be supported by several floating buoys to

Patterns of deep-sea genetic connectivity in the New Zealand region: implications for management of benthic ecosystems.

Directory of Open Access Journals (Sweden)

Eleanor K Bors

Full Text Available Patterns of genetic connectivity are increasingly considered in the design of marine protected areas (MPAs in both shallow and deep water. In the New Zealand Exclusive Economic Zone (EEZ, deep-sea communities at upper bathyal depths (<2000 m are vulnerable to anthropogenic disturbance from fishing and potential mining operations. Currently, patterns of genetic connectivity among deep-sea populations throughout New Zealand's EEZ are not well understood. Using the mitochondrial Cytochrome Oxidase I and 16S rRNA genes as genetic markers, this study aimed to elucidate patterns of genetic connectivity among populations of two common benthic invertebrates with contrasting life history strategies. Populations of the squat lobster Munida gracilis and the polychaete Hyalinoecia longibranchiata were sampled from continental slope, seamount, and offshore rise habitats on the Chatham Rise, Hikurangi Margin, and Challenger Plateau. For the polychaete, significant population structure was detected among distinct populations on the Chatham Rise, the Hikurangi Margin, and the Challenger Plateau. Significant genetic differences existed between slope and seamount populations on the Hikurangi Margin, as did evidence of population differentiation between the northeast and southwest parts of the Chatham Rise. In contrast, no significant population structure was detected across the study area for the squat lobster. Patterns of genetic connectivity in Hyalinoecia longibranchiata are likely influenced by a number of factors including current regimes that operate on varying spatial and temporal scales to produce potential barriers to dispersal. The striking difference in population structure between species can be attributed to differences in life history strategies. The results of this study are discussed in the context of existing conservation areas that are intended to manage anthropogenic threats to deep-sea benthic communities in the New Zealand region.

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

Major consequences of an intense dense shelf water cascading event on deep-sea benthic trophic conditions and meiofaunal biodiversity

Directory of Open Access Journals (Sweden)

A. Pusceddu

2013-04-01

Full Text Available Numerous submarine canyons around the world are preferential conduits for episodic dense shelf water cascading (DSWC, which quickly modifies physical and chemical ambient conditions while transporting large amounts of material towards the base of slope and basin. Observations conducted during the last 20 yr in the Lacaze-Duthiers and Cap de Creus canyons (Gulf of Lion, NW Mediterranean Sea report several intense DSWC events. The effects of DSWC on deep-sea ecosystems are almost unknown. To investigate the effects of these episodic events, we analysed changes in the meiofaunal biodiversity inside and outside the canyon. Sediment samples were collected at depths varying from ca. 1000 to > 2100 m in May 2004 (before a major event, April 2005 (during a major cascading event and in October 2005, August 2006, April 2008 and April 2009 (after a major event. We report here that the late winter–early spring 2005 cascading led to a reduction of the organic matter contents in canyon floor sediments down to 1800 m depth, whereas surface sediments at about 2200 m depth showed an increase. Our findings suggest that the nutritional material removed from the shallower continental shelf, canyon floor and flanks, and also the adjacent open slope was rapidly transported to the deep margin. During the cascading event the meiofaunal abundance and biodiversity in the studied deep-sea sediments were significantly lower than after the event. Benthic assemblages during the cascading were significantly different from those in all other sampling periods in both the canyon and deep margin. After only six months from the cessation of the cascading, benthic assemblages in the impacted sediments were again similar to those observed in other sampling periods, thus illustrating a quick recovery. Since the present climate change is expected to increase the intensity and frequency of these episodic events, we anticipate that they will increasingly affect benthic bathyal

Deep water overflow in the Faroe Bank Channel; modelling, processes, and impact

DEFF Research Database (Denmark)

Rullyanto, Arief

, creating new water masses with distinct temperature, salinity and density characteristics. The change of water mass characteristics not only affects the local environment, but also far distant regions. The Faroe Bank Channel, which is located in the southern part of Faroe Islands, is one of the most...... under different circumstances. The focus is on the Faroe Bank Channel, a relatively small region, which has a significant impact on the global ocean circulation and marine organisms that live in its environment....... or tides, but also deep beneath the surface, where deep-water currents circulate waters throughout the world’s oceans. In certain very-localized regions, the flow of the deep-water has to travel over a sill in a narrow submarine channel. This overflow process mixes the deep water with overlying waters...

Effects of Straw Return in Deep Soils with Urea Addition on the Soil Organic Carbon Fractions in a Semi-Arid Temperate Cornfield.

Science.gov (United States)

Zou, Hongtao; Ye, Xuhong; Li, Jiaqi; Lu, Jia; Fan, Qingfeng; Yu, Na; Zhang, Yuling; Dang, Xiuli; Zhang, Yulong

2016-01-01

Returning straw to deep soil layers by using a deep-ditching-ridge-ploughing method is an innovative management practice that improves soil quality by increasing the soil organic carbon (SOC) content. However, the optimum quantity of straw return has not been determined. To solve this practical production problem, the following treatments with different amounts of corn straw were investigated: no straw return, CK; 400 kg ha-1 straw, S400; 800 kg ha-1 straw, S800; 1200 kg ha-1 straw, S1200; and 1600 kg ha-1 straw, S1600. After straw was returned to the soil for two years, the microbial biomass C (MBC), easily oxidized organic C (EOC), dissolved organic C (DOC) and light fraction organic C (LFOC) content were measured at three soil depths (0-10, 10-20, and 20-40 cm). The results showed that the combined application of 800 kg ha-1 straw significantly increased the EOC, MBC, and LFOC contents and was a suitable agricultural practice for this region. Moreover, our results demonstrated that returning straw to deep soil layers was effective for increasing the SOC content.

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.

Increasing the statistical significance of entanglement detection in experiments.

Science.gov (United States)

Jungnitsch, Bastian; Niekamp, Sönke; Kleinmann, Matthias; Gühne, Otfried; Lu, He; Gao, Wei-Bo; Chen, Yu-Ao; Chen, Zeng-Bing; Pan, Jian-Wei

2010-05-28

Entanglement is often verified by a violation of an inequality like a Bell inequality or an entanglement witness. Considerable effort has been devoted to the optimization of such inequalities in order to obtain a high violation. We demonstrate theoretically and experimentally that such an optimization does not necessarily lead to a better entanglement test, if the statistical error is taken into account. Theoretically, we show for different error models that reducing the violation of an inequality can improve the significance. Experimentally, we observe this phenomenon in a four-photon experiment, testing the Mermin and Ardehali inequality for different levels of noise. Furthermore, we provide a way to develop entanglement tests with high statistical significance.

Application of a water balance model for estimating deep infiltration in a karstic watershed

Directory of Open Access Journals (Sweden)

Maria Lúcia Calijuri

2011-12-01

Full Text Available The current scenario of water scarcity evidences the need for an adequate management of water resources. In karstic regions, the water flow through fractures significantly increases the water infiltration rate, which explains the small number of rivers and the importance of groundwater for urban supply. Therefore, the water balance is necessary since it may aid decision making processes and guide water management projects. The objective of this paper was to perform the water balance of a watershed situated in a karstic region quantifying infiltration, runoff and evapotranspiration. The study area is located near the Tancredo Neves International Airport in Confins, in the state of Minas Gerais, Brazil. Most of the area consists of forest formations (40.9%, and pastures (34.5%. In order to estimate deep infiltration, the BALSEQ model was used. BALSEQ is a numeric model of sequential water balance in which deep infiltration at the end of the day is given by the difference between daily precipitation and the sum of surface runoff, evapotranspiration and the variation of the amount of water stored in the soil. The results show that approximately 60% of total annual precipitation result in deep infiltration, considering the recharge period from September to March. After the dry period, the areas with no vegetal cover present higher deep infiltration. However, over the months, the contribution of the vegetated areas becomes greater, showing the importance of these areas to aquifer recharge.

Bacterial Community Response in Deep Faroe-Shetland Channel Sediments Following Hydrocarbon Entrainment With and Without Dispersant Addition

Directory of Open Access Journals (Sweden)

Luis J. Perez Calderon

2018-05-01

Full Text Available Deep sea oil exploration is increasing and presents environmental challenges for deep ocean ecosystems. Marine oil spills often result in contamination of sediments with oil; following the Deepwater Horizon (DwH disaster up to 31% of the released oil entrained in the water column was deposited as oily residues on the seabed. Although the aftermath of DwH was studied intensely, lessons learned may not be directly transferable to other deep-sea hydrocarbon exploration areas, such as the Faroe-Shetland Channel (FSC which comprises cold temperatures and a unique hydrodynamic regime. Here, transport of hydrocarbons into deep FSC sediments, subsequent responses in benthic microbial populations and effects of dispersant application on hydrocarbon fate and microbial communities were investigated. Sediments from 1,000 m in the FSC were incubated at 0°C for 71 days after addition of a 20-hydrocarbon component oil-sediment aggregate. Dispersant was added periodically from day 4. An additional set of cores using sterilized and homogenized sediment was analyzed to evaluate the effects of sediment matrix modification on hydrocarbon entrainment. Sediment layers were independently analyzed for hydrocarbon content by gas chromatography with flame ionization detection and modeled with linear mixed effects models. Oil was entrained over 4 cm deep into FSC sediments after 42 days and dispersant effectiveness on hydrocarbon removal from sediment to the water column decreased with time. Sterilizing and homogenizing sediment resulted in hydrocarbon transport over 4 cm into sediments after 7 days. Significant shifts in bacterial populations were observed (DGGE profiling in response to hydrocarbon exposure after 42 days and below 2 cm deep. Dispersant application resulted in an accelerated and modified shift in bacterial communities. Bacterial 16S rRNA gene sequencing of oiled sediments revealed dominance of Colwellia and of Fusibacter when dispersant was applied over

Deep-Diving California Sea Lions: Are They Pushing Their Physiological Limit

Science.gov (United States)

2015-09-30

highly variable. Venous oxygen content can actually increase during short duration dives. This suggests very little muscle blood flow and evven the use...the sea lion, the emperor penguin (Aptenodytes forsteri), another animal that dives on inspiration with a large respiratory O2 store, also can...in deep-diving emperor penguins (Wright et al. 2014), and in deep-diving bottlenose dolphins (Tursiops truncatus), which also dive on inspiration

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.

Geothermal probes for the development of medium-deep geothermal heating; Erdwaermesonden zur Erschliessung der mitteltiefen Geothermie

Energy Technology Data Exchange (ETDEWEB)

Stuckmann, Uwe [REHAU AG + Co, Erlangen (Germany)

2012-07-01

Compared to the near-surface geothermal energy, in the medium-deep geothermal between between 400 and 1,000 meters higher temperature levels may opened up. Thus the efficiency of geothermal power plants can be increased. The possibly higher installation costs are significantly higher yield compared to the yields and withdrawal benefits. At higher thermal gradient of the underground it even is possible to dispense entirely on the heat pump and to heat directly.

Interactions between deep bedrock aquifers and surface water in function of recharge and topography: a numerical study

Science.gov (United States)

Goderniaux, P.; Davy, P.; Le Borgne, T.; Bresciani, E.; Jimenez-Martinez, J.

2011-12-01

In crystalline rock regions, such as Brittany (France), important reserves of groundwater into deep fractured aquifers are increasingly used and provide high quality water compared to shallow aquifers which can be subject to agricultural contamination. However, recharge processes of these deep aquifers and interactions with surface water are not yet fully understood. In some areas, intensive pumping is carried out without guarantee of the resource quantity and quality. Understanding these processes is crucial for sustainable management of the resource. In this study, we study how deep groundwater fluxes, pathways, ages, and river-aquifer interactions vary according to recharge. We assume that water flowing from the ground surface is distributed between shallow more permeable layers and deep layers. This repartition mostly depends on recharge rates. With high recharge, groundwater levels are high and subsurface streamlines are relatively short between recharge areas and existing draining rivers, which constitutes a very dense network. Therefore, most of the groundwater fluxes occur through the more permeable shallow layers. With low recharge, groundwater levels are lower, and river and shallow permeable levels are partly disconnected from each other. This induces a general increase of the groundwater streamlines length from the recharge areas to more sporadic discharge areas, and more fluxes occur through the deep layers. Recharge conditions and river-aquifer interactions have changed over the last thousands of years, due to change in precipitation, temperatures, existence of permafrost, etc. They have strongly influenced deep groundwater fluxes and can explain current groundwater age and flux distribution. To study these interactions, a regional-scale finite difference flow model was implemented. The model covers an area of 1400 km 2 , a depth of 1 km, and the topography is characteristic of Brittany. As rivers are mainly fed by groundwater drainage, seepages faces

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...

Numerical Analysis on Seepage in the deep overburden CFRD

Science.gov (United States)

Zeyu, GUO; Junrui, CHAI; Yuan, QIN

2017-12-01

There are many problems in the construction of hydraulic structures on deep overburden because of its complex foundation structure and poor geological condition. Seepage failure is one of the main problems. The Combination of the seepage control system of the face rockfill dam and the deep overburden can effectively control the seepage of construction of the concrete face rockfill dam on the deep overburden. Widely used anti-seepage measures are horizontal blanket, waterproof wall, curtain grouting and so on, but the method, technique and its effect of seepage control still have many problems thus need further study. Due to the above considerations, Three-dimensional seepage field numerical analysis based on practical engineering case is conducted to study the seepage prevention effect under different seepage prevention methods, which is of great significance to the development of dam technology and the development of hydropower resources in China.

Volume fracturing of deep shale gas horizontal wells

Directory of Open Access Journals (Sweden)

Tingxue Jiang

2017-03-01

Full Text Available Deep shale gas reservoirs buried underground with depth being more than 3500 m are characterized by high in-situ stress, large horizontal stress difference, complex distribution of bedding and natural cracks, and strong rock plasticity. Thus, during hydraulic fracturing, these reservoirs often reveal difficult fracture extension, low fracture complexity, low stimulated reservoir volume (SRV, low conductivity and fast decline, which hinder greatly the economic and effective development of deep shale gas. In this paper, a specific and feasible technique of volume fracturing of deep shale gas horizontal wells is presented. In addition to planar perforation, multi-scale fracturing, full-scale fracture filling, and control over extension of high-angle natural fractures, some supporting techniques are proposed, including multi-stage alternate injection (of acid fluid, slick water and gel and the mixed- and small-grained proppant to be injected with variable viscosity and displacement. These techniques help to increase the effective stimulated reservoir volume (ESRV for deep gas production. Some of the techniques have been successfully used in the fracturing of deep shale gas horizontal wells in Yongchuan, Weiyuan and southern Jiaoshiba blocks in the Sichuan Basin. As a result, Wells YY1HF and WY1HF yielded initially 14.1 × 104 m3/d and 17.5 × 104 m3/d after fracturing. The volume fracturing of deep shale gas horizontal well is meaningful in achieving the productivity of 50 × 108 m3 gas from the interval of 3500–4000 m in Phase II development of Fuling and also in commercial production of huge shale gas resources at a vertical depth of less than 6000 m.

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 Orbital Sub-Q Hyaluronic Acid Filler Injection for Enophthalmic Sighted Eyes in Parry-Romberg Syndrome.

Science.gov (United States)

Feldman, Ilan; Sheptulin, Vladimir A; Grusha, Yaroslav O; Malhotra, Raman

2018-01-23

The authors present a consecutive series of deep orbital Sub-Q injections to treat enophthalmic sighted eyes in Parry-Romberg syndrome patients. Retrospective, interventional case series in 2 centers. Data were collected on patient demographics, Parry-Romberg syndrome onset age, previous orbital and eyelid surgeries, diplopia, ocular movement restriction before and after the injection, number of injections, interval between injections, indication for any top-up or dissolution of filler, and any other complications. In all cases, the hyaluronic acid gel used was Restylane Sub-Q + Lidocaine. A total of 8 injections on 3 patients with Parry-Romberg syndrome, and significant enophthalmos is reported. All injections were with deep orbital Sub-Q filler. All patients were females, aged 32, 24, and 52 years old while their symptoms started at 15, 16, and 30 years old, respectively. None had orbital surgery prior to the injection. Follow up period was 2, 7, and 5 years respectively. All presented a significant enophthalmos of 4 mm which reduced to 1 mm after the injection, and duration effect was 18, 24, and 20 months, respectively. We observed a significant improvement in enophthalmos, lagophthalmos, exposure keratopathy, and even ocular motility. Lagophthalmos improved from 1, 4, and 7 mm to 0, 1, and 2 mm post injection. Ocular motility improved with no onset of new limitation or diplopia. Lower eyelid retraction increased in 1 patient after orbital injection. No other complications occurred. Deep orbital Sub-Q hyaluronic injection for treatment of enophthalmos in Parry-Romberg syndrome is an useful option in sighted eyes.

Deep-sea genetic resources: New frontiers for science and stewardship in areas beyond national jurisdiction

Science.gov (United States)

Harden-Davies, Harriet

2017-03-01

The deep-sea is a large source of marine genetic resources (MGR), which have many potential uses and are a growing area of research. Much of the deep-sea lies in areas beyond national jurisdiction (ABNJ), including 65% of the global ocean. MGR in ABNJ occupy a significant gap in the international legal framework. Access and benefit sharing of MGR is a key issue in the development of a new international legally-binding instrument under the United Nations Convention on the Law of the Sea (UNCLOS) for the conservation and sustainable use of marine biological diversity in ABNJ. This paper examines how this is relevant to deep-sea scientific research and identifies emerging challenges and opportunities. There is no internationally agreed definition of MGR, however, deep-sea genetic resources could incorporate any biological material including genes, proteins and natural products. Deep-sea scientific research is the key actor accessing MGR in ABNJ and sharing benefits such as data, samples and knowledge. UNCLOS provides the international legal framework for marine scientific research, international science cooperation, capacity building and marine technology transfer. Enhanced implementation could support access and benefit sharing of MGR in ABNJ. Deep-sea scientific researchers could play an important role in informing practical new governance solutions for access and benefit sharing of MGR that promote scientific research in ABNJ and support deep-sea stewardship. Advancing knowledge of deep-sea biodiversity in ABNJ, enhancing open-access to data and samples, standardisation and international marine science cooperation are significant potential opportunity areas.

40Ar/39Ar studies of deep sea igneous rocks

International Nuclear Information System (INIS)

Seidemann, D.

1978-01-01

An attempt to date deep-sea igneous rocks reliably was made using the 40 Ar/ 39 Ar dating technique. It was determined that the 40 Ar/ 39 Ar incremental release technique could not be used to eliminate the effects of excess radiogenic 40 Ar in deep-sea basalts. Excess 40 Ar is released throughout the extraction temperature range and cannot be distinguished from 40 Ar generated by in situ 40 K decay. The problem of the reduction of K-Ar dates associated with sea water alteration of deep-sea igneous rocks could not be resolved using the 40 Ar/ 39 Ar technique. Irradiation induced 39 Ar loss and/or redistribution in fine-grained and altered igneous rocks results in age spectra that are artifacts of the experimental procedure and only partly reflect the geologic history of the sample. Therefore, caution must be used in attributing significance to age spectra of fine grained and altered deep-sea igneous rocks. Effects of 39 Ar recoil are not important for either medium-grained (or coarser) deep-sea rocks or glasses because only a small fraction of the 39 Ar recoils to channels of easy diffusion, such as intergranular boundaries or cracks, during the irradiation. (author)

In-Home Sleep Recordings in Military Veterans With Posttraumatic Stress Disorder Reveal Less REM and Deep Sleep <1 Hz

Directory of Open Access Journals (Sweden)

Julie A. Onton

2018-05-01

Full Text Available Veterans with posttraumatic stress disorder (PTSD often report suboptimal sleep quality, often described as lack of restfulness for unknown reasons. These experiences are sometimes difficult to objectively quantify in sleep lab assessments. Here, we used a streamlined sleep assessment tool to record in-home 2-channel electroencephalogram (EEG with concurrent collection of electrodermal activity (EDA and acceleration. Data from a single forehead channel were transformed into a whole-night spectrogram, and sleep stages were classified using a fully automated algorithm. For this study, 71 control subjects and 60 military-related PTSD subjects were analyzed for percentage of time spent in Light, Hi Deep (1–3 Hz, Lo Deep (<1 Hz, and rapid eye movement (REM sleep stages, as well as sleep efficiency and fragmentation. The results showed a significant tendency for PTSD sleepers to spend a smaller percentage of the night in REM (p < 0.0001 and Lo Deep (p = 0.001 sleep, while spending a larger percentage of the night in Hi Deep (p < 0.0001 sleep. The percentage of combined Hi+Lo Deep sleep did not differ between groups. All sleepers usually showed EDA peaks during Lo, but not Hi, Deep sleep; however, PTSD sleepers were more likely to lack EDA peaks altogether, which usually coincided with a lack of Lo Deep sleep. Linear regressions with all subjects showed that a decreased percentage of REM sleep in PTSD sleepers was accounted for by age, prazosin, SSRIs and SNRIs (p < 0.02, while decreased Lo Deep and increased Hi Deep in the PTSD group could not be accounted for by any factor in this study (p < 0.005. Linear regression models with only the PTSD group showed that decreased REM correlated with self-reported depression, as measured with the Depression, Anxiety, and Stress Scales (DASS; p < 0.00001. DASS anxiety was associated with increased REM time (p < 0.0001. This study shows altered sleep patterns in sleepers with PTSD that can be partially accounted

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

Science.gov (United States)

Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

2015-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

Potential impact of global climate change on benthic deep-sea microbes.

Science.gov (United States)

Danovaro, Roberto; Corinaldesi, Cinzia; Dell'Anno, Antonio; Rastelli, Eugenio

2017-12-15

Benthic deep-sea environments are the largest ecosystem on Earth, covering ∼65% of the Earth surface. Microbes inhabiting this huge biome at all water depths represent the most abundant biological components and a relevant portion of the biomass of the biosphere, and play a crucial role in global biogeochemical cycles. Increasing evidence suggests that global climate changes are affecting also deep-sea ecosystems, both directly (causing shifts in bottom-water temperature, oxygen concentration and pH) and indirectly (through changes in surface oceans' productivity and in the consequent export of organic matter to the seafloor). However, the responses of the benthic deep-sea biota to such shifts remain largely unknown. This applies particularly to deep-sea microbes, which include bacteria, archaea, microeukaryotes and their viruses. Understanding the potential impacts of global change on the benthic deep-sea microbial assemblages and the consequences on the functioning of the ocean interior is a priority to better forecast the potential consequences at global scale. Here we explore the potential changes in the benthic deep-sea microbiology expected in the coming decades using case studies on specific systems used as test models. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Search for parity-nonconservation effects in deep-inelastic μN interaction

International Nuclear Information System (INIS)

Bushnin, Yu.B.; Dunaitsev, A.F.; Dzelyadin, R.I.; Zallo, A.

1976-01-01

The difference in the cross sections for deep inelastic scattering of muons with average momenta 21 GeV/c with right and left helicity at large angles, i.e., with large momentum transfer, has been measured. No statistically-significant dependence of cross sections on the longitudinal polarization of muons has been found, i.e., no parity-nonconservation effects in deep inelastic μN interaction have been observed. The limitations have been obtained for the constant of vector-axial interaction

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

Science.gov (United States)

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.

DeepPIV: Particle image velocimetry measurements using deep-sea, remotely operated vehicles

Science.gov (United States)

Katija, Kakani; Sherman, Alana; Graves, Dale; Klimov, Denis; Kecy, Chad; Robison, Bruce

2015-11-01

The midwater region of the ocean (below the euphotic zone and above the benthos) is one of the largest ecosystems on our planet, yet remains one of the least explored. Little-known marine organisms that inhabit midwater have developed life strategies that contribute to their evolutionary success, and may inspire engineering solutions for societally relevant challenges. Although significant advances in underwater vehicle technologies have improved access to midwater, small-scale, in situ fluid mechanics measurement methods that seek to quantify the interactions that midwater organisms have with their physical environment are lacking. Here we present DeepPIV, an instrumentation package affixed to remotely operated vehicles that quantifies fluid motions from the surface of the ocean down to 4000 m depths. Utilizing ambient suspended particulate, fluid-structure interactions are evaluated on a range of marine organisms in midwater. Initial science targets include larvaceans, biological equivalents of flapping flexible foils, that create mucus houses to filter food. Little is known about the structure of these mucus houses and the function they play in selectively filtering particles, and these dynamics can serve as particle-mucus models for human health. Using DeepPIV, we reveal the complex structures and flows generated within larvacean mucus houses, and elucidate how these structures function. Funding is gratefully acknowledged from the Packard Foundation.

Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method

Directory of Open Access Journals (Sweden)

Yuhan Jia

2017-01-01

Full Text Available Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN and long short-term memory (LSTM to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.

Deep Friction Massage Versus Steroid Injection in the Treatment of Lateral Epicondylitis.

Science.gov (United States)

Yi, Rosemary; Bratchenko, Walter W; Tan, Virak

2018-01-01

The aim of the study was to determine the efficacy of deep friction massage in the treatment of lateral epicondylitis by comparing outcomes with a control group treated with splinting and therapy and with an experimental group receiving a local steroid injection. A randomized clinical trial was conducted to compare outcomes after recruitment of consecutive patients presenting with lateral epicondylitis. Patients were randomized to receive one of 3 treatments: group 1: splinting and stretching, group 2: a cortisone injection, or group 3: a lidocaine injection with deep friction massage. Pretreatment and posttreatment parameters of visual analog scale (VAS) pain ratings, Disabilities of the Arm, Shoulder and Hand (DASH) scores, and grip strength were measured. Outcomes were measured at early follow-up (6-12 weeks) and at 6-month follow-up. There was a significant improvement in VAS pain score in all treatment groups at early follow-up. DASH score and grip strength improved in the cortisone injection group and the deep friction massage group at early follow-up; these parameters did not improve in the splinting and stretching group. At 6-month follow-up, only patients in the deep friction massage group demonstrated a significant improvement in all outcome measures, including VAS pain score, DASH score, and grip strength. Deep friction massage is an effective treatment for lateral epicondylitis and can be used in patients who have failed other nonoperative treatments, including cortisone injection.

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.

Relationship between aging and T1 relaxation time in deep gray matter: A voxel-based analysis.

Science.gov (United States)

Okubo, Gosuke; Okada, Tomohisa; Yamamoto, Akira; Fushimi, Yasutaka; Okada, Tsutomu; Murata, Katsutoshi; Togashi, Kaori

2017-09-01

To investigate age-related changes in T 1 relaxation time in deep gray matter structures in healthy volunteers using magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE). In all, 70 healthy volunteers (aged 20-76, mean age 42.6 years) were scanned at 3T magnetic resonance imaging (MRI). A MP2RAGE sequence was employed to quantify T 1 relaxation times. After the spatial normalization of T 1 maps with the diffeomorphic anatomical registration using the exponentiated Lie algebra algorithm, voxel-based regression analysis was conducted. In addition, linear and quadratic regression analyses of regions of interest (ROIs) were also performed. With aging, voxel-based analysis (VBA) revealed significant T 1 value decreases in the ventral-inferior putamen, nucleus accumbens, and amygdala, whereas T 1 values significantly increased in the thalamus and white matter as well (P time vary by location in deep gray matter. 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:724-731. © 2017 International Society for Magnetic Resonance in Medicine.

Phytohormone supplementation significantly increases growth of Chlamydomonas reinhardtii cultivated for biodiesel production.

Science.gov (United States)

Park, Won-Kun; Yoo, Gursong; Moon, Myounghoon; Kim, Chul Woong; Choi, Yoon-E; Yang, Ji-Won

2013-11-01

Cultivation is the most expensive step in the production of biodiesel from microalgae, and substantial research has been devoted to developing more cost-effective cultivation methods. Plant hormones (phytohormones) are chemical messengers that regulate various aspects of growth and development and are typically active at very low concentrations. In this study, we investigated the effect of different phytohormones on microalgal growth and biodiesel production in Chlamydomonas reinhardtii and their potential to lower the overall cost of commercial biofuel production. The results indicated that all five of the tested phytohormones (indole-3-acetic acid, gibberellic acid, kinetin, 1-triacontanol, and abscisic acid) promoted microalgal growth. In particular, hormone treatment increased biomass production by 54 to 69 % relative to the control growth medium (Tris-acetate-phosphate, TAP). Phytohormone treatments also affected microalgal cell morphology but had no effect on the yields of fatty acid methyl esters (FAMEs) as a percent of biomass. We also tested the effect of these phytohormones on microalgal growth in nitrogen-limited media by supplementation in the early stationary phase. Maximum cell densities after addition of phytohormones were higher than in TAP medium, even when the nitrogen source was reduced to 40 % of that in TAP medium. Taken together, our results indicate that phytohormones significantly increased microalgal growth, particularly in nitrogen-limited media, and have potential for use in the development of efficient microalgal cultivation for biofuel production.

Microbiological characterization of deep geological compartments

International Nuclear Information System (INIS)

Barsotti, V.; Sergeant, C.; Vesvres, M.H.; Coulon, S.; Joulian, C.; Garrido, F.; Ollivier, B.

2012-01-01

Document available in extended abstract form only. Microbial life in deep sediments and Earth's crust is now acknowledged by the scientific world. The deep subsurface biosphere contributes significantly to fundamental biogeochemical processes. However, despite great advances in geo-microbiological studies, deep terrestrial ecosystems are microbiologically poorly understood, mainly due to their inaccessibility. The drilling down to the base of the Triassic (1980 meters deep) in the geological formations of the eastern Paris Basin performed by ANDRA (EST433) in 2008 provides us a good opportunity to explore the deep biosphere. We conditioned the samples on the coring site, in as aseptic conditions as possible. In addition to storage at atmospheric pressure, a portion of the four Triassic samples was placed in a 190 bars pressurized chamber to investigate the influence of the conservation pressure factor on the found microflora. In parallel, in order to evaluate a potential bacterial contamination of the cores by the drilling fluids, samples of mud just before each sample drilling were taken and analyzed. The microbial exploration can be divided in two parts: - A cultural approach in different culture media for metabolic groups as methanogens, fermenters and sulphate reducing bacteria to stimulate their growth and to isolate microbial cells still viable. - A molecular approach by direct extraction of genomic DNA from the geological samples to explore a larger biodiversity. The limits are here the difficulties to extract DNA from these low biomass containing rocks. After comparison and optimization of several DNA extraction methods, the bacterial diversity present in rock cores was analyzed using DGGE (Denaturating Gel Gradient Electrophoresis) and cloning. The detailed results of all these investigations will be presented: - Despite all 400 cultural conditions experimented (with various media, salinities, temperatures, conservation pressure, agitation), no viable and

Deep Space Spaceflight: The Challenge of Crew Performance in Autonomous Operations

Science.gov (United States)

Thaxton, S. S.; Williams, T. J.; Norsk, P.; Zwart, S.; Crucian, B.; Antonsen, E. L.

2018-02-01

Distance from Earth and limited communications in future missions will increase the demands for crew autonomy and dependence on automation, and Deep Space Gateway presents an opportunity to study the impacts of these increased demands on human performance.

Formation of metal and dielectric liners using a solution process for deep trench capacitors.

Science.gov (United States)

Ham, Yong-Hyun; Kim, Dong-Pyo; Baek, Kyu-Ha; Park, Kun-Sik; Kim, Moonkeun; Kwon, Kwang-Ho; Shin, Hong-Sik; Lee, Kijun; Do, Lee-Mi

2012-07-01

We demonstrated the feasibility of metal and dielectric liners using a solution process for deep trench capacitor application. The deep Si trench via with size of 10.3 microm and depth of 71 microm were fabricated by Bosch process in deep reactive ion etch (DRIE) system. The aspect ratio was about 7. Then, nano-Ag ink and poly(4-vinylphenol) (PVPh) were used to form metal and dielectric liners, respectively. The thicknesses of the Ag and PVPh liners were about 144 and 830 nm, respectively. When the curing temperature of Ag film increased from 120 to 150 degrees C, the sheet resistance decreased rapidly from 2.47 to 0.72 Omega/sq and then slightly decreased to 0.6 Omega/sq with further increasing the curing temperature beyond 150 degrees C. The proposed liner formation method using solution process is a simple and cost effective process for the high capacity of deep trench capacitor.

Deep learning aided decision support for pulmonary nodules diagnosing: a review.

Science.gov (United States)

Yang, Yixin; Feng, Xiaoyi; Chi, Wenhao; Li, Zhengyang; Duan, Wenzhe; Liu, Haiping; Liang, Wenhua; Wang, Wei; Chen, Ping; He, Jianxing; Liu, Bo

2018-04-01

Deep learning techniques have recently emerged as promising decision supporting approaches to automatically analyze medical images for different clinical diagnosing purposes. Diagnosing of pulmonary nodules by using computer-assisted diagnosing has received considerable theoretical, computational, and empirical research work, and considerable methods have been developed for detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades. The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the deep learning aided decision support for pulmonary nodules diagnosing. As far as the authors know, this is the first time that a review is devoted exclusively to deep learning techniques for pulmonary nodules diagnosing.

Processing speed and working memory span: their differential role in superficial and deep memory processes in schizophrenia.

Science.gov (United States)

Brébion, Gildas; Bressan, Rodrigo A; Pilowsky, Lyn S; David, Anthony S

2011-05-01

Previous work has suggested that decrement in both processing speed and working memory span plays a role in the memory impairment observed in patients with schizophrenia. We undertook a study to examine simultaneously the effect of these two factors. A sample of 49 patients with schizophrenia and 43 healthy controls underwent a battery of verbal and visual memory tasks. Superficial and deep encoding memory measures were tallied. We conducted regression analyses on the various memory measures, using processing speed and working memory span as independent variables. In the patient group, processing speed was a significant predictor of superficial and deep memory measures in verbal and visual memory. Working memory span was an additional significant predictor of the deep memory measures only. Regression analyses involving all participants revealed that the effect of diagnosis on all the deep encoding memory measures was reduced to non-significance when processing speed was entered in the regression. Decreased processing speed is involved in verbal and visual memory deficit in patients, whether the task require superficial or deep encoding. Working memory is involved only insofar as the task requires a certain amount of effort.

Deep Brain Stimulation of the internal globus pallidus in refractory Tourette Syndrome.

Science.gov (United States)

Smeets, A Y J M; Duits, A A; Plantinga, B R; Leentjens, A F G; Oosterloo, M; Visser-Vandewalle, V; Temel, Y; Ackermans, L

2016-03-01

Deep Brain Stimulation in psychiatric disorders is becoming an increasingly performed surgery. At present, seven different targets have been stimulated in Tourette Syndrome, including the internal globus pallidus. We describe the effects on tics and comorbid behavioral disorders of Deep Brain Stimulation of the anterior internal globus pallidus in five patients with refractory Tourette Syndrome. This study was performed as an open label study with follow-up assessment between 12 and 38 months. Patients were evaluated twice, one month before surgery and at long-term follow-up. Primary outcome was tic severity, assessed by several scales. Secondary outcomes were comorbid behavioral disorders, mood and cognition. The final position of the active contacts of the implanted electrodes was investigated and side effects were reported. Three males and two females were included with a mean age of 41.6 years (SD 9.7). The total post-operative score on the Yale Global Tic Severity Scale was significantly lower than the pre-operative score (42.2±4.8 versus 12.8±3.8, P=0.043). There was also a significant reduction on the modified Rush Video-Based Tic Rating Scale (13.0±2.0 versus 7.0±1.6, P=0.041) and in the total number of video-rated tics (259.6±107.3 versus 49.6±24.8, P=0.043). No significant difference on the secondary outcomes was found, however, there was an improvement on an individual level for obsessive-compulsive behavior. The final position of the active contacts was variable in our sample and no relationship between position and stimulation effects could be established. Our study suggests that Deep Brain Stimulation of the anterior internal globus pallidus is effective in reducing tic severity, and possibly also obsessive-compulsive behavior, in refractory Tourette patients without serious adverse events or side-effects. Copyright © 2016 Elsevier B.V. All rights reserved.

Deep-UV Emitters and Detectors Based on Lattice-Matched Cubic Oxide Semiconductors (4.2 Optoelectronics)

Science.gov (United States)

2015-05-14

calculated   by   dividing   photo-­‐‑ generated  current  by  the  optical  power  spectrum  of  the   lamp .    A   UV ...the optimized parameters for growth. Efforts led to significant increases in solar?blind detector responsivity (up to 0.1 A/W) with sub-­ nanoamp...Aug-2014 Approved for Public Release; Distribution Unlimited Final Report: Deep- UV Emitters and Detectors Based on Lattice- Matched Cubic Oxide

Evaluating the Factors that Facilitate a Deep Understanding of Data Analysis

Directory of Open Access Journals (Sweden)

Oliver Burmeister

1995-11-01

Full Text Available Ideally the product of tertiary informatic study is more than a qualification, it is a rewarding experience of learning in a discipline area. It should build a desire for a deeper understanding and lead to fruitful research both personally and for the benefit of the wider community. This paper asks: 'What are the factors that lead to this type of quality (deep learning in data analysis?' In the study reported in this paper, students whose general approach to learning was achieving or surface oriented adopted a deep approach when the context encouraged it. An overseas study found a decline in deep learning at this stage of a tertiary program; the contention of this paper is that the opposite of this expected outcome was achieved due to the enhanced learning environment. Though only 15.1% of students involved in this study were deep learners, the data analysis instructional context resulted in 38.8% of students achieving deep learning outcomes. Other factors discovered that contributed to deep learning outcomes were an increase in the intrinsic motivation of students to study the domain area; their prior knowledge of informatics; assessment that sought an integrated, developed yet comprehensive understanding of analytical concepts and processes; and, their learning preferences. The preferences of deep learning students are analyzed in comparison to another such study of professionals in informatics, examining commonalties and differences between this and the wider professional study.

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.

Assessing the Level of Disability, Deep Cervical Flexor Endurance and Fear Avoidance Beliefs in Bankers with Neck Pain

Directory of Open Access Journals (Sweden)

Deptee Warikoo

2013-08-01

Full Text Available Objective: To assess the level of disability, the deep cervical flexor endurance and fear avoidance beliefs (FAB in bankers with neck pain and to find a correlation between disability and deep cervical muscle endurance, FAB and disability, FAB and deep flexor muscle endurance. Methods: It ws an observational study. The Subjects who had neck pain and minimum 5 years’ experience as a Banker participated in the study. Total 100 subjects were selected. All the subjects were assessed for their disability by the neck pain and disability score (NPDI, their deep cervical flexor endurance using Pressure Biofeedback using Cranio-Cervical flexion test (CCFT and Fear Avoidance Belief by using questionnaire( FABQ. Results: It was found that bankers have a moderate level of disability. The results showed an elevated fear avoidance belief with a mean value of FABQ-PA 21.61±4.42 and FABQ-W 37.81± 5.69. The results indicated that a negative correlation was found between NPDI and CCFT (r=0.855. A positive correlation was found between NPDI and FABQ-PA(r=0.337, FABQ-W(r=0.500. In the present study a negative correlation was found between CCFT and FABQ-W(r=0.553, FABQ-PA (0.348 and positive correlation (r=0.540 was found between FABQ-PA and FABQ-W. Conclusion: The present study concluded that there was a significant level of disability and significantly decreased endurance level and increased fear avoidance beliefs (both work and physical activity related among bankers with neck pain. In addition to that there was a significant correlation found between NPDI and CCFT, NPDI and FABQ, CCFT and FABQ, FABQ-W and FABQ-PA.

Study of the inter-relation between shallow and deep aquifers in Mardan Valley, Pakistan

International Nuclear Information System (INIS)

Ishaq Sajjad, M.

1987-04-01

This study concerns the determination of the relationship between shallow and deep aquifers in the Mardan Valley, in Pakistan. The environmental isotopes, 18 O, 2 H, 3 H, 14 C and 13 C were used in conjunction with classical hydrogeological methods to determine the origins of the different groundwaters in the valley. The irrigation water contributes significantly to the water logging of the study area. There is also the evidence of upward leakage from the deep groundwater system which contributes to this effect. There is no evidence of contamination of the deep aquifers as the quality is good. Groundwater movement in the deep aquifer is slow in the order of tens of years

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.

Factors Contributing to Changes in a Deep Approach to Learning in Different Learning Environments

Science.gov (United States)

Postareff, Liisa; Parpala, Anna; Lindblom-Ylänne, Sari

2015-01-01

The study explored factors explaining changes in a deep approach to learning. The data consisted of interviews with 12 students from four Bachelor-level courses representing different disciplines. We analysed and compared descriptions of students whose deep approach either increased, decreased or remained relatively unchanged during their courses.…

Contrasting impacts of light reduction on sediment biogeochemistry in deep- and shallow-water tropical seagrass assemblages (Green Island, Great Barrier Reef).

Science.gov (United States)

Schrameyer, Verena; York, Paul H; Chartrand, Kathryn; Ralph, Peter J; Kühl, Michael; Brodersen, Kasper Elgetti; Rasheed, Michael A

2018-05-01

Seagrass meadows increasingly face reduced light availability as a consequence of coastal development, eutrophication, and climate-driven increases in rainfall leading to turbidity plumes. We examined the impact of reduced light on above-ground seagrass biomass and sediment biogeochemistry in tropical shallow- (∼2 m) and deep-water (∼17 m) seagrass meadows (Green Island, Australia). Artificial shading (transmitting ∼10-25% of incident solar irradiance) was applied to the shallow- and deep-water sites for up to two weeks. While above-ground biomass was unchanged, higher diffusive O 2 uptake (DOU) rates, lower O 2 penetration depths, and higher volume-specific O 2 consumption (R) rates were found in seagrass-vegetated sediments as compared to adjacent bare sand (control) areas at the shallow-water sites. In contrast, deep-water sediment characteristics did not differ between bare sand and vegetated sites. At the vegetated shallow-water site, shading resulted in significantly lower hydrogen sulphide (H 2 S) levels in the sediment. No shading effects were found on sediment biogeochemistry at the deep-water site. Overall, our results show that the sediment biogeochemistry of shallow-water (Halodule uninervis, Syringodium isoetifolium, Cymodocea rotundata and C. serrulata) and deep-water (Halophila decipiens) seagrass meadows with different species differ in response to reduced light. The light-driven dynamics of the sediment biogeochemistry at the shallow-water site could suggest the presence of a microbial consortium, which might be stimulated by photosynthetically produced exudates from the seagrass, which becomes limited due to lower seagrass photosynthesis under shaded conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

KAUST Repository

Kulmanov, Maxat

2017-09-27

Motivation A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. Results We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.

Achieving deep reductions in US transport greenhouse gas emissions: Scenario analysis and policy implications

International Nuclear Information System (INIS)

McCollum, David; Yang, Christopher

2009-01-01

This paper investigates the potential for making deep cuts in US transportation greenhouse gas (GHG) emissions in the long-term (50-80% below 1990 levels by 2050). Scenarios are used to envision how such a significant decarbonization might be achieved through the application of advanced vehicle technologies and fuels, and various options for behavioral change. A Kaya framework that decomposes GHG emissions into the product of four major drivers is used to analyze emissions and mitigation options. In contrast to most previous studies, a relatively simple, easily adaptable modeling methodology is used which can incorporate insights from other modeling studies and organize them in a way that is easy for policymakers to understand. Also, a wider range of transportation subsectors is considered here-light- and heavy-duty vehicles, aviation, rail, marine, agriculture, off-road, and construction. This analysis investigates scenarios with multiple options (increased efficiency, lower-carbon fuels, and travel demand management) across the various subsectors and confirms the notion that there are no 'silver bullet' strategies for making deep cuts in transport GHGs. If substantial emission reductions are to be made, considerable action is needed on all fronts, and no subsectors can be ignored. Light-duty vehicles offer the greatest potential for emission reductions; however, while deep reductions in other subsectors are also possible, there are more limitations in the types of fuels and propulsion systems that can be used. In all cases travel demand management strategies are critical; deep emission cuts will not likely be possible without slowing growth in travel demand across all modes. Even though these scenarios represent only a small subset of the potential futures in which deep reductions might be achieved, they provide a sense of the magnitude of changes required in our transportation system and the need for early and aggressive action if long-term targets are to be met.

Bacteriological examination and biological characteristics of deep frozen bone preserved by gamma sterilization

International Nuclear Information System (INIS)

Pham Quang Ngoc; Le The Trung; Vo Van Thuan; Ho Minh Duc

1999-01-01

To promote the surgical success in Vietnam, we should supply bone allografts of different sizes. For this reason we have developed a standard procedure in procurement, deep freezing, packaging and radiation sterilization of massive bone. The achievement in this attempt will be briefly reported. The dose of 10-15 kGy is proved to be suitable for radiation sterilization of massive bone allografts being treated in clean condition and preserved in deep frozen. Neither deep freezing nor radiation sterilization cause any significant loss of biochemical stability of massive bone allografts especially when deep freezing combines with radiation. There were neither cross infection nor change of biological characteristics found after 6 months of storage since radiation treatment. In addition to results of the previous research and development of tissue grafts for medical care, the deep freezing radiation sterilization has been established for preservation of massive bone that is of high demand for surgery in Vietnam

Increased thalamic gamma band activity correlates with symptom relief following deep brain stimulation in humans with Tourette's syndrome.

Directory of Open Access Journals (Sweden)

Nicholas Maling

Full Text Available Tourette syndrome (TS is an idiopathic, childhood-onset neuropsychiatric disorder, which is marked by persistent multiple motor and phonic tics. The disorder is highly disruptive and in some cases completely debilitating. For those with severe, treatment-refractory TS, deep brain stimulation (DBS has emerged as a possible option, although its mechanism of action is not fully understood. We performed a longitudinal study of the effects of DBS on TS symptomatology while concomitantly examining neurophysiological dynamics. We present the first report of the clinical correlation between the presence of gamma band activity and decreased tic severity. Local field potential recordings from five subjects implanted in the centromedian nucleus (CM of the thalamus revealed a temporal correlation between the power of gamma band activity and the clinical metrics of symptomatology as measured by the Yale Global Tic Severity Scale and the Modified Rush Tic Rating Scale. Additional studies utilizing short-term stimulation also produced increases in gamma power. Our results suggest that modulation of gamma band activity in both long-term and short-term DBS of the CM is a key factor in mitigating the pathophysiology associated with TS.

Deep brain stimulation effects in dystonia: time course of electrophysiological changes in early treatment.

Science.gov (United States)

Ruge, Diane; Tisch, Stephen; Hariz, Marwan I; Zrinzo, Ludvic; Bhatia, Kailash P; Quinn, Niall P; Jahanshahi, Marjan; Limousin, Patricia; Rothwell, John C

2011-08-15

Deep brain stimulation to the internal globus pallidus is an effective treatment for primary dystonia. The optimal clinical effect often occurs only weeks to months after starting stimulation. To better understand the underlying electrophysiological changes in this period, we assessed longitudinally 2 pathophysiological markers of dystonia in patients prior to and in the early treatment period (1, 3, 6 months) after deep brain stimulation surgery. Transcranial magnetic stimulation was used to track changes in short-latency intracortical inhibition, a measure of excitability of GABA(A) -ergic corticocortical connections and long-term potentiation-like synaptic plasticity (as a response to paired associative stimulation). Deep brain stimulation remained on for the duration of the study. Prior to surgery, inhibition was reduced and plasticity increased in patients compared with healthy controls. Following surgery and commencement of deep brain stimulation, short-latency intracortical inhibition increased toward normal levels over the following months with the same monotonic time course as the patients' clinical benefit. In contrast, synaptic plasticity changed rapidly, following a nonmonotonic time course: it was absent early (1 month) after surgery, and then over the following months increased toward levels observed in healthy individuals. We postulate that before surgery preexisting high levels of plasticity form strong memories of dystonic movement patterns. When deep brain stimulation is turned on, it disrupts abnormal basal ganglia signals, resulting in the absent response to paired associative stimulation at 1 month. Clinical benefit is delayed because engrams of abnormal movement persist and take time to normalize. Our observations suggest that plasticity may be a driver of long-term therapeutic effects of deep brain stimulation in dystonia. Copyright © 2011 Movement Disorder Society.

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...

An ultrasound study of gestational and postural changes in the deep venous system of the leg in pregnancy.

Science.gov (United States)

Macklon, N S; Greer, I A; Bowman, A W

1997-02-01

To investigate gestational and postural changes in diameter and blood flow in the proximal deep leg veins during pregnancy. A longitudinal, prospective observational study. The ultrasound department of a teaching maternity hospital. Twenty-four healthy women with uncomplicated singleton pregnancies. Real-time and duplex Doppler ultrasound assessments of the vessel diameter, flow velocity and respiratory flow fluctuation in the proximal deep leg veins of women serially measured from the first trimester of pregnancy to six weeks postnatally. The effects of increasing gestation and the adoption of the left lateral position on the above parameters. An increase in vessel diameter and a fall in flow velocity with increasing gestation was observed. However, no change in venous flow variation was observed. Delivery had reverse effects. Flow velocity was slower in the left than right legs, but on adoption of the left lateral position an increase in flow velocity and venous flow variation was observed in both legs during pregnancy. These data are consistent with the observed increase in incidence and pattern of deep venous thrombosis in pregnancy and may aid interpretation of duplex Doppler ultrasound examinations for deep venous thrombosis in pregnancy. Postural changes should be part of this evaluation. The gravid uterus may not be the sole cause for postural changes in deep venous flow velocity.

Greenland deep boreholes inform on sliding and deformation of the basal ice

Science.gov (United States)

Dahl-Jensen, D.

2017-12-01

Repeated measurements of the deformation of the deep boreholes on the Greenland ice sheet informs on the basal sliding, near basal deformation and in general on the horizontal velocity through the ice. Results of the logging of the boreholes at Dye3, GRIP, NGRIP, NEEM and Camp Century through the last 40 years by the Danish Ice and Climate group will be presented and discussed. The results on the flow will be compared with the information on ice properties, impurity load and bedrock entrained material from the deep ice cores and the radio echo sounding images near the drill sites.The results show that the basal movement often happens in an impurity rich zone above the bedrock while pure basal sliding is limited even in the presence of basal water and significant basal melt.Most of the deep ice core sites are located close to ice divides where the surface velocity is limited so significant basal sliding is not expected. Exceptions are the surface velocities at Camp Century and Dye 3, both being 13 m/yr.Finally, the ongoing deep drilling at EGRIP will shortly be presented where we are drilling in the center of the North East Greenland Ice Stream (NEGIS).

Diverse, rare microbial taxa responded to the Deepwater Horizon deep-sea hydrocarbon plume.

Science.gov (United States)

Kleindienst, Sara; Grim, Sharon; Sogin, Mitchell; Bracco, Annalisa; Crespo-Medina, Melitza; Joye, Samantha B

2016-02-01

The Deepwater Horizon (DWH) oil well blowout generated an enormous plume of dispersed hydrocarbons that substantially altered the Gulf of Mexico's deep-sea microbial community. A significant enrichment of distinct microbial populations was observed, yet, little is known about the abundance and richness of specific microbial ecotypes involved in gas, oil and dispersant biodegradation in the wake of oil spills. Here, we document a previously unrecognized diversity of closely related taxa affiliating with Cycloclasticus, Colwellia and Oceanospirillaceae and describe their spatio-temporal distribution in the Gulf's deepwater, in close proximity to the discharge site and at increasing distance from it, before, during and after the discharge. A highly sensitive, computational method (oligotyping) applied to a data set generated from 454-tag pyrosequencing of bacterial 16S ribosomal RNA gene V4-V6 regions, enabled the detection of population dynamics at the sub-operational taxonomic unit level (0.2% sequence similarity). The biogeochemical signature of the deep-sea samples was assessed via total cell counts, concentrations of short-chain alkanes (C1-C5), nutrients, (colored) dissolved organic and inorganic carbon, as well as methane oxidation rates. Statistical analysis elucidated environmental factors that shaped ecologically relevant dynamics of oligotypes, which likely represent distinct ecotypes. Major hydrocarbon degraders, adapted to the slow-diffusive natural hydrocarbon seepage in the Gulf of Mexico, appeared unable to cope with the conditions encountered during the DWH spill or were outcompeted. In contrast, diverse, rare taxa increased rapidly in abundance, underscoring the importance of specialized sub-populations and potential ecotypes during massive deep-sea oil discharges and perhaps other large-scale perturbations.

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.

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.

The phenomenology of deep-inelastic processes

International Nuclear Information System (INIS)

Moretto, L.G.

1983-01-01

The field of heavy-ion deep-inelastic reactions is reviewed with particular attention to the experimental picture. The most important degrees of freedom involved in the process are identified and illustrated with relevant experiments. Energy dissipation and mass transfer are discussed in terms of particles and/or phonons exchanged in the process. The equilibration of the fragment neutron-to-proton ratios is inspected for evidence of giant isovector resonances. The angular momentum effects are observed in the fragment angular distributions and the angular momentum transfer is inferred from the magnitude and alignment of the fragments spins. The possible sources of light particles accompanying the deep-inelastic reactions are discussed. The use of the sequentially emitted particles as angular momentum probes is illustrated. The significance and uses of a thermalized component emitted by the dinucleus is reviewed. The possible presence of Fermi jets in the prompt component is shown to be critical to the justification of the one-body theories. (orig.)

Increasing the statistical significance of entanglement detection in experiments

Energy Technology Data Exchange (ETDEWEB)

Jungnitsch, Bastian; Niekamp, Soenke; Kleinmann, Matthias; Guehne, Otfried [Institut fuer Quantenoptik und Quanteninformation, Innsbruck (Austria); Lu, He; Gao, Wei-Bo; Chen, Zeng-Bing [Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei (China); Chen, Yu-Ao; Pan, Jian-Wei [Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei (China); Physikalisches Institut, Universitaet Heidelberg (Germany)

2010-07-01

Entanglement is often verified by a violation of an inequality like a Bell inequality or an entanglement witness. Considerable effort has been devoted to the optimization of such inequalities in order to obtain a high violation. We demonstrate theoretically and experimentally that such an optimization does not necessarily lead to a better entanglement test, if the statistical error is taken into account. Theoretically, we show for different error models that reducing the violation of an inequality can improve the significance. We show this to be the case for an error model in which the variance of an observable is interpreted as its error and for the standard error model in photonic experiments. Specifically, we demonstrate that the Mermin inequality yields a Bell test which is statistically more significant than the Ardehali inequality in the case of a photonic four-qubit state that is close to a GHZ state. Experimentally, we observe this phenomenon in a four-photon experiment, testing the above inequalities for different levels of noise.

Deep-Sea Mining With No Net Loss of Biodiversity—An Impossible Aim

Directory of Open Access Journals (Sweden)

Holly J. Niner

2018-03-01

Full Text Available Deep-sea mining is likely to result in biodiversity loss, and the significance of this to ecosystem function is not known. “Out of kind” biodiversity offsets substituting one ecosystem type (e.g., coral reefs for another (e.g., abyssal nodule fields have been proposed to compensate for such loss. Here we consider a goal of no net loss (NNL of biodiversity and explore the challenges of applying this aim to deep seabed mining, based on the associated mitigation hierarchy (avoid, minimize, remediate. We conclude that the industry cannot at present deliver an outcome of NNL. This results from the vulnerable nature of deep-sea environments to mining impacts, currently limited technological capacity to minimize harm, significant gaps in ecological knowledge, and uncertainties of recovery potential of deep-sea ecosystems. Avoidance and minimization of impacts are therefore the only presently viable means of reducing biodiversity losses from seabed mining. Because of these constraints, when and if deep-sea mining proceeds, it must be approached in a precautionary and step-wise manner to integrate new and developing knowledge. Each step should be subject to explicit environmental management goals, monitoring protocols, and binding standards to avoid serious environmental harm and minimize loss of biodiversity. “Out of kind” measures, an option for compensation currently proposed, cannot replicate biodiversity and ecosystem services lost through mining of the deep seabed and thus cannot be considered true offsets. The ecosystem functions provided by deep-sea biodiversity contribute to a wide range of provisioning services (e.g., the exploitation of fish, energy, pharmaceuticals, and cosmetics, play an essential role in regulatory services (e.g., carbon sequestration and are important culturally. The level of “acceptable” biodiversity loss in the deep sea requires public, transparent, and well-informed consideration, as well as wide agreement

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.

Comparison of deep and superficial abdominal muscle activity between experienced Pilates and resistance exercise instructors and controls during stabilization exercise.

Science.gov (United States)

Moon, Ji-Hyun; Hong, Sang-Min; Kim, Chang-Won; Shin, Yun-A

2015-06-01

Pilates and resistance exercises are used for lumbar stabilization training. However, it is unclear which exercise is more effective for lumbar stabilization. In our study, we aimed to compare surface muscle activity and deep muscle thickness during relaxation and spinal stabilization exercise in experienced Pilates and resistance exercise instructors. This study is a retrospective case control study set in the Exercise Prescription Laboratory and Sports Medicine Center. The participants included Pilates instructors (mean years of experience, 3.20±1.76; n=10), resistance exercise instructors (mean years of experience, 2.53±0.63; n=10), and controls (n=10). The participants performed 4 different stabilization exercises: abdominal drawing-in maneuver, bridging, roll-up, and one-leg raise. During the stabilization exercises, surface muscle activity was measured with electromyography, whereas deep muscle thickness was measured by ultrasound imaging. During the 4 stabilization exercises, the thickness of the transverse abdominis (TrA) was significantly greater in the Pilates-trained group than the other 2 other groups. The internal oblique (IO) thickness was significantly greater in the Pilates- and resistance-trained group than the control group, during the 4 exercises. However, the surface muscle activities were similar between the groups. Both Pilates and resistance exercise instructors had greater activation of deep muscles, such as the TrA and IO, than the control subjects. Pilates and resistance exercise are both effective for increasing abdominal deep muscle thickness.

Comparison of the induced fields using different coil configurations during deep transcranial magnetic stimulation.

Directory of Open Access Journals (Sweden)

Mai Lu

Full Text Available Stimulation of deeper brain structures by transcranial magnetic stimulation (TMS plays a role in the study of reward and motivation mechanisms, which may be beneficial in the treatment of several neurological and psychiatric disorders. However, electric field distributions induced in the brain by deep transcranial magnetic stimulation (dTMS are still unknown. In this paper, the double cone coil, H-coil and Halo-circular assembly (HCA coil which have been proposed for dTMS have been numerically designed. The distributions of magnetic flux density, induced electric field in an anatomically based realistic head model by applying the dTMS coils were numerically calculated by the impedance method. Results were compared with that of standard figure-of-eight (Fo8 coil. Simulation results show that double cone, H- and HCA coils have significantly deep field penetration compared to the conventional Fo8 coil, at the expense of induced higher and wider spread electrical fields in superficial cortical regions. Double cone and HCA coils have better ability to stimulate deep brain subregions compared to that of the H-coil. In the mean time, both double cone and HCA coils increase risk for optical nerve excitation. Our results suggest although the dTMS coils offer new tool with potential for both research and clinical applications for psychiatric and neurological disorders associated with dysfunctions of deep brain regions, the selection of the most suitable coil settings for a specific clinical application should be based on a balanced evaluation between stimulation depth and focality.

Deep structure and origin of active volcanoes in China

Directory of Open Access Journals (Sweden)

Dapeng Zhao

2010-10-01

Full Text Available We synthesize significant recent results on the deep structure and origin of the active volcanoes in mainland China. Magmatism in the western Pacific arc and back-arc areas is caused by dehydration of the subducting slab and by corner flow in the mantle wedge, whereas the intraplate magmatism in China has different origins. The active volcanoes in Northeast China (such as the Changbai and Wudalianchi are caused by hot upwelling in the big mantle wedge (BMW above the stagnant slab in the mantle transition zone and deep slab dehydration as well. The Tengchong volcano in Southwest China is caused by a similar process in the BMW above the subducting Burma microplate (or Indian plate. The Hainan volcano in southernmost China is a hotspot fed by a lower-mantle plume which may be associated with the Pacific and Philippine Sea slabs’ deep subduction in the east and the Indian slab’s deep subduction in the west down to the lower mantle. The stagnant slab finally collapses down to the bottom of the mantle, which can trigger the upwelling of hot mantle materials from the lower mantle to the shallow mantle beneath the subducting slabs and may cause the slab–plume interactions.

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 ...

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....

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...

Deep hydrotreating of middle distillates from crude and shale oils

Energy Technology Data Exchange (ETDEWEB)

Landau, M.V. [The Blechner Center for Industrial Catalysis and Process Development, Ben-Gurion University of the Negev, Beer-Sheva (Israel)

1997-06-20

The potential scientific and technological solutions to the problems that appear as a result of shifting the hydrotreating of crude oil middle distillates and shale oils from the `normal` to the `deep` mode are considered on the basis of the reactivities and transformation routes of the least-reactive sulfur-, nitrogen-, and oxygen-containing compounds. The efficiency of selecting the optimal feedstock, increasing the process severity, improving the catalysts activity, and using alternative catalytic routes are compared, taking into account the specific issues related to deep hydrodesulfurization/hydrodenitrogenation/hydrodeoxygenation, i.e., chemical aspects, kinetics and catalysts

Decadal phytoplankton dynamics in response to episodic climatic disturbances in a subtropical deep freshwater ecosystem.

Science.gov (United States)

Ko, Chia-Ying; Lai, Chao-Chen; Hsu, Huang-Hsiung; Shiah, Fuh-Kwo

2017-02-01

Information of the decadal timescale effects of episodic climatic disturbances (i.e., typhoons) on phytoplankton in freshwater ecosystems have received less attention and fewer seasonal evaluations partly due to the lack of long-term time-series monitoring data in typhoon prevailing areas. Through field observations of a total 36 typhoon cases in a subtropical deep freshwater ecosystem in the period of 2005-2014, we quantified phytoplankton biomass, production and growth rate in response to meteorological and hydrological changes in the weeks before, during and after typhoons between summer and autumn, and also investigated the effects of typhoon characteristics on the aforementioned phytoplankton responses. The results showed that phytoplankton exposed to typhoon disturbances generally exhibited an increasing trend over the weeks before, during and after typhoons in summer but varied in autumn. The correlations and multivariate regressions showed different contributions of meteorological and hydrological variables to individual phytoplankton responses before, during and after typhoons between seasons. The post-typhoon weeks (i.e., within two weeks after a typhoon had passed) were especially important for the timeline of phytoplankton increases and with a detectable seasonal variation that the chlorophyll a concentration significantly increased in autumn whereas both primary production and growth rate were associated with significant changes in summer. Additionally, phytoplankton responses during the post-typhoon weeks were significantly different between discrete or continuous types of typhoon events. Our work illustrated the fact that typhoons did influence phytoplankton responses in the subtropical deep freshwater ecosystem and typhoon passages in summer and autumn affected the phytoplankton dynamics differently. Nevertheless, sustained and systematic monitoring in order to advance our understanding of the role of typhoons between seasons in the modulation of

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...

Temporomandibular disorders and psychological status in adult patients with a deep bite

DEFF Research Database (Denmark)

Sonnesen, Liselotte; Svensson, Peter

2008-01-01

Temporomandibular disorders (TMDs) and psychological status were examined in adult patients with a deep bite and compared with an adult age- and gender-matched control group with neutral occlusion. The deep bite group consisted of 20 females (mean age 30.3 years) and 10 males (mean age 33.1 years......). The control group comprised 20 females (mean age 29.4 years) and 10 males (mean age 34.2 years). TMD examination, according to the Research Diagnostic Criteria for TMD (RDC/TMD), cephalometric lateral radiographs, registration of occlusion, and bite force were performed. To test the mean differences between...... group compared with the controls. Somatization scores were significantly higher in the deep bite group compared with the controls (P psychological...

Deep eutectic solvents as performance additives in biphasic reactions

NARCIS (Netherlands)

Lan, Dongming; Wang, Xuping; Zhou, Pengfei; Hollmann, F.; Wang, Yonghua

2017-01-01

Deep eutectic solvents act as surfactants in biphasic (hydrophobic/aqueous) reaction mixtures enabling higher interfacial surface areas at lower mechanical stress as compared to simple emulsions. Exploiting this effect the rate of a chemoenzymatic epoxidation reaction was increased more than

Distinguishing bulk traps and interface states in deep-level transient spectroscopy

International Nuclear Information System (INIS)

Coelho, A V P; Adam, M C; Boudinov, H

2011-01-01

A new method for the distinction of discrete bulk deep levels and interface states related peaks in deep-level transient spectroscopy spectra is proposed. The measurement of two spectra using different reverse voltages while keeping pulse voltage fixed causes different peak maximum shifts in each case: for a reverse voltage modulus increase, a bulk deep-level related peak maximum will remain unchanged or shift towards lower temperatures while only interface states related peak maximum will be able to shift towards higher temperatures. This method has the advantage of being non-destructive and also works in the case of bulk traps with strong emission rate dependence on the electric field. Silicon MOS capacitors and proton implanted GaAs Schottky diodes were employed to experimentally test the method.

Improving face image extraction by using deep learning technique

Science.gov (United States)

Xue, Zhiyun; Antani, Sameer; Long, L. R.; Demner-Fushman, Dina; Thoma, George R.

2016-03-01

The National Library of Medicine (NLM) has made a collection of over a 1.2 million research articles containing 3.2 million figure images searchable using the Open-iSM multimodal (text+image) search engine. Many images are visible light photographs, some of which are images containing faces ("face images"). Some of these face images are acquired in unconstrained settings, while others are studio photos. To extract the face regions in the images, we first applied one of the most widely-used face detectors, a pre-trained Viola-Jones detector implemented in Matlab and OpenCV. The Viola-Jones detector was trained for unconstrained face image detection, but the results for the NLM database included many false positives, which resulted in a very low precision. To improve this performance, we applied a deep learning technique, which reduced the number of false positives and as a result, the detection precision was improved significantly. (For example, the classification accuracy for identifying whether the face regions output by this Viola- Jones detector are true positives or not in a test set is about 96%.) By combining these two techniques (Viola-Jones and deep learning) we were able to increase the system precision considerably, while avoiding the need to manually construct a large training set by manual delineation of the face regions.

Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology.

Science.gov (United States)

Zhang, Jing; Song, Yanlin; Xia, Fan; Zhu, Chenjing; Zhang, Yingying; Song, Wenpeng; Xu, Jianguo; Ma, Xuelei