Hypointensity on postcontrast MR imaging from compression of the sacral promontory in enlarged uterus with huge leiomyoma and adenomyosis

International Nuclear Information System (INIS)

Uotani, Kensuke; Monzawa, Shuichi; Adachi, Shuji; Takemori, Masayuki; Kaji, Yasushi; Sugimura, Kazuro

2007-01-01

In patients with huge leiomyoma and with adenomyosis of the uterus, a peculiar area of hypointensity was occasionally observed on postcontrast magnetic resonance (MR) imaging in the dorsal portion of the enlarged uterus near the sacral promontory. We describe the imaging characteristics of these MR findings and correlate them with histopathological findings to examine whether the areas represent specific pathological changes. Ten patients with huge leiomyomas and two with huge adenomyotic lesions whose imaging revealed the hypointensity were enrolled. All had enlarged uteri that extended beyond the sacral promontory. MR findings of the hypointense areas were evaluated and correlated with histopathological findings in 5 patients with leiomyoma and two with adenomyosis who had hysterectomy. The ten patients with leiomyoma showed flare-shaped hypointensity arising from the dorsal surface of the uterine body that extended deep into the tumor. The base of the hypointense areas was narrow in 5 patients with intramural leiomyoma and broad in five with subserosal leiomyoma. Two patients with adenomyosis showed nodular-shaped areas of hypointensity in front of the sacral promontory. Precontrast T 1 - and T 2 -weighted MR images showed no signal abnormalities in the portions corresponding to the hypointensity in any of the 12 patients. Pathological examinations showed no specific findings in the portions corresponding to the hypointensity in the 7 patients who had hysterectomy. The areas of hypointensity may represent functional changes, such as decreased localized blood flow caused by compression of the sacral promontory. (author)

Huge cystic craniopharyngioma with unusual extensions

Energy Technology Data Exchange (ETDEWEB)

Kitano, I.; Yoneda, K.; Yamakawa, Y.; Fukui, M.; Kinoshita, K.

1981-09-01

The findings on computed tomography (CT) of a huge cystic craniopharyngioma in a 3-year-old girl are described. The cyst occupied both anterior cranial fossae and a part of it extended to the region of the third ventricle which was displaced posteriorly. The tumor showed no contrast enhancement after the intravenous administration of contrast medium.

Inflammatory pseudotumor causing deep vein thrombosis after metal-on-metal hip resurfacing arthroplasty.

LENUS (Irish Health Repository)

Memon, Adeel Rasool

2013-01-01

Metal-on-metal hip resurfacings have recently been associated with a variety of complications resulting from adverse reaction to metal debris. We report a case of extensive soft tissue necrosis associated with a huge pelvic mass causing extensive deep vein thrombosis of the lower limb secondary to mechanical compression of the iliac vein. This is a rare and unusual cause of deep vein thrombosis after metal-on-metal hip resurfacing arthroplasty.

Simulation of noisy dynamical system by Deep Learning

Science.gov (United States)

Yeo, Kyongmin

2017-11-01

Deep learning has attracted huge attention due to its powerful representation capability. However, most of the studies on deep learning have been focused on visual analytics or language modeling and the capability of the deep learning in modeling dynamical systems is not well understood. In this study, we use a recurrent neural network to model noisy nonlinear dynamical systems. In particular, we use a long short-term memory (LSTM) network, which constructs internal nonlinear dynamics systems. We propose a cross-entropy loss with spatial ridge regularization to learn a non-stationary conditional probability distribution from a noisy nonlinear dynamical system. A Monte Carlo procedure to perform time-marching simulations by using the LSTM is presented. The behavior of the LSTM is studied by using noisy, forced Van der Pol oscillator and Ikeda equation.

Newtonian self-gravitating system in a relativistic huge void universe model

Energy Technology Data Exchange (ETDEWEB)

Nishikawa, Ryusuke; Nakao, Ken-ichi [Department of Mathematics and Physics, Graduate School of Science, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi, Osaka 558-8585 (Japan); Yoo, Chul-Moon, E-mail: ryusuke@sci.osaka-cu.ac.jp, E-mail: knakao@sci.osaka-cu.ac.jp, E-mail: yoo@gravity.phys.nagoya-u.ac.jp [Division of Particle and Astrophysical Science, Graduate School of Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8602 (Japan)

2016-12-01

We consider a test of the Copernican Principle through observations of the large-scale structures, and for this purpose we study the self-gravitating system in a relativistic huge void universe model which does not invoke the Copernican Principle. If we focus on the the weakly self-gravitating and slowly evolving system whose spatial extent is much smaller than the scale of the cosmological horizon in the homogeneous and isotropic background universe model, the cosmological Newtonian approximation is available. Also in the huge void universe model, the same kind of approximation as the cosmological Newtonian approximation is available for the analysis of the perturbations contained in a region whose spatial size is much smaller than the scale of the huge void: the effects of the huge void are taken into account in a perturbative manner by using the Fermi-normal coordinates. By using this approximation, we derive the equations of motion for the weakly self-gravitating perturbations whose elements have relative velocities much smaller than the speed of light, and show the derived equations can be significantly different from those in the homogeneous and isotropic universe model, due to the anisotropic volume expansion in the huge void. We linearize the derived equations of motion and solve them. The solutions show that the behaviors of linear density perturbations are very different from those in the homogeneous and isotropic universe model.

Shallow and deep dynamic stall for flapping low Reynolds number airfoils

Energy Technology Data Exchange (ETDEWEB)

Ol, Michael V. [Wright-Patterson AFB, Air Force Research Lab., Dayton, OH (United States); Bernal, Luis; Kang, Chang-Kwon; Shyy, Wei [University of Michigan, Department of Aerospace Engineering, Ann Arbor, MI (United States)

2009-05-15

We consider a combined experimental (based on flow visualization, direct force measurement and phase-averaged 2D particle image velocimetry in a water tunnel), computational (2D Reynolds-averaged Navier-Stokes) and theoretical (Theodorsen's formula) approach to study the fluid physics of rigid-airfoil pitch-plunge in nominally two-dimensional conditions. Shallow-stall (combined pitch-plunge) and deep-stall (pure-plunge) are compared at a reduced frequency commensurate with flapping-flight in cruise in nature. Objectives include assessment of how well attached-flow theory can predict lift coefficient even in the presence of significant separation, and how well 2D velocimetry and 2D computation can mutually validate one another. The shallow-stall case shows promising agreement between computation and experiment, while in the deep-stall case, the computation's prediction of flow separation lags that of the experiment, but eventually evinces qualitatively similar leading edge vortex size. Dye injection was found to give good qualitative match with particle image velocimetry in describing leading edge vortex formation and return to flow reattachment, and also gave evidence of strong spanwise growth of flow separation after leading-edge vortex formation. Reynolds number effects, in the range of 10,000-60,000, were found to influence the size of laminar separation in those phases of motion where instantaneous angle of attack was well below stall, but have limited effect on post-stall flowfield behavior. Discrepancy in lift coefficient time history between experiment, theory and computation was mutually comparable, with no clear failure of Theodorsen's formula. This is surprising and encouraging, especially for the deep-stall case, because the theory's assumptions are clearly violated, while its prediction of lift coefficient remains useful for capturing general trends. (orig.)

Dragon kings of the deep sea: marine particles deviate markedly from the common number-size spectrum.

Science.gov (United States)

Bochdansky, Alexander B; Clouse, Melissa A; Herndl, Gerhard J

2016-03-04

Particles are the major vector for the transfer of carbon from the upper ocean to the deep sea. However, little is known about their abundance, composition and role at depths greater than 2000 m. We present the first number-size spectrum of bathy- and abyssopelagic particles to a depth of 5500 m based on surveys performed with a custom-made holographic microscope. The particle spectrum was unusual in that particles of several millimetres in length were almost 100 times more abundant than expected from the number spectrum of smaller particles, thereby meeting the definition of "dragon kings." Marine snow particles overwhelmingly contributed to the total particle volume (95-98%). Approximately 1/3 of the particles in the dragon-king size domain contained large amounts of transparent exopolymers with little ballast, which likely either make them neutrally buoyant or cause them to sink slowly. Dragon-king particles thus provide large volumes of unique microenvironments that may help to explain discrepancies in deep-sea biogeochemical budgets.

Huge uterine-cervical diverticulum mimicking as a cyst

Directory of Open Access Journals (Sweden)

S Chufal

2012-01-01

Full Text Available Here we report an incidental huge uterine-cervical diverticulum from a total abdominal hysterectomy specimen in a perimenopausal woman who presented with acute abdominal pain. The diverticulum was mimicking with various cysts present in the lateral side of the female genital tract. Histopathological examination confirmed this to be a cervical diverticulum with communication to uterine cavity through two different openings. They can attain huge size if left ignored for long duration and present a diagnostic challenge to clinicians, radiologists, as well as pathologists because of its extreme rarity. Therefore, diverticula should also be included as a differential diagnosis. Its histopathological confirmation also highlights that diverticula can present as an acute abdomen, requiring early diagnosis with appropriate timely intervention. Immunohistochemistry CD 10 has also been used to differentiate it from a mesonephric cyst.

A parallel solver for huge dense linear systems

Science.gov (United States)

Badia, J. M.; Movilla, J. L.; Climente, J. I.; Castillo, M.; Marqués, M.; Mayo, R.; Quintana-Ortí, E. S.; Planelles, J.

2011-11-01

HDSS (Huge Dense Linear System Solver) is a Fortran Application Programming Interface (API) to facilitate the parallel solution of very large dense systems to scientists and engineers. The API makes use of parallelism to yield an efficient solution of the systems on a wide range of parallel platforms, from clusters of processors to massively parallel multiprocessors. It exploits out-of-core strategies to leverage the secondary memory in order to solve huge linear systems O(100.000). The API is based on the parallel linear algebra library PLAPACK, and on its Out-Of-Core (OOC) extension POOCLAPACK. Both PLAPACK and POOCLAPACK use the Message Passing Interface (MPI) as the communication layer and BLAS to perform the local matrix operations. The API provides a friendly interface to the users, hiding almost all the technical aspects related to the parallel execution of the code and the use of the secondary memory to solve the systems. In particular, the API can automatically select the best way to store and solve the systems, depending of the dimension of the system, the number of processes and the main memory of the platform. Experimental results on several parallel platforms report high performance, reaching more than 1 TFLOP with 64 cores to solve a system with more than 200 000 equations and more than 10 000 right-hand side vectors. New version program summaryProgram title: Huge Dense System Solver (HDSS) Catalogue identifier: AEHU_v1_1 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHU_v1_1.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 87 062 No. of bytes in distributed program, including test data, etc.: 1 069 110 Distribution format: tar.gz Programming language: Fortran90, C Computer: Parallel architectures: multiprocessors, computer clusters Operating system

Connecting slow earthquakes to huge earthquakes

OpenAIRE

Obara, Kazushige; Kato, Aitaro

2016-01-01

Slow earthquakes are characterized by a wide spectrum of fault slip behaviors and seismic radiation patterns that differ from those of traditional earthquakes. However, slow earthquakes and huge megathrust earthquakes can have common slip mechanisms and are located in neighboring regions of the seismogenic zone. The frequent occurrence of slow earthquakes may help to reveal the physics underlying megathrust events as useful analogs. Slow earthquakes may function as stress meters because of th...

Deep Feature Learning and Cascaded Classifier for Large Scale Data

DEFF Research Database (Denmark)

Prasoon, Adhish

from data rather than having a predefined feature set. We explore deep learning approach of convolutional neural network (CNN) for segmenting three dimensional medical images. We propose a novel system integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D......This thesis focuses on voxel/pixel classification based approaches for image segmentation. The main application is segmentation of articular cartilage in knee MRIs. The first major contribution of the thesis deals with large scale machine learning problems. Many medical imaging problems need huge...... amount of training data to cover sufficient biological variability. Learning methods scaling badly with number of training data points cannot be used in such scenarios. This may restrict the usage of many powerful classifiers having excellent generalization ability. We propose a cascaded classifier which...

Dragon kings of the deep sea: marine particles deviate markedly from the common number-size spectrum

NARCIS (Netherlands)

Bochdansky, A.B.; Clouse, M.A.; Herndl, G.

2016-01-01

Particles are the major vector for the transfer of carbon from the upper ocean to the deep sea. However, little is known about their abundance, composition and role at depths greater than 2000?m. We present the first number-size spectrum of bathy- and abyssopelagic particles to a depth of 5500?m

The numbers game

Directory of Open Access Journals (Sweden)

Oli Brown

2008-10-01

Full Text Available Estimates of the potential number of ‘climate changemigrants’ vary hugely. In order to persuade policymakers ofthe need to act and to provide a sound basis for appropriateresponses, there is an urgent need for better analysis, betterdata and better predictions.

From tiny microalgae to huge biorefineries

OpenAIRE

Gouveia, L.

2014-01-01

Microalgae are an emerging research field due to their high potential as a source of several biofuels in addition to the fact that they have a high-nutritional value and contain compounds that have health benefits. They are also highly used for water stream bioremediation and carbon dioxide mitigation. Therefore, the tiny microalgae could lead to a huge source of compounds and products, giving a good example of a real biorefinery approach. This work shows and presents examples of experimental...

Effect of QW thickness and numbers on performance characteristics of deep violet InGaN MQW lasers

Science.gov (United States)

Alahyarizadeh, Gh.; Amirhoseiny, M.; Hassan, Z.

2015-03-01

The performance characteristics of deep violet indium gallium nitride (InGaN) multiquantum well (MQW) laser diodes (LDs) with an emission wavelength of around 390 nm have been investigated using the integrated system engineering technical computer aided design (ISE-TCAD) software. A comparative study on the effect of quantum well (QW) thickness and number on electrical and optical performance of deep violet In0.082Ga0.918N/GaN MQW LDs have been carried out. The simulation results showed that the highest slope efficiency and external differential quantum efficiency (DQE), as well as the lowest threshold current are obtained when the number of wells is two. The different QW thickness values of 2.2, 2.5, 2.8, 3 and 3.2 nm were compared and the best results were achieved for 2.5 nm QW thickness. The radiative recombination rate decreases with increasing QW thickness because of decreasing electron and hole carrier densities in wells. By increasing QW thickness, output power decreases and threshold current increases.

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.

Angola. Petroleum discovery by Elf on the block number 17 in deep water

International Nuclear Information System (INIS)

Anon.

1996-01-01

This article describes the petroleum discovery in deep water in Angola. The drilling was executed by 1365 meters deep and gave a petroleum of good quality. The Elf company emphasizes that it is its third discovery in deep water in the Guinea gulf after Nkossa and Moho in Congo. (N.C.)

Designing A General Deep Web Access Approach Based On A Newly Introduced Factor; Harvestability Factor (HF)

NARCIS (Netherlands)

Khelghati, Mohammadreza; van Keulen, Maurice; Hiemstra, Djoerd

2014-01-01

The growing need of accessing more and more information draws attentions to huge amount of data hidden behind web forms defined as deep web. To make this data accessible, harvesters have a crucial role. Targeting different domains and websites enhances the need to have a general-purpose harvester

Huge mucinous cystadenoma of the pancreas mistaken for a ...

African Journals Online (AJOL)

Cystic tumors of the pancreas are rare and can be confused with pseudocysts.We present a 50 year old woman with a huge mucinous cystadenoma of the pancreas initially diagnosed and managed with a cystojejunostomy and cyst wall biopsy. She required another laparotomy and tumor excision after histological ...

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.

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.

Aggressive angiomyxoma presenting with huge abdominal lump: A case report

Science.gov (United States)

Kumar, Sanjeev; Agrawal, Nikhil; Khanna, Rahul; Khanna, AK

2008-01-01

Agressive angiomyxoma is a rare mesenchymal neoplasm. It mainly presents in females. We here present a case of angiomyxoma presenting as huge abdominal lump along with gluteal swelling. Case note is described along with brief review of literature. PMID:18755035

Umbilicoplasty in children with huge umbilical hernia | Komlatsè ...

African Journals Online (AJOL)

With a mean follow-up of 10 months, we had 10 excellent results and two fair results according to our criteria. Conclusion: Our two lateral fl aps umbilicoplasty is well-adapted to HUH in children. Itis simple and assures a satisfactory anatomical and cosmetic result. Key words: Children, huge umbilical hernia, Togo, umbilical ...

Simulation of droplet impact onto a deep pool for large Froude numbers in different open-source codes

Science.gov (United States)

Korchagova, V. N.; Kraposhin, M. V.; Marchevsky, I. K.; Smirnova, E. V.

2017-11-01

A droplet impact on a deep pool can induce macro-scale or micro-scale effects like a crown splash, a high-speed jet, formation of secondary droplets or thin liquid films, etc. It depends on the diameter and velocity of the droplet, liquid properties, effects of external forces and other factors that a ratio of dimensionless criteria can account for. In the present research, we considered the droplet and the pool consist of the same viscous incompressible liquid. We took surface tension into account but neglected gravity forces. We used two open-source codes (OpenFOAM and Gerris) for our computations. We review the possibility of using these codes for simulation of processes in free-surface flows that may take place after a droplet impact on the pool. Both codes simulated several modes of droplet impact. We estimated the effect of liquid properties with respect to the Reynolds number and Weber number. Numerical simulation enabled us to find boundaries between different modes of droplet impact on a deep pool and to plot corresponding mode maps. The ratio of liquid density to that of the surrounding gas induces several changes in mode maps. Increasing this density ratio suppresses the crown splash.

Huge mucinous cystadenoma of ovary, describing a young patient: case report

Directory of Open Access Journals (Sweden)

Soheila Aminimoghaddam

2017-08-01

Conclusion: Ovarian cysts in young women who are associated with elevated levels of tumor markers and ascites require careful evaluation. Management of ovarian cysts depends on patient's age, size of the cyst, and its histopathological nature. Conservative surgery such as ovarian cystectomy or salpingo-oophorectomy is adequate in mucinous tumors of ovary. Multiple frozen sections are very important to know the malignant variation of this tumor and helps accurate patient management. Surgical expertise is required to prevent complications in huge tumors has distorted the anatomy, so gynecologic oncologist plays a prominent role in management. In this case, beside of the huge tumor and massive ascites uterine and ovaries were preserved by gynecologist oncologist and patient is well up to now.

A Huge Ovarian Dermoid Cyst: Successful Laparoscopic Total Excision

OpenAIRE

Uyanikoglu, Hacer; Dusak, Abdurrahim

2017-01-01

Giant ovarian cysts, ≥15 cm in diameter, are quite rare in women of reproductive age. Here, we present a case of ovarian cyst with unusual presentation treated by laparoscopic surgery. On histology, mass was found to be mature cystic teratoma. The diagnostic and management challenges posed by this huge ovarian cyst were discussed in the light of the literature.

A Huge Ovarian Cyst in a Middle-Aged Iranian Female

Directory of Open Access Journals (Sweden)

Mohammad Kazem Moslemi

2010-05-01

Full Text Available A 38-year-old Iranian woman was found to have a huge ovarian cystic mass. Her presenting symptom was vague abdominal pain and severe abdominal distention. She underwent laparotomy and after surgical removal, the mass was found to be mucinous cystadenoma on histology.

Huge music archives on mobile devices

DEFF Research Database (Denmark)

Blume, H.; Bischl, B.; Botteck, M.

2011-01-01

The availability of huge nonvolatile storage capacities such as flash memory allows large music archives to be maintained even in mobile devices. With the increase in size, manual organization of these archives and manual search for specific music becomes very inconvenient. Automated dynamic...... organization enables an attractive new class of applications for managing ever-increasing music databases. For these types of applications, extraction of music features as well as subsequent feature processing and music classification have to be performed. However, these are computationally intensive tasks...... and difficult to tackle on mobile platforms. Against this background, we provided an overview of algorithms for music classification as well as their computation times and other hardware-related aspects, such as power consumption on various hardware architectures. For mobile platforms such as smartphones...

Huge Mesenteric Lymphangioma – A Rare Cause of Acute Abdomen

African Journals Online (AJOL)

Lymphangiomas are benign congenital masses which occur most commonly in head and neck of children and incidence of mesenteric lymphangiomas is very rare. We report such a case of huge mesenteric lymphangioma in a 20 year old male who presented to us with acute abdomen. Pre-operative diagnosis is difficult ...

Huge cystic craniopharyngioma. Changes of cyst density on computed tomography

Energy Technology Data Exchange (ETDEWEB)

Takamura, Seishi; Fukumura, Akinobu; Ito, Yoshihiro; Itoyama, Yoichi; Matsukado, Yasuhiko

1986-06-01

The findings of computed tomography (CT) of a huge cystic craniopharyngioma in a 57-year-old woman are described. Cyst density varied from low to high levels in a short duration. Follow-up CT scans were regarded as important to diagnose craniopharyngioma. The mechanism of increment of cyst density was discussed.

Strategies in filtering in the number field sieve

NARCIS (Netherlands)

S.H. Cavallar

2000-01-01

textabstractA critical step when factoring large integers by the Number Field Sieve consists of finding dependencies in a huge sparse matrix over the field GF(2), using a Block Lanczos algorithm. Both size and weight (the number of non-zero elements) of the matrix critically affect the running time

61 HUGE BENIGN GRANULOSA CELL TUMOUR IN A 61 YEAR ...

African Journals Online (AJOL)

Dr. E. P. Gharoro

peritoneal cavity, huge right ovarian cyst measuring 37cm/29cm as in figure 1a, weighing 8.3 kg with a thick smooth wall without excrescences on surface. ... is released in the blood during pregnancy and is produced in other conditions such as endometriosis, fibroids and diverticulitis. It is useful in monitoring therapy.

Tiny Grains Give Huge Gains: Nanocrystal–Based Signal Amplification for Biomolecule Detection

Science.gov (United States)

Tong, Sheng; Ren, Binbin; Zheng, Zhilan; Shen, Han; Bao, Gang

2013-01-01

Nanocrystals, despite their tiny sizes, contain thousands to millions of atoms. Here we show that the large number of atoms packed in each metallic nanocrystal can provide a huge gain in signal amplification for biomolecule detection. We have devised a highly sensitive, linear amplification scheme by integrating the dissolution of bound nanocrystals and metal-induced stoichiometric chromogenesis, and demonstrated that signal amplification is fully defined by the size and atom density of nanocrystals, which can be optimized through well-controlled nanocrystal synthesis. Further, the rich library of chromogenic reactions allows implementation of this scheme in various assay formats, as demonstrated by the iron oxide nanoparticle linked immunosorbent assay (ILISA) and blotting assay developed in this study. Our results indicate that, owing to the inherent simplicity, high sensitivity and repeatability, the nanocrystal based amplification scheme can significantly improve biomolecule quantification in both laboratory research and clinical diagnostics. This novel method adds a new dimension to current nanoparticle-based bioassays. PMID:23659350

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.

A huge renal capsular leiomyoma mimicking retroperitoneal sarcoma

Directory of Open Access Journals (Sweden)

Lal Anupam

2009-01-01

Full Text Available A huge left renal capsular leiomyoma mimicking retroperitoneal sarcoma presented in a patient as an abdominal mass. Computed tomography displayed a large heterogeneous retro-peritoneal mass in the left side of the abdomen with inferior and medial displacement as well as loss of fat plane with the left kidney. Surgical exploration revealed a capsulated mass that was tightly adherent to the left kidney; therefore, total tumor resection with radical left nephrectomy was performed. Histopathology ultimately confirmed the benign nature of the mass. This is the largest leiomyoma reported in literature to the best of our knowledge.

A young woman with a huge paratubal cyst

Directory of Open Access Journals (Sweden)

Ceren Golbasi

2016-09-01

Full Text Available Paratubal cysts are asymptomatic embryological remnants. These cysts are usually diagnosed during adolescence and reproductive age. In general, their sizes are small but can be complicated by rupture, torsion, or hemorrhage. Paratubal cysts are often discovered fortuitously on routine ultrasound examination. We report a 19-year-old female patient who presented with irregular menses and abdominal pain. Ultrasound examination revealed a huge cystic mass at the right adnexial area. The diagnosis was confirmed as paratubal cyst during laporotomy and, hence, cystectomy and right salpingectomy were performed. [Cukurova Med J 2016; 41(3.000: 573-576

Nanocellulose, a tiny fiber with huge applications.

Science.gov (United States)

Abitbol, Tiffany; Rivkin, Amit; Cao, Yifeng; Nevo, Yuval; Abraham, Eldho; Ben-Shalom, Tal; Lapidot, Shaul; Shoseyov, Oded

2016-06-01

Nanocellulose is of increasing interest for a range of applications relevant to the fields of material science and biomedical engineering due to its renewable nature, anisotropic shape, excellent mechanical properties, good biocompatibility, tailorable surface chemistry, and interesting optical properties. We discuss the main areas of nanocellulose research: photonics, films and foams, surface modifications, nanocomposites, and medical devices. These tiny nanocellulose fibers have huge potential in many applications, from flexible optoelectronics to scaffolds for tissue regeneration. We hope to impart the readers with some of the excitement that currently surrounds nanocellulose research, which arises from the green nature of the particles, their fascinating physical and chemical properties, and the diversity of applications that can be impacted by this material. Copyright © 2016 Elsevier Ltd. All rights reserved.

The therapy for huge goiter together with hyperthyroidism through 131I case studies

International Nuclear Information System (INIS)

He Jianhua; Yu Wencai; Zeng Qingwen; Wu Congjun

2001-01-01

Objective: 214 cases of the treatment of huge goiter with hyperthyroidism are revised to collect clinic material for the improvement of therapy to hyperthyroidism indications through 131 I. Methods: In all of these cases, patients take a full dose of 131 I based on MC Garack's formula for one time. Results: Among them, 154 resolved, accounting for 72%, 139 of the cases were reduced to normal size, which accounted for 64.9% of the patients. Only 114 cases of patients had side-effect, and during one year 12.1% of them have symptoms of hypothyroidism. Conclusion: The statistics shows that 131 I is convenient, safe, well and with reduces suffering from treating huge goiter with hyperthyroidism

Huge Tongue Lipoma: A Case Report

Directory of Open Access Journals (Sweden)

Mohammad Ali Damghani

2015-03-01

Full Text Available Introduction: Lipomas are among the most common tumors of the human body. However, they are uncommon in the oral cavity and are observed as slow growing, painless, and asymptomatic yellowish submucosal masses. Surgical excision is the treatment of choice and recurrence is not expected.    Case Report: The case of a 30-year-old woman with a huge lipoma on the tip of her tongue since 3 years, is presented. She had difficulty with speech and mastication because the tongue tumor was filling the oral cavity. Clinical examination revealed a yellowish lesion, measuring 8 cm in maximum diameter, protruding from the lingual surface. The tumor was surgically excised with restoration of normal tongue function and histopathological examination of the tumor confirmed that it was a lipoma.   Conclusion:  Tongue lipoma is rarely seen and can be a cause of macroglossia. Surgical excision for lipoma is indicated for symptomatic relief and exclusion of associated malignancy.

Reduction in training time of a deep learning model in detection of lesions in CT

Science.gov (United States)

Makkinejad, Nazanin; Tajbakhsh, Nima; Zarshenas, Amin; Khokhar, Ashfaq; Suzuki, Kenji

2018-02-01

Deep learning (DL) emerged as a powerful tool for object detection and classification in medical images. Building a well-performing DL model, however, requires a huge number of images for training, and it takes days to train a DL model even on a cutting edge high-performance computing platform. This study is aimed at developing a method for selecting a "small" number of representative samples from a large collection of training samples to train a DL model for the could be used to detect polyps in CT colonography (CTC), without compromising the classification performance. Our proposed method for representative sample selection (RSS) consists of a K-means clustering algorithm. For the performance evaluation, we applied the proposed method to select samples for the training of a massive training artificial neural network based DL model, to be used for the classification of polyps and non-polyps in CTC. Our results show that the proposed method reduce the training time by a factor of 15, while maintaining the classification performance equivalent to the model trained using the full training set. We compare the performance using area under the receiveroperating- characteristic curve (AUC).

The SCUBA-2 Cosmology Legacy Survey: the EGS deep field - I. Deep number counts and the redshift distribution of the recovered cosmic infrared background at 450 and 850 μ m

Science.gov (United States)

Zavala, J. A.; Aretxaga, I.; Geach, J. E.; Hughes, D. H.; Birkinshaw, M.; Chapin, E.; Chapman, S.; Chen, Chian-Chou; Clements, D. L.; Dunlop, J. S.; Farrah, D.; Ivison, R. J.; Jenness, T.; Michałowski, M. J.; Robson, E. I.; Scott, Douglas; Simpson, J.; Spaans, M.; van der Werf, P.

2017-01-01

We present deep observations at 450 and 850 μm in the Extended Groth Strip field taken with the SCUBA-2 camera mounted on the James Clerk Maxwell Telescope as part of the deep SCUBA-2 Cosmology Legacy Survey (S2CLS), achieving a central instrumental depth of σ450 = 1.2 mJy beam-1 and σ850 = 0.2 mJy beam-1. We detect 57 sources at 450 μm and 90 at 850 μm with signal-to-noise ratio >3.5 over ˜70 arcmin2. From these detections, we derive the number counts at flux densities S450 > 4.0 mJy and S850 > 0.9 mJy, which represent the deepest number counts at these wavelengths derived using directly extracted sources from only blank-field observations with a single-dish telescope. Our measurements smoothly connect the gap between previous shallower blank-field single-dish observations and deep interferometric ALMA results. We estimate the contribution of our SCUBA-2 detected galaxies to the cosmic infrared background (CIB), as well as the contribution of 24 μm-selected galaxies through a stacking technique, which add a total of 0.26 ± 0.03 and 0.07 ± 0.01 MJy sr-1, at 450 and 850 μm, respectively. These surface brightnesses correspond to 60 ± 20 and 50 ± 20 per cent of the total CIB measurements, where the errors are dominated by those of the total CIB. Using the photometric redshifts of the 24 μm-selected sample and the redshift distributions of the submillimetre galaxies, we find that the redshift distribution of the recovered CIB is different at each wavelength, with a peak at z ˜ 1 for 450 μm and at z ˜ 2 for 850 μm, consistent with previous observations and theoretical models.

How a huge HEP experiment is designed course

CERN Multimedia

CERN. Geneva HR-FAS

2007-01-01

More than twenty years after the idea of building the LHC machine was discussed in a workshop in Lausanne in 1984 for the first time, it is instructive to look back on the historical process which has led the community to where we are today with four huge detectors being commissioned and eagerly awaiting first beam collisions in 2008. The main design principles, detector features and performance characteristics of the ATLAS and CMS detectors will be briefly covered in these two lectures with, as an interlude, a wonderful DVD from ATLAS outreach depicting how particles interact and are detected in the various components of the experiments.

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.

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...

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.

Really big numbers

CERN Document Server

Schwartz, Richard Evan

2014-01-01

In the American Mathematical Society's first-ever book for kids (and kids at heart), mathematician and author Richard Evan Schwartz leads math lovers of all ages on an innovative and strikingly illustrated journey through the infinite number system. By means of engaging, imaginative visuals and endearing narration, Schwartz manages the monumental task of presenting the complex concept of Big Numbers in fresh and relatable ways. The book begins with small, easily observable numbers before building up to truly gigantic ones, like a nonillion, a tredecillion, a googol, and even ones too huge for names! Any person, regardless of age, can benefit from reading this book. Readers will find themselves returning to its pages for a very long time, perpetually learning from and growing with the narrative as their knowledge deepens. Really Big Numbers is a wonderful enrichment for any math education program and is enthusiastically recommended to every teacher, parent and grandparent, student, child, or other individual i...

Deep learning methods for protein torsion angle prediction.

Science.gov (United States)

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

Huge gastric diospyrobezoars successfully treated by oral intake and endoscopic injection of Coca-Cola.

Science.gov (United States)

Chung, Y W; Han, D S; Park, Y K; Son, B K; Paik, C H; Jeon, Y C; Sohn, J H

2006-07-01

A diospyrobezoar is a type of phytobezoar that is considered to be harder than any other types of phytobezoars. Here, we describe a new treatment modality, which effectively and easily disrupted huge gastric diospyrobezoars. A 41-year-old man with a history of diabetes mellitus was admitted with lower abdominal pain and vomiting. Upper gastrointestinal endoscopy revealed three huge, round diospyrobezoars in the stomach. He was made to drink two cans of Coca-Cola every 6 h. At endoscopy the next day, the bezoars were partially dissolved and turned to be softened. We performed direct endoscopic injection of Coca-Cola into each bezoar. At repeated endoscopy the next day, the bezoars were completely dissolved.

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/.

It was huge! Nursing students' first experience at AORN Congress.

Science.gov (United States)

Byrne, Michelle; Cantrell, Kelly; Fletcher, Daphne; McRaney, David; Morris, Kelly

2004-01-01

AN EXPERIENTIAL KNOWLEDGE of mentoring through nursing students' perspectives may enhance AORN's ability to recruit students to perioperative nursing and aid future planning for student involvement in the Association. IN 2003, four first-year nursing students attended the AORN Congress in Chicago with their nursing instructor and mentor. The students' experiences were captured using a thematic analysis to analyze their journals. THE FIVE COMMON THEMES identified were "it was huge," "exhibits," "student program," "exploring the city," and "suggestions for future planning."

A Method for Improving Reliability of Radiation Detection using Deep Learning Framework

International Nuclear Information System (INIS)

Chang, Hojong; Kim, Tae-Ho; Han, Byunghun; Kim, Hyunduk; Kim, Ki-duk

2017-01-01

Radiation detection is essential technology for overall field of radiation and nuclear engineering. Previously, technology for radiation detection composes of preparation of the table of the input spectrum to output spectrum in advance, which requires simulation of numerous predicted output spectrum with simulation using parameters modeling the spectrum. In this paper, we propose new technique to improve the performance of radiation detector. The software in the radiation detector has been stagnant for a while with possible intrinsic error of simulation. In the proposed method, to predict the input source using output spectrum measured by radiation detector is performed using deep neural network. With highly complex model, we expect that the complex pattern between data and the label can be captured well. Furthermore, the radiation detector should be calibrated regularly and beforehand. We propose a method to calibrate radiation detector using GAN. We hope that the power of deep learning may also reach to radiation detectors and make huge improvement on the field. Using improved radiation detector, the reliability of detection would be confident, and there are many tasks remaining to solve using deep learning in nuclear engineering society.

Assisted Diagnosis Research Based on Improved Deep Autoencoder

Directory of Open Access Journals (Sweden)

Ke Zhang-Han

2017-01-01

Full Text Available Deep Autoencoder has the powerful ability to learn features from large number of unlabeled samples and a small number of labeled samples. In this work, we have improved the network structure of the general deep autoencoder and applied it to the disease auxiliary diagnosis. We have achieved a network by entering the specific indicators and predicting whether suffering from liver disease, the network using real physical examination data for training and verification. Compared with the traditional semi-supervised machine learning algorithm, deep autoencoder will get higher accuracy.

Deep groundwater chemistry

International Nuclear Information System (INIS)

Wikberg, P.; Axelsen, K.; Fredlund, F.

1987-06-01

Starting in 1977 and up till now a number of places in Sweden have been investigated in order to collect the necessary geological, hydrogeological and chemical data needed for safety analyses of repositories in deep bedrock systems. Only crystalline rock is considered and in many cases this has been gneisses of sedimentary origin but granites and gabbros are also represented. Core drilled holes have been made at nine sites. Up to 15 holes may be core drilled at one site, the deepest down to 1000 m. In addition to this a number of boreholes are percussion drilled at each site to depths of about 100 m. When possible drilling water is taken from percussion drilled holes. The first objective is to survey the hydraulic conditions. Core drilled boreholes and sections selected for sampling of deep groundwater are summarized. (orig./HP)

A new species of deep-water Holothuroidea (Echinodermata of the genus Synallactes from off western Mexico

Directory of Open Access Journals (Sweden)

Claude Massin

2010-08-01

Full Text Available An undescribed species of Synallactes was collected during a deep-water benthic fauna survey off the Pacific coast of Mexico in the East Pacific, with the R/V El Puma. This new species differs from all the other known Synallactes by the presence of huge massive rods in the tube feet, some of them club-shaped. The later ossicle shape is unique among Holothuroidea. This is the first record of a Synallactes in the Gulf of California.

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.

Successful Vaginal Delivery despite a Huge Ovarian Mucinous Cystadenoma Complicating Pregnancy: A Case Report

Directory of Open Access Journals (Sweden)

Dipak Mandi

2013-12-01

Full Text Available A 22-year-old patient with 9 months of amenorrhea and a huge abdominal swelling was admitted to our institution with an ultrasonography report of a multiloculated cystic space-occupying lesion, almost taking up the whole abdomen (probably of ovarian origin, along with a single live intrauterine fetus. She delivered vaginally a boy baby within 4 hours of admission without any maternal complication, but the baby had features of intrauterine growth restriction along with low birth weight. On the 8th postpartum day, the multiloculated cystic mass, which arose from the right ovary and weighed about 11 kg, was removed via laparotomy. A mucinous cystadenoma with no malignant cells in peritoneal washing was detected in histopathology examination. This report describes a rare case of a successful vaginal delivery despite a huge cystadenoma of the right ovary complicating the pregnancy.

Number Meaning and Number Grammar in English and Spanish

Science.gov (United States)

Bock, Kathryn; Carreiras, Manuel; Meseguer, Enrique

2012-01-01

Grammatical agreement makes different demands on speakers of different languages. Being widespread in the languages of the world, the features of agreement systems offer valuable tests of how language affects deep-seated domains of human cognition and categorization. Number agreement is one such domain, with intriguing evidence that typological…

A case report of surgical debulking for a huge mass of elephantiasis neuromatosa

Science.gov (United States)

Hoshi, Manabu; Ieguchi, Makoto; Taguchi, Susumu; Yamasaki, Shinya

2009-01-01

Achievement of a safe outcome for an extensive mass with hypervascularity in the extremities requires a surgical team skilled in musculoskeletal oncology. We report debulking surgery for a huge mass of elephantiasis neuromatosa in the right leg of a 56-year old man using the novel Ligasure® vessel sealing system. PMID:21139882

Partial ureterectomy for a huge primary leiomyoma of the ureter

International Nuclear Information System (INIS)

Nouralizadeh, A.; Tabib, A.; Taheri, M.; Torbati, P.M.

2010-01-01

A case of a huge primary leiomyoma of the ureter in which only partial ureterectomy was performed is presented. The benign nature of the mass was primarily confirmed with frozen section at the time of surgery and then with immunohistochemistry (IHC). To the best of our knowledge, this case is a unique form of leiomyoma of the ureter due to its large size. There have been only ten cases of primary leiomyoma of the ureter reported since 1955 and all of them were very small in size. Our case is considered to be the eleventh. (author)

Deep web search: an overview and roadmap

NARCIS (Netherlands)

Tjin-Kam-Jet, Kien; Trieschnigg, Rudolf Berend; Hiemstra, Djoerd

2011-01-01

We review the state-of-the-art in deep web search and propose a novel classification scheme to better compare deep web search systems. The current binary classification (surfacing versus virtual integration) hides a number of implicit decisions that must be made by a developer. We make these

A case report of surgical debulking for a huge mass of elephantiasis neuromatosa

Directory of Open Access Journals (Sweden)

Shinya Yamasaki

2009-07-01

Full Text Available Achievement of a safe outcome for an extensive mass with hypervascularity in the extremities requires a surgical team skilled in musculoskeletal oncology. We report debulking surgery for a huge mass of elephantiasis neuromatosa in the right leg of a 56-year old man using the novel Ligasure® vessel sealing system.

Disaster Characteristics and Mitigation Measures of Huge Glacial Debris Flows along the Sichuan-Tibet Railway

Science.gov (United States)

Liu, Jinfeng; You, Yong; Zhang, Guangze; Wang, Dong; Chen, Jiangang; Chen, Huayong

2017-04-01

The Ranwu-Tongmai section of the Sichuan-Tibet Railway passes through the Palongzangbu River basin which locates in the southeast Qinghai-Tibetan Plateau. Due to widely distributed maritime glacier in this area, the huge glacier debris flows are very developed. Consequently, the disastrous glacier debris flows with huge scale (106-108 m3 for one debris flow event) and damage become one of the key influencing factors for the route alignment of the Sichuan-Tibet Railway. The research on disaster characteristics and mitigation measures of huge glacial debris flows in the study area were conducted by the remote sensing interpretation, field investigation, parameter calculation and numerical simulation. Firstly, the distribution of the glaciers, glacier lakes and glacier debris flows were identified and classified; and the disaster characteristics for the huge glacier debris flow were analyzed and summarized. Secondly, the dynamic parameters including the flood peak discharge, debris flow peak discharge, velocity, total volume of a single debris flow event were calculated. Based on the disaster characteristics and the spatial relation with the railway, some mitigation principles and measures were proposed. Finally, the Guxiang Gully, where a huge glacier debris flow with 2*108m3 in volume occurred in 1953, was selected as a typical case to analyze its disaster characteristics and mitigation measures. The interpretation results show that the glacier area is about 970 km2 which accounts for 19% of the total study area. 130 glacier lakes and 102 glacier debris flows were identified and classified. The Sichuan-Tibet Railway passes through 43 glacier debris flows in the study area. The specific disaster characteristics were analyzed and corresponding mitigation measures were proposed for the route selection of the railway. For the Guxiang Gully, a numerical simulation to simulate the deposition condition at the alluvial fan was conducted. the simulation results show that the

Deep Correlated Holistic Metric Learning for Sketch-Based 3D Shape Retrieval.

Science.gov (United States)

Dai, Guoxian; Xie, Jin; Fang, Yi

2018-07-01

How to effectively retrieve desired 3D models with simple queries is a long-standing problem in computer vision community. The model-based approach is quite straightforward but nontrivial, since people could not always have the desired 3D query model available by side. Recently, large amounts of wide-screen electronic devices are prevail in our daily lives, which makes the sketch-based 3D shape retrieval a promising candidate due to its simpleness and efficiency. The main challenge of sketch-based approach is the huge modality gap between sketch and 3D shape. In this paper, we proposed a novel deep correlated holistic metric learning (DCHML) method to mitigate the discrepancy between sketch and 3D shape domains. The proposed DCHML trains two distinct deep neural networks (one for each domain) jointly, which learns two deep nonlinear transformations to map features from both domains into a new feature space. The proposed loss, including discriminative loss and correlation loss, aims to increase the discrimination of features within each domain as well as the correlation between different domains. In the new feature space, the discriminative loss minimizes the intra-class distance of the deep transformed features and maximizes the inter-class distance of the deep transformed features to a large margin within each domain, while the correlation loss focused on mitigating the distribution discrepancy across different domains. Different from existing deep metric learning methods only with loss at the output layer, our proposed DCHML is trained with loss at both hidden layer and output layer to further improve the performance by encouraging features in the hidden layer also with desired properties. Our proposed method is evaluated on three benchmarks, including 3D Shape Retrieval Contest 2013, 2014, and 2016 benchmarks, and the experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.

Airway management of a rare huge-size supraglottic mass

International Nuclear Information System (INIS)

Abou-Zeid, Haitham A.; Al-Ghamdi, Abdel Mohsin A.; Al-Qurain, Abdel-Aziz A.; Mokhazy, Khalid M.

2006-01-01

Laser excision of a huge-sized supraglottic mass nearly obstructing the airway passage is a real challenge to anesthesiologists. Upper airway obstruction due to neoplasm in supraglottic region, is traditionally managed by preoperative tracheostomy, however, such a common procedure can potentially have an impact on long-term outcome. A 26-year-old patient presented with dysphagia caused by left cystic vallecular synovial sarcoma. The airway was successfully secured via fiberoptic bronchoscopy, followed by excision of the supraglottic tumor with CO2 laser surgery. Tracheostomy was not required. The patient was discharged from the hospital on the 4th day of surgery. This case, highlights the possibility to secure the airway passage without performing preoperative tracheostomy resulting in good outcome and short hospital stay. (author)

Applying a punch with microridges in multistage deep drawing processes.

Science.gov (United States)

Lin, Bor-Tsuen; Yang, Cheng-Yu

2016-01-01

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

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/ .

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.

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.

Huge hydrocephalus: definition, management, and complications.

Science.gov (United States)

Faghih Jouibari, Morteza; Baradaran, Nazanin; Shams Amiri, Rouzbeh; Nejat, Farideh; El Khashab, Mostafa

2011-01-01

Lack of comprehensive knowledge and numerous socioeconomic problems may make the parents leave hydrocephalic children untreated, leading to progressive hydrocephalus and eventual unordinary big head. Management of huge hydrocephalus (HH) differs from common hydrocephalus. We present our experience in the management of these children. HH is defined as head circumference larger than the height of the infant. Nine infants with HH have been shunted in Children's Hospital Medical Center and followed up for 0.5 to 7 years. The most common cause of hydrocephalus was aqueductal stenosis. The mean age of patients during shunting was 3 months. The head circumference ranged from 56 to 94 cm with the average of 67 cm. Cognitive statuses were appropriate based on their age in five patients. Motor development was normal only in one patient. Complications were found in most cases which included subdural effusion (six patients), shunt infection (four patients), skin injury (three patients), proximal catheter coming out of ventricle to the subdural space (two patients), and shunt exposure (one patient). Three patients died due to shunt infection and sepsis. Numerous complications may occur in patients with HH after shunt operation such as subdural effusion, ventricular collapse, electrolyte disturbance, skull deformity, scalp injury, and shunt infection. Mental and motor disabilities are very common in patients with HH. Many of these complications can be related to overdrainage; therefore, drainage control using programmable shunts is advisable.

Aquaculture and energy-generation benefit from pipeline deep under the sea

Energy Technology Data Exchange (ETDEWEB)

Anon

2002-09-01

The Natural Energy Laboratory of Hawaii chose about 10,000 feet of HDPE pipe in 55-inch and 63-inch diameters for an application to pump ashore 38 degree F seawater from deep below the ocean surface for use in aquaculture and energy generation. The pipe was supplied by KWH Pipe of Mississauga, Ontario. It is well known that the world's tropical oceans are huge collectors of heat energy which can be utilized for various scientific and practical endeavours, Ocean Thermal Energy Conversion (OTEC) as the process is called, utilizes the difference between warm surface seawater and cold deep seawater to produce energy. The cold seawater can also be used to air condition buildings, desalinate water, grow lobsters and fish, produce algae and shellfish, grow cold-climate fruit and vegetables and much more. In the typical application the pipe is filled with air, which supports it and its anchors during towing to the site where the pipe is flooded for sinking. In the application described here, a separate warm water structure was also installed near the 80-foot deep end of one shore-crossing tunnel; spool pieces connect that structure and the offshore HDPE pipe to the two tunnels constructed earlier. The tunnels extend onshore to the pump station which provides the power to bring the cold water to shore. Other than the Hawaii installation, the only existing example is at Cornell University where the university campus buildings are being cooled by pumping cold water from 250 feet deep in Cayuga Lake through a two-mile long, 63-inch HDPE pipeline.

Huge natural gas reserves central to capacity work, construction plans in Iran

International Nuclear Information System (INIS)

Anon.

1994-01-01

Questions about oil production capacity in Iran tend to mask the country's huge potential as a producer of natural gas. Iran is second only to Russia in gas reserves, which National Iranian Gas Co. estimates at 20.7 trillion cu m. Among hurdles to Iran's making greater use of its rich endowment of natural gas are where and how to sell gas not used inside the country. The marketing logistics problem is common to other Middle East holders of gas reserves and a reason behind the recent proliferation of proposals for pipeline and liquefied natural gas schemes targeting Europe and India. But Iran's challenges are greater than most in the region. Political uncertainties and Islamic rules complicate long-term financing of transportation projects and raise questions about security of supply. As a result, Iran has remained mostly in the background of discussions about international trade of Middle Eastern gas. The country's huge gas reserves, strategic location, and existing transport infrastructure nevertheless give it the potential to be a major gas trader if the other issues can be resolved. The paper discusses oil capacity plans, gas development, gas injection for enhanced oil recovery, proposals for exports of gas, and gas pipeline plans

A New Pixels Flipping Method for Huge Watermarking Capacity of the Invoice Font Image

Directory of Open Access Journals (Sweden)

Li Li

2014-01-01

Full Text Available Invoice printing just has two-color printing, so invoice font image can be seen as binary image. To embed watermarks into invoice image, the pixels need to be flipped. The more huge the watermark is, the more the pixels need to be flipped. We proposed a new pixels flipping method in invoice image for huge watermarking capacity. The pixels flipping method includes one novel interpolation method for binary image, one flippable pixels evaluation mechanism, and one denoising method based on gravity center and chaos degree. The proposed interpolation method ensures that the invoice image keeps features well after scaling. The flippable pixels evaluation mechanism ensures that the pixels keep better connectivity and smoothness and the pattern has highest structural similarity after flipping. The proposed denoising method makes invoice font image smoother and fiter for human vision. Experiments show that the proposed flipping method not only keeps the invoice font structure well but also improves watermarking capacity.

A new pixels flipping method for huge watermarking capacity of the invoice font image.

Science.gov (United States)

Li, Li; Hou, Qingzheng; Lu, Jianfeng; Xu, Qishuai; Dai, Junping; Mao, Xiaoyang; Chang, Chin-Chen

2014-01-01

Invoice printing just has two-color printing, so invoice font image can be seen as binary image. To embed watermarks into invoice image, the pixels need to be flipped. The more huge the watermark is, the more the pixels need to be flipped. We proposed a new pixels flipping method in invoice image for huge watermarking capacity. The pixels flipping method includes one novel interpolation method for binary image, one flippable pixels evaluation mechanism, and one denoising method based on gravity center and chaos degree. The proposed interpolation method ensures that the invoice image keeps features well after scaling. The flippable pixels evaluation mechanism ensures that the pixels keep better connectivity and smoothness and the pattern has highest structural similarity after flipping. The proposed denoising method makes invoice font image smoother and fiter for human vision. Experiments show that the proposed flipping method not only keeps the invoice font structure well but also improves watermarking capacity.

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

Ubiquitous UAVs: a cloud based framework for storing, accessing and processing huge amount of video footage in an efficient way

Science.gov (United States)

Efstathiou, Nectarios; Skitsas, Michael; Psaroudakis, Chrysostomos; Koutras, Nikolaos

2017-09-01

Nowadays, video surveillance cameras are used for the protection and monitoring of a huge number of facilities worldwide. An important element in such surveillance systems is the use of aerial video streams originating from onboard sensors located on Unmanned Aerial Vehicles (UAVs). Video surveillance using UAVs represent a vast amount of video to be transmitted, stored, analyzed and visualized in a real-time way. As a result, the introduction and development of systems able to handle huge amount of data become a necessity. In this paper, a new approach for the collection, transmission and storage of aerial videos and metadata is introduced. The objective of this work is twofold. First, the integration of the appropriate equipment in order to capture and transmit real-time video including metadata (i.e. position coordinates, target) from the UAV to the ground and, second, the utilization of the ADITESS Versatile Media Content Management System (VMCMS-GE) for storing of the video stream and the appropriate metadata. Beyond the storage, VMCMS-GE provides other efficient management capabilities such as searching and processing of videos, along with video transcoding. For the evaluation and demonstration of the proposed framework we execute a use case where the surveillance of critical infrastructure and the detection of suspicious activities is performed. Collected video Transcodingis subject of this evaluation as well.

Gaussian distribution of LMOV numbers

Directory of Open Access Journals (Sweden)

A. Mironov

2017-11-01

Full Text Available Recent advances in knot polynomial calculus allowed us to obtain a huge variety of LMOV integers counting degeneracy of the BPS spectrum of topological theories on the resolved conifold and appearing in the genus expansion of the plethystic logarithm of the Ooguri–Vafa partition functions. Already the very first look at this data reveals that the LMOV numbers are randomly distributed in genus (! and are very well parameterized by just three parameters depending on the representation, an integer and the knot. We present an accurate formulation and evidence in support of this new puzzling observation about the old puzzling quantities. It probably implies that the BPS states, counted by the LMOV numbers can actually be composites made from some still more elementary objects.

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

Science.gov (United States)

Nitta, Tohru

2017-10-01

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

A huge bladder calculus causing acute renal failure.

Science.gov (United States)

Komeya, Mitsuru; Sahoda, Tamami; Sugiura, Shinpei; Sawada, Takuto; Kitami, Kazuo

2013-02-01

A 81-year-old male was referred to our emergency outpatient unit due to acute renal failure. The level of serum creatinine was 276 μmol/l. A CT scan showed bilateral hydronephroureter, large bladder stone (7 cm × 6 cm × 6 cm) and bladder wall thickness. He was diagnosed as post renal failure due to bilateral hydronephroureter. Large bladder stone is thought to be the cause of bilateral hydronephroureter and renal failure. To improve renal failure, we performed open cystolithotomy and urethral catheterization. Three days after the surgery, the level of serum creatinine decreased to 224 μmol/l. He was discharged from our hospital with uneventful course. Bladder calculus is thought to be a rare cause of renal failure. We summarize the characteristics of bladder calculus causing renal failure. We should keep that long-term pyuria and urinary symptom, and repeated urinary tract infection can cause huge bladder calculus and renal failure in mind.

Biodiversity's big wet secret: the global distribution of marine biological records reveals chronic under-exploration of the deep pelagic ocean.

Directory of Open Access Journals (Sweden)

Thomas J Webb

Full Text Available BACKGROUND: Understanding the distribution of marine biodiversity is a crucial first step towards the effective and sustainable management of marine ecosystems. Recent efforts to collate location records from marine surveys enable us to assemble a global picture of recorded marine biodiversity. They also effectively highlight gaps in our knowledge of particular marine regions. In particular, the deep pelagic ocean--the largest biome on Earth--is chronically under-represented in global databases of marine biodiversity. METHODOLOGY/PRINCIPAL FINDINGS: We use data from the Ocean Biogeographic Information System to plot the position in the water column of ca 7 million records of marine species occurrences. Records from relatively shallow waters dominate this global picture of recorded marine biodiversity. In addition, standardising the number of records from regions of the ocean differing in depth reveals that regardless of ocean depth, most records come either from surface waters or the sea bed. Midwater biodiversity is drastically under-represented. CONCLUSIONS/SIGNIFICANCE: The deep pelagic ocean is the largest habitat by volume on Earth, yet it remains biodiversity's big wet secret, as it is hugely under-represented in global databases of marine biological records. Given both its value in the provision of a range of ecosystem services, and its vulnerability to threats including overfishing and climate change, there is a pressing need to increase our knowledge of Earth's largest ecosystem.

Analytic number theory an introductory course

CERN Document Server

Bateman, Paul T

2004-01-01

This valuable book focuses on a collection of powerful methods ofanalysis that yield deep number-theoretical estimates. Particularattention is given to counting functions of prime numbers andmultiplicative arithmetic functions. Both real variable ("elementary")and complex variable ("analytic") methods are employed.

Propranolol in Treatment of Huge and Complicated Infantile Hemangiomas in Egyptian Children

OpenAIRE

Hassan, Basheir A.; Shreef, Khalid S.

2014-01-01

Background. Infantile hemangiomas (IHs) are the most common benign tumours of infancy. Propranolol has recently been reported to be a highly effective treatment for IHs. This study aimed to evaluate the efficacy and side effects of propranolol for treatment of complicated cases of IHs. Patients and Methods. This prospective clinical study included 30 children with huge or complicated IHs; their ages ranged from 2 months to 1 year. They were treated by oral propranolol. Treatment outcomes were...

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/.

How can we identify and communicate the ecological value of deep-sea ecosystem services?

Science.gov (United States)

Jobstvogt, Niels; Townsend, Michael; Witte, Ursula; Hanley, Nick

2014-01-01

Submarine canyons are considered biodiversity hotspots which have been identified for their important roles in connecting the deep sea with shallower waters. To date, a huge gap exists between the high importance that scientists associate with deep-sea ecosystem services and the communication of this knowledge to decision makers and to the wider public, who remain largely ignorant of the importance of these services. The connectivity and complexity of marine ecosystems makes knowledge transfer very challenging, and new communication tools are necessary to increase understanding of ecological values beyond the science community. We show how the Ecosystem Principles Approach, a method that explains the importance of ocean processes via easily understandable ecological principles, might overcome this challenge for deep-sea ecosystem services. Scientists were asked to help develop a list of clear and concise ecosystem principles for the functioning of submarine canyons through a Delphi process to facilitate future transfers of ecological knowledge. These ecosystem principles describe ecosystem processes, link such processes to ecosystem services, and provide spatial and temporal information on the connectivity between deep and shallow waters. They also elucidate unique characteristics of submarine canyons. Our Ecosystem Principles Approach was successful in integrating ecological information into the ecosystem services assessment process. It therefore has a high potential to be the next step towards a wider implementation of ecological values in marine planning. We believe that successful communication of ecological knowledge is the key to a wider public support for ocean conservation, and that this endeavour has to be driven by scientists in their own interest as major deep-sea stakeholders.

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...

On the huge Lie superalgebra of pseudo superdifferential operators and super KP-hierarchies

International Nuclear Information System (INIS)

Sedra, M.B.

1995-08-01

Lie superalgebraic methods are used to establish a connection between the huge Lie superalgebra Ξ of super (pseudo) differential operators and various super KP-hierarchies. We show in particular that Ξ splits into 5 = 2 x 2 + 1 graded algebras expected to correspond to five classes of super KP-hierarchies generalizing the well-known Manin-Radul and Figueroa O'Farrill-Ramos supersymmetric KP-hierarchies. (author). 10 refs

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.

Huge endometrioma mimicking mucinous cystadenoma on MR : A case report

Energy Technology Data Exchange (ETDEWEB)

Hwang, Im Kyung; Kim, Bong Soo; Nam, Kung Sook; Kim, Heung Cheol; Yoo, Yun Sik; Lee, Mee Ran; Hwang, Woo Chul [Hallym University, Chunchon (Korea, Republic of)

2001-12-01

Endometriosis is a relatively common gynecologic disease affecting women during their reproductive years. For its diagnosis, magnetic resonance imaging has been shown to have greater specificity than other modalities. Although lesions may show variable signal intensity due to numerous stages of bleeding, the characteristic finding of endometrioma which distinguishes it from other ovarian cystic masses is relatively high signal intensity on T1-weighted images and heterogeneous signal intensity with prominent shading on 72-weighted images. We report an atypical case involving a huge endometrioma. Because of varying signal intensity on T1- and T2-weighted images and scanty shading on T2-weighted images, the findings were misinterpreted and mucinous cystadenoma was diagnosed.

Spectrometer magnet for experiment NA4 (deep inelastic muon scattering)

CERN Multimedia

CERN PhotoLab

1977-01-01

This is one section of the toroidal-field spectrometer magnet of experiment NA4 (deep inelastic muon scattering), shown here during the installation period and later located in the North Area of the SPS. To see all 4 sections, select 7709201. Igor Savin from Dubna looks at what his lab had provided: the huge iron disks were machined at and provided by Dubna. Multi-Wire Proportional Chambers were installed in the gaps between the packs of 4 disks. When the beam from the SPS struck the target (to the right in this picture), the iron would quickly stop the hadronic shower, whilst the muons would go on, performing oscillations in the toroidal field. NA4 was a CERN-Dubna-Munich-Saclay (later also Bologna) collaboration, spokesman: Carlo Rubbia.

Image inpainting and super-resolution using non-local recursive deep convolutional network with skip connections

Science.gov (United States)

Liu, Miaofeng

2017-07-01

In recent years, deep convolutional neural networks come into use in image inpainting and super-resolution in many fields. Distinct to most of the former methods requiring to know beforehand the local information for corrupted pixels, we propose a 20-depth fully convolutional network to learn an end-to-end mapping a dataset of damaged/ground truth subimage pairs realizing non-local blind inpainting and super-resolution. As there often exist image with huge corruptions or inpainting on a low-resolution image that the existing approaches unable to perform well, we also share parameters in local area of layers to achieve spatial recursion and enlarge the receptive field. To avoid the difficulty of training this deep neural network, skip-connections between symmetric convolutional layers are designed. Experimental results shows that the proposed method outperforms state-of-the-art methods for diverse corrupting and low-resolution conditions, it works excellently when realizing super-resolution and image inpainting simultaneously

A Deep Hydrographic Section Across the Tasman Sea.

Science.gov (United States)

1985-09-01

the same cruise, TC1, as that on which the magneto- telluric moorings (plus a RANRL recording current-meter) were deployed. A small number of deep...that of Wyrtki (1961) who described the different water masses of this area and the northward movement of deep waters from Antarctica. Boland and

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.

Black hole firewalls require huge energy of measurement

Science.gov (United States)

Hotta, Masahiro; Matsumoto, Jiro; Funo, Ken

2014-06-01

The unitary moving mirror model is one of the best quantum systems for checking the reasoning of the original firewall paradox of Almheiri et al. [J. High Energy Phys. 02 (2013) 062] in quantum black holes. Though the late-time part of radiations emitted from the mirror is fully entangled with the early part, no firewall exists with a deadly, huge average energy flux in this model. This is because the high-energy entanglement structure of the discretized systems in almost maximally entangled states is modified so as to yield the correct description of low-energy effective field theory. Furthermore, the strong subadditivity paradox of firewalls is resolved using nonlocality of general one-particle states and zero-point fluctuation entanglement. Due to the Reeh-Schlieder theorem in quantum field theory, another firewall paradox is inevitably raised with quantum remote measurements in the model. We resolve this paradox from the viewpoint of the energy cost of measurements. No firewall appears, as long as the energy for the measurement is much smaller than the ultraviolet cutoff scale.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Directory of Open Access Journals (Sweden)

Fan Zhang

2016-04-01

Full Text Available With the development of synthetic aperture radar (SAR technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO. However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

Science.gov (United States)

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-04-07

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Huge Intracanal lumbar Disc Herniation: a Review of Four Cases

Directory of Open Access Journals (Sweden)

Farzad Omidi-Kashani

2016-01-01

Full Text Available Lumbar disc herniation (LDH is the most common cause of sciatica and only in about 10% of the affected patients, surgical intervention is necessary. The side of the patient (the side of most prominent clinical complaints is usually consistent with the side of imaging (the side with most prominent disc herniation on imaging scans. In this case series, we presented our experience in four cases with huge intracanal LDH that a mismatch between the patient’s side and the imaging’s side was present. In these cases, for deciding to do the operation, the physicians need to rely more on clinical findings, but for deciding the side of discectomy, imaging characteristic (imaging side may be a more important criterion.

A Framework for Transparently Accessing Deep Web Sources

Science.gov (United States)

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…

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.

How can we identify and communicate the ecological value of deep-sea ecosystem services?

Directory of Open Access Journals (Sweden)

Niels Jobstvogt

Full Text Available Submarine canyons are considered biodiversity hotspots which have been identified for their important roles in connecting the deep sea with shallower waters. To date, a huge gap exists between the high importance that scientists associate with deep-sea ecosystem services and the communication of this knowledge to decision makers and to the wider public, who remain largely ignorant of the importance of these services. The connectivity and complexity of marine ecosystems makes knowledge transfer very challenging, and new communication tools are necessary to increase understanding of ecological values beyond the science community. We show how the Ecosystem Principles Approach, a method that explains the importance of ocean processes via easily understandable ecological principles, might overcome this challenge for deep-sea ecosystem services. Scientists were asked to help develop a list of clear and concise ecosystem principles for the functioning of submarine canyons through a Delphi process to facilitate future transfers of ecological knowledge. These ecosystem principles describe ecosystem processes, link such processes to ecosystem services, and provide spatial and temporal information on the connectivity between deep and shallow waters. They also elucidate unique characteristics of submarine canyons. Our Ecosystem Principles Approach was successful in integrating ecological information into the ecosystem services assessment process. It therefore has a high potential to be the next step towards a wider implementation of ecological values in marine planning. We believe that successful communication of ecological knowledge is the key to a wider public support for ocean conservation, and that this endeavour has to be driven by scientists in their own interest as major deep-sea stakeholders.

[Radical Resection of Huge Gastrointestinal Stromal Tumor of the Stomach Following Neoadjuvant Chemotherapy with lmatinib - ACase Report].

Science.gov (United States)

Hiraki, Yoko; Kato, Hiroaki; Shiraishi, Osamu; Tanaka, Yumiko; Iwama, Mitsuru; Yasuda, Atsushi; Shinkai, Masayuki; Kimura, Yutaka; Imano, Motohiro; Imamoto, Haruhiko; Yasuda, Takushi

2017-11-01

The usefulness and safety of imatinibfor neoadjuvant chemotherapy for resectable gastrointestinal stromal tumor(GIST) has not been established. We reported a case of a huge GIST of the stomach that was safely resected following preoperative imatinibtherapy. A 69-year-old man was hospitalized with abdominal fullness which increased rapidly from a month ago. A CT scan showed a huge tumor containing solid and cystic component which was accompanied by an extra-wall nodule. The tumor was strongly suspected to be originated from the stomach and EUS-FNA revealed GIST. We diagnosed GIST of the stomach and initiated preoperative adjuvant chemotherapy with imatinib because there was a risk for the break of tumor capsule and composite resection of the other organs without prior chemotherapy. After the administration of imatinib4 00 mg/day for 6months, the solid component was decreased in size and its' activity by PET-CT had declined, but the size of the cystic component was not changed and the patient's complaint of fullness was not reduced. Then, after a week cessation of imatinib, we performed surgical removal of the tumor with partial gastrectomy without surgical complication during and after the operation. Imatinibwas resumed 2 weeks later postoperatively and 1 year and 8 months has passed since the operation without recurrence. Neoadjuvant chemotherapy with imatinibhas the potential to become an important therapeutic option for the treatment of huge GISTs.

Surgical resection of a huge cemento-ossifying fibroma in skull base by intraoral approach.

Science.gov (United States)

Cheng, Xiao-Bing; Li, Yun-Peng; Lei, De-Lin; Li, Xiao-Dong; Tian, Lei

2011-03-01

Cemento-ossifying fibroma, also known as ossifying fibroma, usually occurs in the mandible and less commonly in the maxilla. The huge example in the skull base is even rare. We present a case of a huge cemento-ossifying fibroma arising below the skull base of a 30-year-old woman patient. Radiologic investigations showed a giant, lobulated, heterogeneous calcified hard tissue mass, which is well circumscribed and is a mixture of radiolucent and radiopaque, situated at the rear of the right maxilla to the middle skull base. The tumor expands into the right maxillary sinus and the orbital cavity, fusing with the right maxilla at the maxillary tuberosity and blocking the bilateral choanas, which caused marked proptosis and blurred vision. The tumor was resected successfully by intraoral approach, and pathologic examination confirmed the lesion to be a cemento-ossifying fibroma. This case demonstrates that cemento-ossifying fibroma in the maxilla, not like in the mandible, may appear more aggressive because the extensive growth is unimpeded by anatomic obstacles and that the intraoral approach can be used to excise the tumor in the skull base.

Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks

Science.gov (United States)

Ienco, Dino; Gaetano, Raffaele; Dupaquier, Claire; Maurel, Pierre

2017-10-01

Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to efficiently analyze such kind of information is still an open question in the remote sensing field. Recently, deep learning methods proved suitable to deal with remote sensing data mainly for scene classification (i.e. Convolutional Neural Networks - CNNs - on single images) while only very few studies exist involving temporal deep learning approaches (i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series. In this letter we evaluate the ability of Recurrent Neural Networks, in particular the Long-Short Term Memory (LSTM) model, to perform land cover classification considering multi-temporal spatial data derived from a time series of satellite images. We carried out experiments on two different datasets considering both pixel-based and object-based classification. The obtained results show that Recurrent Neural Networks are competitive compared to state-of-the-art classifiers, and may outperform classical approaches in presence of low represented and/or highly mixed classes. We also show that using the alternative feature representation generated by LSTM can improve the performances of standard classifiers.

Reconstruction of juxta-articular huge defects of distal femur with vascularized fibular bone graft and Ilizarov's distraction osteogenesis.

Science.gov (United States)

Lai, Davy; Chen, Chuan-Mu; Chiu, Fang-Yao; Chang, Ming-Chau; Chen, Tain-Hsiung

2007-01-01

We evaluate the effect of reconstructing huge defects (mean, 15.8 cm) of the distal femur with Ilizarov's distraction osteogenesis and free twin-barreled vascularized fibular bone graft (TVFG). We retrospectively reviewed a consecutive series of five patients who had cases of distal femoral fractures with huge defects and infection that were treated by the Ilizarov's distraction osteogenesis. After radical debridement, two of the five cases had free TVFG and monolocal distraction osteogenesis, and another two cases had multilocal distraction osteogenesis with knee fusion because of loss of the joint congruity. The other case with floating knee injury had bilocal distraction osteogenesis and a preserved knee joint. The mean defect of distal femur was 15.8 cm (range, 14-18 cm) in length. The mean length of distraction osteogenesis by Ilizarov's apparatus was 8.2 cm. The mean length of TVFG was 8 cm. The average duration from application of Ilizarov's apparatus to achievement of bony union was 10.2 months (range, 8-13 months). At the end of the follow-up, ranges of motion of three knees were 0 to 45 degrees, 0 to 60 degrees, and 0 to 90 degrees. Two cases had knee arthrodesis with bony fusion because of loss of the joint congruity. There were no leg length discrepancies in all five patients. In addition, three patients had pin tract infections and one case had a 10 degree varus deformity of the femur. Juxta-articular huge defect (>10 cm) of distal femur remains a challenge to orthopedic surgeons. Ilizarov's technique provides the capability to maintain stability, eradicate infection, restore leg length, and to perform adjuvant reconstructive procedure easily. In this study, we found that combining Ilizarov's distraction osteogenesis with TVFG results in improved patient outcome for patients with injuries such as supracondylar or intercondylar infected fractures or nonunion of distal femur with huge bone defect.

How to Fill a Narrow 27 km Long Tube with a Huge Number of Accelerator Components?

CERN Document Server

Muttoni, Yvon; Valbuena, Roger

2005-01-01

As in large scale industrial projects, research projects, such as giant and complex particle accelerators, require intensive spatial integration studies using 3D CAD models, from the design to the installation phases. The future management of the LHC machine configuration during its operation will rely on the quality of the information, produced during these studies.This paper presents the powerful data-processing tools used in the project to ensure the spatial integration of several thousand different components in the limited space available.It describes how the documentation and information generated have been made available to a great number of users through a dedicated Web site and how installation nonconformities were handled.

How to fill a narrow 27 KM long tube with a huge number of accelerator components?

CERN Document Server

Muttoni, Y; Valbuena, R

2005-01-01

As in large scale industrial projects, research projects, such as giant and complex particle accelerators, require intensive spatial integration studies using 3D CAD models, from the design to the installation phases. The future management of the LHC machine configuration during its operation will rely on the quality of the information, produced during these studies. This paper presents the powerful data-processing tools used in the project to ensure the spatial integration of several thousand different components in the limited space available. It describes how the documentation and information generated have been made available to a great number of users through a dedicated Web site and how installation nonconformities were handled.

The efficacy of stereotactic body radiation therapy on huge hepatocellular carcinoma unsuitable for other local modalities

International Nuclear Information System (INIS)

Que, Jenny Y; Lin, Li-Ching; Lin, Kuei-Li; Lin, Chia-Hui; Lin, Yu-Wei; Yang, Ching-Chieh

2014-01-01

To evaluate the safety and efficacy of Cyberknife stereotactic body radiation therapy (SBRT) and its effect on survival in patients with unresectable huge hepatocellular carcinoma (HCC) unsuitable of other standard treatment option. Between 2009 and 2011, 22 patients with unresectable huge HCC (≧10 cm) were treated with SBRT. dose ranged from 26 Gy to 40 Gy in five fractions. Overall survival (OS) and disease-progression free survival (DPFS) were determined by Kaplan-Meier analysis. Tumor response and toxicities were also assessed. After a median follow-up of 11.5 month (range 2–46 months). The objective response rate was achieved in 86.3% (complete response (CR): 22.7% and partial response (PR): 63.6%). The 1-yr. local control rate was 55.56%. The 1-year OS was 50% and median survival was 11 months (range 2–46 months). In univariate analysis, Child-Pugh stage (p = 0.0056) and SBRT dose (p = 0.0017) were significant factors for survival. However, in multivariate analysis, SBRT dose (p = 0.0072) was the most significant factor, while Child-Pugh stage of borderline significance. (p = 0.0514). Acute toxicities were mild and well tolerated. This study showed that SBRT can be delivered safely to huge HCC and achieved a substantial tumor regression and survival. The results suggest this technique should be considered a salvage treatment. However, local and regional recurrence remain the major cause of failure. Further studies of combination of SBRT and other treatment modalities may be reasonable

Distributed and parallel approach for handle and perform huge datasets

Science.gov (United States)

Konopko, Joanna

2015-12-01

Big Data refers to the dynamic, large and disparate volumes of data comes from many different sources (tools, machines, sensors, mobile devices) uncorrelated with each others. It requires new, innovative and scalable technology to collect, host and analytically process the vast amount of data. Proper architecture of the system that perform huge data sets is needed. In this paper, the comparison of distributed and parallel system architecture is presented on the example of MapReduce (MR) Hadoop platform and parallel database platform (DBMS). This paper also analyzes the problem of performing and handling valuable information from petabytes of data. The both paradigms: MapReduce and parallel DBMS are described and compared. The hybrid architecture approach is also proposed and could be used to solve the analyzed problem of storing and processing Big Data.

Analysis of the Huge Immigration of Sogatella furcifera (Hemiptera: Delphacidae) to Southern China in the Spring of 2012.

Science.gov (United States)

Sun, Si-Si; Bao, Yun-Xuan; Wu, Yan; Lu, Min-Hong; Tuan, Hoang-Anh

2018-02-08

Sogatella furcifera (Horváth) is a migratory rice pest that periodically erupts across Asia, and early immigration is an important cause of its outbreak. The early immigration of S. furcifera into southern China shows evident annual fluctuations. In the spring of 2012, the huge size of the immigrant population and the large number of immigration peaks were at levels rarely seen prior to that year. However, little research has been done on the entire process of round-trip migration to clarify the development of the population, the long-distance migration and the final eruption. In this study, the light-trap data for S. furcifera in southern China and Vietnam in 2011-2016 were collected, and the trajectory modeling showed that the early immigrants to southern China came from the northern and central Vietnam, Laos, and northeastern Thailand. Analysis of the development of the population, the migration process and meteorological factors revealed the reasons for the huge size of the early immigration: 1) the expansion of the source area could be seen as a precondition; 2) the large size of the returned population in the last autumn and the warm temperature of southern Vietnam and Laos in the last winter increased the initial populations; 3) the sustained strong southwest winds were conducive to the northward migration of the population during the major immigration period in early May. Therefore, the large-scale immigration of S. furcifera to southern China in the spring of 2012 resulted from the combined effects of several factors involved in the process of round-trip migration. © The Author(s) 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Progressive skin necrosis of a huge occipital encephalocele

Science.gov (United States)

Andarabi, Yasir; Nejat, Farideh; El-Khashab, Mostafa

2008-01-01

Objects: Progressive skin necrosis of giant occipital encephalocoele is an extremely rare complication found in neonates. Infection and ulceration of the necrosed skin may lead to meningitis or sepsis. We present here a neonate with giant occipital encephalocoele showing progressive necrosis during the first day of his life. Methods: A newborn baby was found to have a huge mass in the occipital region, which was covered by normal pink-purplish skin. During the last hours of the first day of his life, the sac started becoming ulcerated accompanied with a rapid color change in the skin, gradually turning darker and then black. The neonate was taken up for urgent excision and repair of the encephalocele. Two years after the operation, he appears to be well-developed without any neurological problems. Conclusion: Necrosis may have resulted from arterial or venous compromise caused by torsion of the pedicle during delivery or after birth. The high pressure inside the sac associated with the thin skin of the encephalocoele may be another predisposing factor. In view of the risk of ulceration and subsequent infection, urgent surgery of the necrotizing encephalocele is suggested. PMID:19753210

Progressive skin necrosis of a huge occipital encephalocele

Directory of Open Access Journals (Sweden)

Andarabi Yasir

2008-01-01

Full Text Available Objects: Progressive skin necrosis of giant occipital encephalocoele is an extremely rare complication found in neonates. Infection and ulceration of the necrosed skin may lead to meningitis or sepsis. We present here a neonate with giant occipital encephalocoele showing progressive necrosis during the first day of his life. Methods: A newborn baby was found to have a huge mass in the occipital region, which was covered by normal pink-purplish skin. During the last hours of the first day of his life, the sac started becoming ulcerated accompanied with a rapid color change in the skin, gradually turning darker and then black. The neonate was taken up for urgent excision and repair of the encephalocele. Two years after the operation, he appears to be well-developed without any neurological problems. Conclusion: Necrosis may have resulted from arterial or venous compromise caused by torsion of the pedicle during delivery or after birth. The high pressure inside the sac associated with the thin skin of the encephalocoele may be another predisposing factor. In view of the risk of ulceration and subsequent infection, urgent surgery of the necrotizing encephalocele is suggested.

Hydrogen-terminated mesoporous silicon monoliths with huge surface area as alternative Si-based visible light-active photocatalysts

KAUST Repository

Li, Ting; Li, Jun; Zhang, Qiang; Blazeby, Emma; Shang, Congxiao; Xu, Hualong; Zhang, Xixiang; Chao, Yimin

2016-01-01

Silicon-based nanostructures and their related composites have drawn tremendous research interest in solar energy storage and conversion. Mesoporous silicon with a huge surface area of 400-900 m2 g-1 developed by electrochemical etching exhibits

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Science.gov (United States)

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

2016-01-11

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

The Effect of Deep Cryogenic Treatment on the Corrosion Behavior of Mg-7Y-1.5Nd Magnesium Alloy

Directory of Open Access Journals (Sweden)

Quantong Jiang

2017-10-01

Full Text Available The effect of quenching on the corrosion resistance of Mg-7Y-1.5Nd alloy was investigated. The as-cast alloy was homogenized at 535 °C for 24 h, followed by quenching in air, water, and liquid nitrogen. Then, all of the samples were peak-aged at 225 °C for 14 h. The microstructures were studied by scanning electron microscopy, energy-dispersive spectrometry, and X-ray diffraction. Corrosion behavior was analyzed by using weight loss rate and gas collection. Electrochemical characterizations revealed that the T4-deep cryogenic sample displayed the strongest corrosion resistance among all of the samples. A new square phase was discovered in the microstructure of the T6-deep cryogenic sample; this phase was hugely responsible for the corrosion property. Cryogenic treatment significantly improved the corrosion resistance of Mg-7Y-1.5Nd alloy.

PDF fit in the fixed-flavour-number scheme

International Nuclear Information System (INIS)

Alekhin, S.; Bluemlein, J.; Moch, S.

2012-02-01

We discuss the heavy-quark contribution to deep inelastic scattering in the scheme with n f =3;4;5 fixed flavors. Based on the recent ABM11 PDF analysis of world data for deep-inelastic scattering and fixed-target data for the Drell-Yan process with the running-mass definition for heavy quarks we show that fixed flavor number scheme is sufficient for describing the deep-inelastic-scattering data in the entire kinematic range. We compare with other PDF sets and comment on the implications for measuring the strong coupling constant α s (M Z ).

In vivo stem cell transplantation using reduced cell numbers.

Science.gov (United States)

Tsutsui, Takeo W

2015-01-01

Dental pulp stem cell (DPSC) characterization is essential for regeneration of a dentin/pulp like complex in vivo. This is especially important for identifying the potential of DPSCs to function as stem cells. Previously reported DPSC transplantation methods have used with huge numbers of cells, along with hydroxyapatite/tricalcium phosphate (HA/TCP), gelatin and fibrin, and collagen scaffolds. This protocol describe a transplantation protocol that uses fewer cells and a temperature-responsive cell culture dish.

Into the deep: A coarse-grained carbonate turbidite thalweg generated by gigantic submarine chutes

Science.gov (United States)

Mulder, Thierry; Gillet, Hervé; Reijmer, John; Droxler, André; cavailhes, Thibault; Hanquiez, Vincent; Fauquembergue, Kelly; Bujan, Stéphane; Blanck, David; bashah, Sara; Guiastrennec, Léa; Fabregas, Natacha; Recouvreur, Audrey; Seibert, Chloé

2017-04-01

New high-resolution multibeam mapping, in the Southeastern Bahamas, images in exquisite details the southern part of Exuma Sound, and its unchartered transition area to the deep abyssal plain of the Western North Atlantic bounded by the Bahama Escarpment (BE) between San Salvador Island and Samana Cay, referred here to the San Salvador abyssal plain. The transition area is locally referred to as Crooked Island Passage, loosely delineated by Crooked, Long, and Conception Islands, Rum and Samana Cays. Surprisingly in such a pure carbonate landscape, the newly established map reveals the detailed and complex morphology of a giant valley formed by numerous gravity flows originated in Exuma Sound itself, in addition to many secondary slope gullies and smaller tributaries draining the surrounding upper slopes. The valley referred here as the Exuma canyon system starts with a perched valley with low sinuosity, characterized by several flow restrictions and knickpoints initiated by the presence of drowned isolated platforms and merging tributaries. The valley abruptly transforms itself into a deep incised canyon, rivaling the depth of the Colorado Grand Canyon, through two major knickpoints with outsized chutes exceeding several hundred of meters in height, a total of 1600-1800 m. The sudden transformation of the wide valley into a deep narrow canyon, occurring when the flows incised deep into an underlying lower Cretaceous drowned carbonate platform, generates a huge hydraulic jump and creates an enormous plunge pool and related deposits with mechanisms comparable to the ones operating along giant subaerial waterfalls. The high kinetic flow energy, constrained by this narrow and deeply incised canyon, formed, when it is released at its mouth in the abyssal plain, a wide deep-sea channel with well-developed levees and fan, made of coarse-grained carbonate defined layers separated by fine carbonate sediments mixed with fine siliciclastics transported along the BE by the

Minimization of number of setups for mounting machines

Energy Technology Data Exchange (ETDEWEB)

Kolman, Pavel; Nchor, Dennis; Hampel, David [Department of Statistics and Operation Analysis, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 603 00 Brno (Czech Republic); Žák, Jaroslav [Institute of Technology and Business, Okružní 517/10, 370 01 České Budejovice (Czech Republic)

2015-03-10

The article deals with the problem of minimizing the number of setups for mounting SMT machines. SMT is a device used to assemble components on printed circuit boards (PCB) during the manufacturing of electronics. Each type of PCB has a different set of components, which are obligatory. Components are placed in the SMT tray. The problem consists in the fact that the total number of components used for all products is greater than the size of the tray. Therefore, every change of manufactured product requires a complete change of components in the tray (i.e., a setup change). Currently, the number of setups corresponds to the number of printed circuit board type. Any production change affects the change of setup and stops production on one shift. Many components occur in more products therefore the question arose as to how to deploy the products into groups so as to minimize the number of setups. This would result in a huge increase in efficiency of production.

Leveraging multiple datasets for deep leaf counting

OpenAIRE

Dobrescu, Andrei; Giuffrida, Mario Valerio; Tsaftaris, Sotirios A

2017-01-01

The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation). Instead, our method tre...

The causes and the nursing interventions of the complications due to repeated embolization therapy for huge cerebral arteriovenous malformations

International Nuclear Information System (INIS)

Sun Lingfang; Sun Ge

2010-01-01

Objective: To investigate the causes of the complications occurred after repeated embolization therapy for huge cerebral arteriovenous malformations and to discuss their nursing interventions. Methods: A total of 54 embolization procedures were performed in 17 patients with huge cerebral arteriovenous malformations. The clinical data were retrospectively analyzed. The causes of complications were carefully examined and the preventive measures were discussed. The prompt and necessary nursing interventions were formulated in order to prevent the complications or serious consequences. Results: Among the total 17 patients, one patient gave up the treatment because of the cerebral hemorrhage which occurred two months after receiving 3 times of embolization therapy. One patient experienced cerebral vascular spasm during the procedure, which was relieved after antispasmodic medication and no neurological deficit was left behind. Two patients developed transient dizziness and headache, which were alleviated spontaneously. One patient presented with nervousness, fear and irritability, which made him hard to cooperate with the operation and the basis intravenous anesthesia was employed. No complications occurred in the remaining cases. Conclusion: The predictive nursing interventions for the prevention of complications are very important for obtaining a successful repeated embolization therapy for huge cerebral arteriovenous malformations, which will ensure that the patients can get the best treatment and the complications can be avoided. (authors)

Fluid Mechanics of Aquatic Locomotion at Large Reynolds Numbers

OpenAIRE

Govardhan, RN; Arakeri, JH

2011-01-01

Abstract | There exist a huge range of fish species besides other aquatic organisms like squids and salps that locomote in water at large Reynolds numbers, a regime of flow where inertial forces dominate viscous forces. In the present review, we discuss the fluid mechanics governing the locomotion of such organisms. Most fishes propel themselves by periodic undulatory motions of the body and tail, and the typical classification of their swimming modes is based on the fraction of their body...

PDF fit in the fixed-flavour-number scheme

Energy Technology Data Exchange (ETDEWEB)

Alekhin, S. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany); Institute for High Energy Physics, Moscow (Russian Federation); Bluemlein, J.; Moch, S. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)

2012-02-15

We discuss the heavy-quark contribution to deep inelastic scattering in the scheme with n{sub f}=3;4;5 fixed flavors. Based on the recent ABM11 PDF analysis of world data for deep-inelastic scattering and fixed-target data for the Drell-Yan process with the running-mass definition for heavy quarks we show that fixed flavor number scheme is sufficient for describing the deep-inelastic-scattering data in the entire kinematic range. We compare with other PDF sets and comment on the implications for measuring the strong coupling constant {alpha}{sub s}(M{sub Z}).

Transcatheter Closure of Bilateral Multiple Huge Pulmonary Arteriovenous Malformations with Homemade Double-Umbrella Occluders

International Nuclear Information System (INIS)

Zhong Hongshan; Xu Ke; Shao Haibo

2008-01-01

A 28-year-old man underwent successful transcatheter occlusion of three huge pulmonary arteriovenous malformations (PAVMs) using homemade double-umbrella occluders and stainless steel coils. Thoracic CT with three-dimensional reconstruction and pulmonary angiography were used for treatment planning and follow-up. The diameters of the feeding vessels were 11 mm, 13 mm, and 14 mm, respectively. This report demonstrates the novel design and utility of the double-umbrella occluder, an alternative tool for treatment of large PAVMs.

Deep Learning Approach for Car Detection in UAV Imagery

Directory of Open Access Journals (Sweden)

Nassim Ammour

2017-03-01

Full Text Available This paper presents an automatic solution to the problem of detecting and counting cars in unmanned aerial vehicle (UAV images. This is a challenging task given the very high spatial resolution of UAV images (on the order of a few centimetres and the extremely high level of detail, which require suitable automatic analysis methods. Our proposed method begins by segmenting the input image into small homogeneous regions, which can be used as candidate locations for car detection. Next, a window is extracted around each region, and deep learning is used to mine highly descriptive features from these windows. We use a deep convolutional neural network (CNN system that is already pre-trained on huge auxiliary data as a feature extraction tool, combined with a linear support vector machine (SVM classifier to classify regions into “car” and “no-car” classes. The final step is devoted to a fine-tuning procedure which performs morphological dilation to smooth the detected regions and fill any holes. In addition, small isolated regions are analysed further using a few sliding rectangular windows to locate cars more accurately and remove false positives. To evaluate our method, experiments were conducted on a challenging set of real UAV images acquired over an urban area. The experimental results have proven that the proposed method outperforms the state-of-the-art methods, both in terms of accuracy and computational time.

Deep Echo State Network (DeepESN): A Brief Survey

OpenAIRE

Gallicchio, Claudio; Micheli, Alessio

2017-01-01

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

PRS: PERSONNEL RECOMMENDATION SYSTEM FOR HUGE DATA ANALYSIS USING PORTER STEMMER

Directory of Open Access Journals (Sweden)

T N Chiranjeevi

2016-04-01

Full Text Available Personal recommendation system is one which gives better and preferential recommendation to the users to satisfy their personalized requirements such as practical applications like Webpage Preferences, Sport Videos preferences, Stock selection based on price, TV preferences, Hotel preferences, books, Mobile phones, CDs and various other products now use recommender systems. The existing Pearson Correlation Coefficient (PCC and item-based algorithm using PCC, are called as UPCC and IPCC respectively. These systems are mainly based on only the rating services and does not consider the user personal preferences, they simply just give the result based on the ratings. As the size of data increases it will give the recommendations based on the top rated services and it will miss out most of user preferences. These are main drawbacks in the existing system which will give same results to the users based on some evaluations and rankings or rating service, they will neglect the user preferences and necessities. To address this problem we propose a new approach called, Personnel Recommendation System (PRS for huge data analysis using Porter Stemmer to solve the above challenges. In the proposed system it provides a personalized service recommendation list to the users and recommends the most useful services to the users which will increase the accuracy and efficiency in searching better services. Particularly, a set of suggestions or keywords are provided to indicate user preferences and we used Collaborative Filtering and Porter Stemmer algorithm which gives a suitable recommendations to the users. In real, the broad experiments are conducted on the huge database which is available in real world, and outcome shows that our proposed personal recommender method extensively improves the precision and efficiency of service recommender system over the KASR method. In our approach mainly consider the user preferences so it will not miss out the any of the data

Process control upgrades yield huge operational improvements

International Nuclear Information System (INIS)

Fitzgerald, W.V.

2001-01-01

Most nuclear plants in North America were designed and built in the late 60 and 70. The regulatory nature of this industry over the years has made design changes at the plant level difficult, if not impossible, to implement. As a result, many plants in this world region have been getting by on technology that is over 40 years behind the times. What this translates into is that the plants have not been able to take advantage of the huge technology gains that have been made in process control during this period. As a result, most of these plants are much less efficient and productive than they could be. One particular area of the plant that is receiving a lot of attention is the feedwater heaters. These systems were put in place to improve efficiency, but most are not operating correctly. This paper will present a case study where one progressive mid-western utility decided that enough was enough and implemented a process control audit of their heater systems. The audit clearly pointed out the existing problems with the current process control system. It resulted in a proposal for the implementation of a state of the art, digital distributed process control system for the heaters along with a complete upgrade of the level controls and field devices that will stabilize heater levels, resulting in significant efficiency gains and lower maintenance bills. Overall the payback period for this investment should be less than 6 months and the plant is now looking for more opportunities that can provide even bigger gains. (author)

Deep Question Answering for protein annotation.

Science.gov (United States)

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/. © The Author(s) 2015. Published by Oxford University Press.

Huge pelvic parachordoma: fine needle aspiration cytology and histological differential diagnosis

Directory of Open Access Journals (Sweden)

Mona A. Kandil

2012-10-01

Full Text Available Parachordoma is an extremely rare soft tissue tumor of unknown lineage. Parachordoma develops most often on the extremities. Only 2 cases have been reported as pelvic parachordoma. A 46-year old Egyptian woman with a huge painful pelvic mass was found to have a parachordoma with ectopic pelvic right kidney. There is only one report in the literature of fine needle aspiration cytology in this setting. The microscopic picture of parachordoma is not new to pathologists but the gross picture of this rare tumor has not previously been published; not even in the World Health Organization classification of soft tissues tumors. Diagnosis was confirmed by immuno-histochemistry. The patient is in good clinical condition without any evidence of recurrence or metastasis after 84 months of follow up.

Topographical effects on wave exciting forces on huge floating structure. 2; Ogata futaishiki kaiyo kozobutsu ni sayosuru haryoku ni kansuru kenkyu. 2

Energy Technology Data Exchange (ETDEWEB)

Imai, Y [Hiroshima University, Hiroshima (Japan); Okusu, M [Kyushu Univ., Fukuoka (Japan). Research Inst. for Applied Mechanics

1997-12-31

A method to predict drift force acting on a floating structure has been developed for a marine structure consisting of a number of floating elements, positioned in a region having a slope at the sea bottom. When a huge marine structure, such as floating air port, is located in a coastal area, scale of the overall structure is very large, of the order of scale of water depth change. The new method assumes that a marine structure consisting of an infinite number of cylindrical floating elements is installed in parallel to the seashore, where symmetrical nature of the configuration allows to predict behavior of the whole system by analyzing one element. Integration of pressures acting on structure surfaces determines the horizontal component of the drift force acting on the structure. Being influenced by topography, drift force predicted peaks at a frequency different from that for the level predicted on the assumption of constant water depth. This indicates the necessity for consideration of seabottom slope and effects of broken waves at the seashore. 6 refs., 12 figs.

Topographical effects on wave exciting forces on huge floating structure. 2; Ogata futaishiki kaiyo kozobutsu ni sayosuru haryoku ni kansuru kenkyu. 2

Energy Technology Data Exchange (ETDEWEB)

Imai, Y. [Hiroshima University, Hiroshima (Japan); Okusu, M. [Kyushu Univ., Fukuoka (Japan). Research Inst. for Applied Mechanics

1996-12-31

A method to predict drift force acting on a floating structure has been developed for a marine structure consisting of a number of floating elements, positioned in a region having a slope at the sea bottom. When a huge marine structure, such as floating air port, is located in a coastal area, scale of the overall structure is very large, of the order of scale of water depth change. The new method assumes that a marine structure consisting of an infinite number of cylindrical floating elements is installed in parallel to the seashore, where symmetrical nature of the configuration allows to predict behavior of the whole system by analyzing one element. Integration of pressures acting on structure surfaces determines the horizontal component of the drift force acting on the structure. Being influenced by topography, drift force predicted peaks at a frequency different from that for the level predicted on the assumption of constant water depth. This indicates the necessity for consideration of seabottom slope and effects of broken waves at the seashore. 6 refs., 12 figs.

Extracting Databases from Dark Data with DeepDive.

Science.gov (United States)

Zhang, Ce; Shin, Jaeho; Ré, Christopher; Cafarella, Michael; Niu, Feng

2016-01-01

DeepDive is a system for extracting relational databases from dark data : the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data - scientific papers, Web classified ads, customer service notes, and so on - were instead in a relational database, it would give analysts a massive and valuable new set of "big data." DeepDive is distinctive when compared to previous information extraction systems in its ability to obtain very high precision and recall at reasonable engineering cost; in a number of applications, we have used DeepDive to create databases with accuracy that meets that of human annotators. To date we have successfully deployed DeepDive to create data-centric applications for insurance, materials science, genomics, paleontologists, law enforcement, and others. The data unlocked by DeepDive represents a massive opportunity for industry, government, and scientific researchers. DeepDive is enabled by an unusual design that combines large-scale probabilistic inference with a novel developer interaction cycle. This design is enabled by several core innovations around probabilistic training and inference.

GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

Directory of Open Access Journals (Sweden)

Syed Tahir Hussain Rizvi

2016-12-01

Full Text Available Deep convolutional neural networks achieve state-of-the-art performance in image classification. The computational and memory requirements of such networks are however huge, and that is an issue on embedded devices due to their constraints. Most of this complexity derives from the convolutional layers and in particular from the matrix multiplications they entail. This paper proposes a complete approach to image classification providing common layers used in neural networks. Namely, the proposed approach relies on a heterogeneous CPU-GPU scheme for performing convolutions in the transform domain. The Compute Unified Device Architecture(CUDA-based implementation of the proposed approach is evaluated over three different image classification networks on a Tegra K1 CPU-GPU mobile processor. Experiments show that the presented heterogeneous scheme boasts a 50× speedup over the CPU-only reference and outperforms a GPU-based reference by 2×, while slashing the power consumption by nearly 30%.

Huge residual resistivity in the quantum critical region of CeAgSb2

International Nuclear Information System (INIS)

Nakashima, Miho; Kirita, Shingo; Asai, Rihito; Kobayashi, Tatsuo C; Okubo, Tomoyuki; Yamada, Mineko; Thamizhavel, Arumugam; Inada, Yoshihiko; Settai, Rikio; Galatanu, Andre; Yamamoto, Etsuji; Ebihara, Takao; Onuki, Yoshichika

2003-01-01

We have studied the effect of pressure on the electrical resistivity of a high-quality single crystal CeAgSb 2 which has a small net ferromagnetic moment of 0.4μ B /Ce. The magnetic ordering temperature T ord = 9.7 K decreases with increasing pressure p and disappears at a critical pressure p c ≅ 3.3 GPa. The residual resistivity, which is close to zero up to 3 GPa, increases steeply above 3 GPa, reaching 55μΩ cm at p c . A huge residual resistivity is found to appear when the magnetic order disappears. (letter to the editor)

The effect of the cranial bone CT numbers on the brain CT numbers

Energy Technology Data Exchange (ETDEWEB)

Fukuda, Hitoshi; Kobayashi, Shotai; Koide, Hiromi; Yamaguchi, Shuhei; Okada, Kazunori; Shimote, Koichi; Tsunematsu, Tokugoro (Shimane Medical Univ., Izumo (Japan))

1989-06-01

The effects of the cranial size and the computed tomography (CT) numbers of the cranial bone on that of the brain were studied in 70 subjects, aged from 30 to 94 years. The subjects had no histories of cerebrovascular accidents and showed no abnormalities in the central nervous system upon physical examinations and a CT scan. We measured the average attenuation values (CT numbers) of each elliptical region (165 pixels, 0.39 cm{sup 2}) at the bilateral thalamus and at twelve areas of the deep white matter. Multiple regression analysis was used to assess the effects of age, cranial size, and cranial bone CT numbers on the brain CT numbers. The effect of the cranial bone CT numbers on the brain CT numbers was statistically significant. The brain CT numbers increased with the increase in the cranial bone CT numbers. There was, however, no significant correlation between brain CT numbers and cranial size. In measuring the brain CT numbers, it is desirable that consideration be given to the cranial bone CT numbers. (author).

Overview of deep learning in medical imaging.

Science.gov (United States)

Suzuki, Kenji

2017-09-01

lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

Structure functions in electron-nucleon deep inelastic scattering

Energy Technology Data Exchange (ETDEWEB)

Saleem, M.; Fazal-E-Aleem (University of the Punjab, Lahore (Pakistan). Dept. of Physics)

1982-06-26

The phenomenological expressions for the structure functions in electron-nucleon deep inelastic scattering are proposed and are shown to satisfy the experimental data as well as a number of sum rules.

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...

Carbon and nitrogen assimilation in deep subseafloor microbial cells

OpenAIRE

Morono, Yuki; Terada, Takeshi; Nishizawa, Manabu; Ito, Motoo; Hillion, François; Takahata, Naoto; Sano, Yuji; Inagaki, Fumio

2011-01-01

Remarkable numbers of microbial cells have been observed in global shallow to deep subseafloor sediments. Accumulating evidence indicates that deep and ancient sediments harbor living microbial life, where the flux of nutrients and energy are extremely low. However, their physiology and energy requirements remain largely unknown. We used stable isotope tracer incubation and nanometer-scale secondary ion MS to investigate the dynamics of carbon and nitrogen assimilation activities in individua...

Cold-seep-like macrofaunal communities in organic- and sulfide-rich sediments of the Congo deep-sea fan

Science.gov (United States)

Olu, K.; Decker, C.; Pastor, L.; Caprais, J.-C.; Khripounoff, A.; Morineaux, M.; Ain Baziz, M.; Menot, L.; Rabouille, C.

2017-08-01

Methane-rich fluids arising from organic matter diagenesis in deep sediment layers sustain chemosynthesis-based ecosystems along continental margins. This type of cold seep develops on pockmarks along the Congo margin, where fluids migrate from deep-buried paleo-channels of the Congo River, acting as reservoirs. Similar ecosystems based on shallow methane production occur in the terminal lobes of the present-day Congo deep-sea fan, which is supplied by huge quantities of primarily terrestrial material carried by turbiditic currents along the 800 km channel, and deposited at depths of up to nearly 5000 m. In this paper, we explore the effect of this carbon enrichment of deep-sea sediments on benthic macrofauna, along the prograding lobes fed by the current active channel, and on older lobes receiving less turbiditic inputs. Macrofaunal communities were sampled using either USNEL cores on the channel levees, or ROV blade cores in the chemosynthesis-based habitats patchily distributed in the active lobe complex. The exceptionally high organic content of the surface sediment in the active lobe complex was correlated with unusual densities of macrofauna for this depth, enhanced by a factor 7-8, compared with those of the older, abandoned lobe, whose sediment carbon content is still higher than in Angola Basin at same depth. Macrofaunal communities, dominated by cossurid polychaetes and tanaids were also more closely related to those colonizing low-flow cold seeps than those of typical deep-sea sediment. In reduced sediments, microbial mats and vesicomyid bivalve beds displayed macrofaunal community patterns that were similar to their cold-seep counterparts, with high densities, low diversity and dominance of sulfide-tolerant polychaetes and gastropods in the most sulfidic habitats. In addition, diversity was higher in vesicomyid bivalve beds, which appeared to bio-irrigate the upper sediment layers. High beta-diversity is underscored by the variability of geochemical

Evolving Deep Networks Using HPC

Energy Technology Data Exchange (ETDEWEB)

Young, Steven R. [ORNL, Oak Ridge; Rose, Derek C. [ORNL, Oak Ridge; Johnston, Travis [ORNL, Oak Ridge; Heller, William T. [ORNL, Oak Ridge; Karnowski, thomas P. [ORNL, Oak Ridge; Potok, Thomas E. [ORNL, Oak Ridge; Patton, Robert M. [ORNL, Oak Ridge; Perdue, Gabriel [Fermilab; Miller, Jonathan [Santa Maria U., Valparaiso

2017-01-01

While a large number of deep learning networks have been studied and published that produce outstanding results on natural image datasets, these datasets only make up a fraction of those to which deep learning can be applied. These datasets include text data, audio data, and arrays of sensors that have very different characteristics than natural images. As these “best” networks for natural images have been largely discovered through experimentation and cannot be proven optimal on some theoretical basis, there is no reason to believe that they are the optimal network for these drastically different datasets. Hyperparameter search is thus often a very important process when applying deep learning to a new problem. In this work we present an evolutionary approach to searching the possible space of network hyperparameters and construction that can scale to 18, 000 nodes. This approach is applied to datasets of varying types and characteristics where we demonstrate the ability to rapidly find best hyperparameters in order to enable practitioners to quickly iterate between idea and result.

Acute abdomen in early pregnancy caused by torsion of bilateral huge multiloculated ovarian cysts

OpenAIRE

Sathiyakala Rajendran; Suthanthira Devi

2015-01-01

The association of pregnancy and torsion of bilateral huge benign ovarian cyst is rare. We report a case of multigravida at 13 weeks of pregnancy presenting with acute onset of lower abdominal pain. Ultrasound revealed bilateral multiloculated ovarian cysts of size 10x10 cm on right side and 15x10cm on left side with evidence of torsion and a single live intrauterine fetus of gestational age 13 weeks 4 days. Emergency laparotomy was done with vaginal susten 200 mg as perioperative tocolysis. ...

Microbiology and Biodegradation: Deep Ultraviolet Microscopy for the Detection, Quantification, and Characterization of Microbes

Science.gov (United States)

2015-11-16

Approved for Public Release; Distribution Unlimited Final Report: 14.3 Microbiology and Biodegradation: Deep Ultraviolet Microscopy for the Detection...Fluroesence; Raman Spectroscopy; Microbiology REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR/MONITOR’S ACRONYM(S) ARO...14.3 Microbiology and Biodegradation: Deep Ultraviolet Microscopy for the Detection, Quantification, and Characterization of Microbes Report Title

Thermally Activated Delayed Fluorescence Emitters for Deep Blue Organic Light Emitting Diodes: A Review of Recent Advances

Directory of Open Access Journals (Sweden)

Thanh-Tuân Bui

2018-03-01

Full Text Available Organic light-emitting diodes offer attractive perspectives for the next generation display and lighting technologies. The potential is huge and the list of potential applications is almost endless. So far, blue emitters still suffer from noticeably inferior electroluminescence performances in terms of efficiency, lifespan, color quality, and charge injection/transport when compared to that of the other colors. Emitting materials matching the NTSC standard blue of coordinates (0.14, 0.08 are extremely rare and still constitutes the focus of numerous academic and industrial researches. In this context, we review herein the recent developments on highly emissive deep-blue thermally activated delayed fluorescence emitters that constitute the third-generation electroluminescent materials.

Neutrino masses, lepton number violation and unification

CERN Document Server

Barbieri, Riccardo

1980-01-01

Theories with parity as a short-distance symmetry lead rather naturally to a small but non-vanishing nu L/sub 2/ mass. A reference formula for the size of the effect is m/sub nu / approximately=m/sup 2 //M with M a huge Majorana mass of the nu /sub R/ field, associated with the breaking of the group down to SU(3)*SU(2)*U(1) and m a typical quark mass, most likely that of charge 2/3. This is because of the Pati-Salam SU(4) which relates neutrinos with charge 2/3 quarks, and is contained in the prototypes of these theories, SO(10) or E/sub 6/. Ten GeV for m requires M approximately=10/sup 11/ GeV in order to saturate the cosmological bound (m/sub nu / of a few eV). This value is not too far from the currently preferred mass approximately=10/sup 14/ GeV of the superheavy gauge bosons. In view of these concepts, the search for neutrino oscillations appears to be of overwhelming importance. A combined effort in all different kinds of possible experiments (reactors, accelerators, deep mines, and solar neutrino obse...

Subcortical heterotopia appearing as huge midline mass in the newborn brain.

Science.gov (United States)

Fukumura, Shinobu; Watanabe, Toshihide; Kimura, Sachiko; Ochi, Satoko; Yoshifuji, Kazuhisa; Tsutsumi, Hiroyuki

2016-02-01

We report the case of a 2-year-old boy who showed a huge midline mass in the brain at prenatal assessment. After birth, magnetic resonance imaging (MRI) revealed a conglomerate mass with an infolded microgyrus at the midline, which was suspected as a midline brain-in-brain malformation. MRI also showed incomplete cleavage of his frontal cortex and thalamus, consistent with lobar holoprosencephaly. The patient underwent an incisional biopsy of the mass on the second day of life. The mass consisted of normal central nervous tissue with gray and white matter, representing a heterotopic brain. The malformation was considered to be a subcortical heterotopia. With maturity, focal signal changes and decreased cerebral perfusion became clear on brain imaging, suggesting secondary glial degeneration. Coincident with these MRI abnormalities, the child developed psychomotor retardation and severe epilepsy focused on the side of the intracranial mass.

Integrated analysis of gene expression, CpG island methylation, and gene copy number in breast cancer cells by deep sequencing.

Directory of Open Access Journals (Sweden)

Zhifu Sun

Full Text Available We used deep sequencing technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+ and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A, and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER- cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER- cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5 kb of the 5' end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER- breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations.

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

Science.gov (United States)

Takai, K; Horikoshi, K

1999-08-01

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

From academia to industry: The story of Google DeepMind

OpenAIRE

Legg, Shane

2014-01-01

Shane Legg left academia to cofound DeepMind Technologies in 2010, along with Demis Hassabis and Mustafa Suleyman. Their vision was to bring together cutting edge machine learning and systems neuroscience in order to create artificial agents with general intelligence. Following investments from a number of famous technology entrepreneurs, including Peter Thiel and Elon Musk, they assembled a team of world class researchers with backgrounds in systems neuroscience, deep learning, reinforcement...

Worldwide Analysis of Sedimentary DNA Reveals Major Gaps in Taxonomic Knowledge of Deep-Sea Benthos

DEFF Research Database (Denmark)

Sinniger, Frédéric; Pawlowski, Jan; Harii, Saki

2016-01-01

in 39 deep-sea sediment samples from bathyal and abyssal depths worldwide. The eDNA dataset was dominated by meiobenthic taxa and we identified all animal phyla commonly found in the deep-sea benthos; yet, the diversity within these phyla remains largely unknown. The large numbers of taxonomically...... for pure and applied deep-sea environmental research but also emphasizes the necessity to integrate such new approaches with traditional morphology-based examination of deep-sea organisms....

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...

Real-time yield estimation based on deep learning

Science.gov (United States)

Rahnemoonfar, Maryam; Sheppard, Clay

2017-05-01

Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.

Deep Borehole Field Test Research Activities at LBNL

Energy Technology Data Exchange (ETDEWEB)

Dobson, Patrick [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Tsang, Chin-Fu [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Kneafsey, Timothy [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Borglin, Sharon [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Piceno, Yvette [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Andersen, Gary [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Nakagawa, Seiji [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Nihei, Kurt [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Rutqvist, Jonny [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Doughty, Christine [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Reagan, Matthew [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2016-08-19

The goal of the U.S. Department of Energy Used Fuel Disposition’s (UFD) Deep Borehole Field Test is to drill two 5 km large-diameter boreholes: a characterization borehole with a bottom-hole diameter of 8.5 inches and a field test borehole with a bottom-hole diameter of 17 inches. These boreholes will be used to demonstrate the ability to drill such holes in crystalline rocks, effectively characterize the bedrock repository system using geophysical, geochemical, and hydrological techniques, and emplace and retrieve test waste packages. These studies will be used to test the deep borehole disposal concept, which requires a hydrologically isolated environment characterized by low permeability, stable fluid density, reducing fluid chemistry conditions, and an effective borehole seal. During FY16, Lawrence Berkeley National Laboratory scientists conducted a number of research studies to support the UFD Deep Borehole Field Test effort. This work included providing supporting data for the Los Alamos National Laboratory geologic framework model for the proposed deep borehole site, conducting an analog study using an extensive suite of geoscience data and samples from a deep (2.5 km) research borehole in Sweden, conducting laboratory experiments and coupled process modeling related to borehole seals, and developing a suite of potential techniques that could be applied to the characterization and monitoring of the deep borehole environment. The results of these studies are presented in this report.

Deep Borehole Field Test Research Activities at LBNL

International Nuclear Information System (INIS)

Dobson, Patrick; Tsang, Chin-Fu; Kneafsey, Timothy; Borglin, Sharon; Piceno, Yvette; Andersen, Gary; Nakagawa, Seiji; Nihei, Kurt; Rutqvist, Jonny; Doughty, Christine; Reagan, Matthew

2016-01-01

The goal of the U.S. Department of Energy Used Fuel Disposition's (UFD) Deep Borehole Field Test is to drill two 5 km large-diameter boreholes: a characterization borehole with a bottom-hole diameter of 8.5 inches and a field test borehole with a bottom-hole diameter of 17 inches. These boreholes will be used to demonstrate the ability to drill such holes in crystalline rocks, effectively characterize the bedrock repository system using geophysical, geochemical, and hydrological techniques, and emplace and retrieve test waste packages. These studies will be used to test the deep borehole disposal concept, which requires a hydrologically isolated environment characterized by low permeability, stable fluid density, reducing fluid chemistry conditions, and an effective borehole seal. During FY16, Lawrence Berkeley National Laboratory scientists conducted a number of research studies to support the UFD Deep Borehole Field Test effort. This work included providing supporting data for the Los Alamos National Laboratory geologic framework model for the proposed deep borehole site, conducting an analog study using an extensive suite of geoscience data and samples from a deep (2.5 km) research borehole in Sweden, conducting laboratory experiments and coupled process modeling related to borehole seals, and developing a suite of potential techniques that could be applied to the characterization and monitoring of the deep borehole environment. The results of these studies are presented in this report.

En bloc resection of huge cemento-ossifying fibroma of mandible: avoiding lower lip split incision.

Science.gov (United States)

Ayub, Tahera; Katpar, Shahjahan; Shafique, Salman; Mirza, Talat

2011-05-01

Cemento-ossifying Fibroma (COF) is an osteogenic benign neoplasm affecting the jaws and other craniofacial bones. It commonly presents as a progressively slow growing pathology, which can sometimes attain an enormous size, causing facial deformity. A case of a huge cemento-ossifying fibroma, appearing as a mandibular dumbell tumour in a male patient is documented, which caused massive bone destruction and deformity. It was surgically removed by performing en bloc resection of mandible avoiding the splitting of lower lip incision technique, thereby maintaining his normal facial appearance.

A multiscale fixed stress split iterative scheme for coupled flow and poromechanics in deep subsurface reservoirs

Science.gov (United States)

Dana, Saumik; Ganis, Benjamin; Wheeler, Mary F.

2018-01-01

In coupled flow and poromechanics phenomena representing hydrocarbon production or CO2 sequestration in deep subsurface reservoirs, the spatial domain in which fluid flow occurs is usually much smaller than the spatial domain over which significant deformation occurs. The typical approach is to either impose an overburden pressure directly on the reservoir thus treating it as a coupled problem domain or to model flow on a huge domain with zero permeability cells to mimic the no flow boundary condition on the interface of the reservoir and the surrounding rock. The former approach precludes a study of land subsidence or uplift and further does not mimic the true effect of the overburden on stress sensitive reservoirs whereas the latter approach has huge computational costs. In order to address these challenges, we augment the fixed-stress split iterative scheme with upscaling and downscaling operators to enable modeling flow and mechanics on overlapping nonmatching hexahedral grids. Flow is solved on a finer mesh using a multipoint flux mixed finite element method and mechanics is solved on a coarse mesh using a conforming Galerkin method. The multiscale operators are constructed using a procedure that involves singular value decompositions, a surface intersections algorithm and Delaunay triangulations. We numerically demonstrate the convergence of the augmented scheme using the classical Mandel's problem solution.

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)

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.

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 Support Vector Machines for Regression Problems

NARCIS (Netherlands)

Wiering, Marco; Schutten, Marten; Millea, Adrian; Meijster, Arnold; Schomaker, Lambertus

2013-01-01

In this paper we describe a novel extension of the support vector machine, called the deep support vector machine (DSVM). The original SVM has a single layer with kernel functions and is therefore a shallow model. The DSVM can use an arbitrary number of layers, in which lower-level layers contain

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.

[A Case of Huge Colon Cancer Accompanied with Severe Hypoproteinemia].

Science.gov (United States)

Hiraki, Sakurao; Kanesada, Kou; Harada, Toshio; Tada, Kousuke; Fukuda, Shintaro

2017-11-01

We report a case of huge colon cancer accompanied with severe hypoproteinemia. A7 4-year-old woman was referred to our hospital because of abdominal fullness. Blood examinations revealed anemia(hemoglobin 8.8 g/dL)and sever hypopro- teinemia(total protein 4.5 g/dL, albumin 1.1 g/dL). Computed tomography examination of abdomen revealed ascites and large tumor(12.5×10.5 cm)at the right side colon. By further examinations ascending colon cancer without distant metastasis was diagnosed, then we performed right hemicolectomy and primary intestinal anastomosis by open surgery. Ahuge type 1 tumor(18×12 cm)was observed in the excised specimen, which invaded to terminal ileum directly. The tumor was diagnosed moderately differentiated adenocarcinoma without lymph node metastasis(pT3N0M0, fStage II ). Postoperative course was uneventful and serum protein concentration recovered gradually to normal range. Protein leakage from the tumor cannot be proved by this case, so we can't diagnose as protein-losing enteropathy, but we strongly doubt this etiology from postoperative course in this case.

Deep-sea mud in the Pacific Ocean as a potential resource for rare-earth elements

Science.gov (United States)

Kato, Yasuhiro; Fujinaga, Koichiro; Nakamura, Kentaro; Takaya, Yutaro; Kitamura, Kenichi; Ohta, Junichiro; Toda, Ryuichi; Nakashima, Takuya; Iwamori, Hikaru

2011-08-01

World demand for rare-earth elements and the metal yttrium--which are crucial for novel electronic equipment and green-energy technologies--is increasing rapidly. Several types of seafloor sediment harbour high concentrations of these elements. However, seafloor sediments have not been regarded as a rare-earth element and yttrium resource, because data on the spatial distribution of these deposits are insufficient. Here, we report measurements of the elemental composition of over 2,000 seafloor sediments, sampled at depth intervals of around one metre, at 78 sites that cover a large part of the Pacific Ocean. We show that deep-sea mud contains high concentrations of rare-earth elements and yttrium at numerous sites throughout the eastern South and central North Pacific. We estimate that an area of just one square kilometre, surrounding one of the sampling sites, could provide one-fifth of the current annual world consumption of these elements. Uptake of rare-earth elements and yttrium by mineral phases such as hydrothermal iron-oxyhydroxides and phillipsite seems to be responsible for their high concentration. We show that rare-earth elements and yttrium are readily recovered from the mud by simple acid leaching, and suggest that deep-sea mud constitutes a highly promising huge resource for these elements.

Rare earth element geochemistry characteristics of seawater and porewater from deep sea in western Pacific.

Science.gov (United States)

Deng, Yinan; Ren, Jiangbo; Guo, Qingjun; Cao, Jun; Wang, Haifeng; Liu, Chenhui

2017-11-28

Deep-sea sediments contain high concentrations of rare earth element (REE) which have been regarded as a huge potential resource. Understanding the marine REE cycle is important to reveal the mechanism of REE enrichment. In order to determine the geochemistry characteristics and migration processes of REE, seawater, porewater and sediment samples were systematically collected from the western Pacific for REE analysis. The results show a relatively flat REE pattern and the HREE (Heavy REE) enrichment in surface and deep seawater respectively. The HREE enrichment distribution patterns, low concentrations of Mn and Fe and negative Ce anomaly occur in the porewater, and high Mn/Al ratios and low U concentrations were observed in sediment, indicating oxic condition. LREE (Light REE) and MREE (Middle REE) enrichment in upper layer and depletion of MREE in deeper layer were shown in porewater profile. This study suggests that porewater flux in the western Pacific basin is a minor source of REEs to seawater, and abundant REEs are enriched in sediments, which is mainly caused by the extensive oxic condition, low sedimentation rate and strong adsorption capacity of sediments. Hence, the removal of REEs of porewater may result in widespread REE-rich sediments in the western Pacific basin.

ALMACAL I: FIRST DUAL-BAND NUMBER COUNTS FROM A DEEP AND WIDE ALMA SUBMILLIMETER SURVEY, FREE FROM COSMIC VARIANCE

Energy Technology Data Exchange (ETDEWEB)

Oteo, I.; Ivison, R. J. [Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ UK (United Kingdom); Zwaan, M. A.; Biggs, A. D. [European Southern Observatory, Karl-Schwarzschild-Strasse 2, D-85748 Garching (Germany); Smail, I., E-mail: ivanoteogomez@gmail.com [Centre for Extragalactic Astronomy, Department of Physics, Durham University, South Road, Durham DH1 3LE UK (United Kingdom)

2016-05-01

We have exploited ALMA calibration observations to carry out a novel, wide, and deep submillimeter (submm) survey, almacal. These calibration data comprise a large number of observations of calibrator fields in a variety of frequency bands and array configurations. By gathering together data acquired during multiple visits to many ALMA calibrators, it is possible to reach noise levels which allow the detection of faint, dusty, star-forming galaxies (DSFGs) over a significant area. In this paper, we outline our survey strategy and report the first results. We have analyzed data for 69 calibrators, reaching depths of ∼25 μ Jy beam{sup −1} at sub-arcsec resolution. Adopting a conservative approach based on ≥5 σ detections, we have found 8 and 11 DSFGs in ALMA bands 6 and 7, respectively, with flux densities S {sub 1.2} m {sub m} ≥ 0.2 mJy. The faintest galaxies would have been missed by even the deepest Herschel surveys. Our cumulative number counts have been determined independently at 870 μ m and 1.2 mm from a sparse sampling of the astronomical sky, and are thus relatively free of cosmic variance. The counts are lower than reported previously by a factor of at least 2×. Future analyses will yield large, secure samples of DSFGs with redshifts determined via the detection of submm spectral lines. Uniquely, our strategy then allows for morphological studies of very faint DSFGs—representative of more normal star-forming galaxies than conventional submm galaxies—in fields where self-calibration is feasible, yielding milliarcsecond spatial resolution.

Huge opportunity for solar cooling

International Nuclear Information System (INIS)

Rowe, Daniel

2014-01-01

In Europe more than 400 solar cooling systems have been installed. By contrast, only a small number of solar cooling installations exist in Australia - primarily adsorption and absorption systems for commercial and hospitals - although these systems are growing. As with other renewable energy technologies, cost is a challenge. However solar cooling is currently competitive with other technologies, with some suggesting that system costs have been decreasing by about 20% per annum in recent times. Australia is also leading efforts in the development of residential solar desiccant technology, currently commercialising Australian-developed technology. Commercial and industrial enterprises are increasingly aware of the impact of demand charges, the potential to install technology as a hedge against future energy price rises and opportunities associated with increased on-site generation and reduced reliance on the grid, often necessitating on-site demand reduction and management. They are also driven by environmental and corporate social responsibility objectives as well as the opportunity for energy independence and uninterruptible operation. Interestingly, many of these interests are mirrdred at residential level, inspiring CSIRO's commercialisation of a domestic scale solar air conditioner with Australian manufacturer Brevis Climate Systems. Australia and other countries are increasingly aware of solar cooling as technology which can reduce or replace grid-powered cooling, particularly in applications where large building thermal energy requirements exist. In these applications, heating, cooling and hot water are generated and used in large amounts and the relative amounts of each can be varied dynamically, depending on building requirements. Recent demonstrations of solar cooling technology in Australia include Hunter TAFE's Solar Desiccant Cooling System - which provides heating, cooling and hot water to commercial training kitchens and classrooms - GPT

Predicting galling behaviour in deep drawing processes

NARCIS (Netherlands)

van der Linde, G.

2011-01-01

Deep drawing is a sheet metal forming process which is widely used in, for example, the automotive industry. With this process it is possible to form complex shaped parts of sheet metal and it is suitable for products that have to be produced in large numbers. The tools for this process are required

Huge increase in gas phase nanoparticle generation by pulsed direct current sputtering in a reactive gas admixture

Science.gov (United States)

Polonskyi, Oleksandr; Peter, Tilo; Mohammad Ahadi, Amir; Hinz, Alexander; Strunskus, Thomas; Zaporojtchenko, Vladimir; Biederman, Hynek; Faupel, Franz

2013-07-01

Using reactive DC sputtering in a gas aggregation cluster source, we show that pulsed discharge gives rise to a huge increase in deposition rate of nanoparticles by more than one order of magnitude compared to continuous operation. We suggest that this effect is caused by an equilibrium between slight target oxidation (during "time-off") and subsequent sputtering of Ti oxides (sub-oxides) at "time-on" with high power impulse.

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...

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

Science.gov (United States)

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

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

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data.

Science.gov (United States)

Koutsoukas, Alexios; Monaghan, Keith J; Li, Xiaoli; Huan, Jun

2017-06-28

In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. The aim of this work was dual, first large number of hyper-parameter configurations were explored to investigate how they affect the performance of DNNs and could act as starting points when tuning DNNs and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naïve Bayes, k-nearest neighbor, random forest and support vector machines. Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed. The open-source Caffe deep-learning framework and modern NVidia GPU units were utilized to carry out this study, allowing large number of DNN configurations to be explored. We show that feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized. Hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons. When compared to the rest methods, tuned DNNs were found to statistically outperform, with p value <0.01 based on Wilcoxon statistical test. DNN achieved on average MCC units of 0.149 higher than NB, 0.092 than kNN, 0.052 than SVM with linear kernel, 0.021 than RF and finally 0.009 higher than SVM with radial basis function kernel. When exploring robustness to noise, non-linear methods were found to perform well when dealing with low levels of noise, lower than or equal to 20%, however when dealing with higher levels of noise, higher than 30%, the Naïve Bayes method was found to perform well and even outperform at the highest level of

Quantum particle-number fluctuations in a two-component Bose gas in a double-well potential

International Nuclear Information System (INIS)

Zin, Pawel; Oles, Bartlomiej; Sacha, Krzysztof

2011-01-01

A two-component Bose gas in a double-well potential with repulsive interactions may undergo a phase separation transition if the interspecies interactions outweigh the intraspecies ones. We analyze the transition in the strong interaction limit within the two-mode approximation. Numbers of particles in each potential well are equal and constant. However, at the transition point, the ground state of the system reveals huge fluctuations of numbers of particles belonging to the different gas components; that is, the probability for observation of any mixture of particles in each potential well becomes uniform.

Huge thermal conductivity enhancement in boron nitride – ethylene glycol nanofluids

International Nuclear Information System (INIS)

Żyła, Gaweł; Fal, Jacek; Traciak, Julian; Gizowska, Magdalena; Perkowski, Krzysztof

2016-01-01

Paper presents the results of experimental studies on thermophysical properties of boron nitride (BN) plate-like shaped particles in ethylene glycol (EG). Essentially, the studies were focused on the thermal conductivity of suspensions of these particles. Nanofluids were obtained with two-step method (by dispersing BN particles in ethylene glycol) and its’ thermal conductivity was analyzed at various mass concentrations, up to 20 wt. %. Thermal conductivity was measured in temperature range from 293.15 K to 338.15 K with 15 K step. The measurements of thermal conductivity of nanofluids were performed in the system based on a device using the transient line heat source method. Studies have shown that nanofluids’ thermal conductivity increases with increasing fraction of nanoparticles. The results of studies also presented that the thermal conductivity of nanofluids changes very slightly with the increase of temperature. - Highlights: • Huge thermal conductivity enhancement in BN-EG nanofluid was reported. • Thermal conductivity increase very slightly with increasing of the temperature. • Thermal conductivity increase linearly with volume concentration of particles.

Huge thermal conductivity enhancement in boron nitride – ethylene glycol nanofluids

Energy Technology Data Exchange (ETDEWEB)

Żyła, Gaweł, E-mail: gzyla@prz.edu.pl [Department of Physics and Medical Engineering, Rzeszow University of Technology, Rzeszow, 35-905 (Poland); Fal, Jacek; Traciak, Julian [Department of Physics and Medical Engineering, Rzeszow University of Technology, Rzeszow, 35-905 (Poland); Gizowska, Magdalena; Perkowski, Krzysztof [Department of Nanotechnology, Institute of Ceramics and Building Materials, Warsaw, 02-676 (Poland)

2016-09-01

Paper presents the results of experimental studies on thermophysical properties of boron nitride (BN) plate-like shaped particles in ethylene glycol (EG). Essentially, the studies were focused on the thermal conductivity of suspensions of these particles. Nanofluids were obtained with two-step method (by dispersing BN particles in ethylene glycol) and its’ thermal conductivity was analyzed at various mass concentrations, up to 20 wt. %. Thermal conductivity was measured in temperature range from 293.15 K to 338.15 K with 15 K step. The measurements of thermal conductivity of nanofluids were performed in the system based on a device using the transient line heat source method. Studies have shown that nanofluids’ thermal conductivity increases with increasing fraction of nanoparticles. The results of studies also presented that the thermal conductivity of nanofluids changes very slightly with the increase of temperature. - Highlights: • Huge thermal conductivity enhancement in BN-EG nanofluid was reported. • Thermal conductivity increase very slightly with increasing of the temperature. • Thermal conductivity increase linearly with volume concentration of particles.

Shallow and Deep-Seated Landslide Differentiation Using Support Vector Machines: A Case Study of the Chuetsu Area, Japan

Directory of Open Access Journals (Sweden)

Jie Dou

2015-01-01

Full Text Available Landslides are one of the most destructive geological disasters affecting Japan every year, resulting in huge losses in life and property. Numerous susceptibility studies have been conducted to minimize the risk of landslides; however, most of these studies do not differentiate landslide types. This study examines the differences in landslide depth, volume and the risk imposed between shallow and deep-seated landslide types. Shallow and deep-seated landslide prediction is useful in utilizing emergency resources by prioritizing target areas while responding to sediment related disasters. This study utilizes a 2-m DEM derived from airborne Light detection and ranging (Lidar, geological information and support vector machines (SVMs to study the 1225 landslides triggered by the M 6.8 Chuetsu earthquake in Japan and the successive aftershocks. Ten factors, including elevation, slope, aspect, curvature, lithology, distance from the nearest geologic boundary, density of geologic boundaries, distance from drainage network, the compound topographic index (CTI and the stream power index (SPI derived from the DEM and a geological map were analyzed. Iterated over 10 random instances the average training and testing accuracy of landslide type prediction was found to be 89.2 and 77.8%, respectively. We also found that the overall accuracy of SVMs does not rapidly decrease with a decrease in training samples. The trained model was then used to prepare a map showing probable future landslides differentiated into shallow and deep-seated landslides.

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...

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.

Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.

Science.gov (United States)

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

2017-12-01

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

Deep 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...

Elective hemi transurethral resection of prostate: a safe and effective method of treating huge benign prostatic hyperplasia

International Nuclear Information System (INIS)

Abidi, S.S.; Feroz, I.; Aslam, M.; Fawad, A.

2012-01-01

To evaluate the safety and efficacy of elective hemi-resection of prostate in patients with huge gland, weighing more than 120 grams. Study Design: Multi centric, analytical comparative study. Place and Duration of Study: Department of Urology, Karachi Medical and Dental College, Abbasi Shaheed Hospital and Dr. Ziauddin Hospital, Karachi, from August 2006 to July 2009. Methodology: All benign cases were included in this study and divided into two groups. In group A, patients having huge prostate (> 120 grams) were placed and hemi TURP was performed. In group B, patients having 60 to 100 grams prostate were placed and conventional Blandy's TURP was performed. Results of both groups were compared in terms of duration of surgery, amount of tissue resected, operative bleeding, postoperative complications, duration of postoperative catheterization, re-admission and re-operations. Effectiveness of procedure was assessed by a simple questionnaire filled by the patients at first month, first year and second year. Patients satisfaction in terms of their ability to void, control urination, frequency, urgency, urge incontinence, haematuria, recurrent UTI, re-admission and re-operations were also assessed. Fisher exact test was applied to compare the safety and efficacy of variables. Results: In group A and B, average age range was 72 and 69 years, average weight of prostate was 148 and 70 grams, average duration of surgery was 102 and 50 minutes respectively. Average weight of resected tissue was 84 and 54 grams and haemoglobin loss was two grams and one gram respectively. Total hospital stay was 5 and 4 days. Total duration of indwelling Foley's catheter (postoperative) was 5 days and 2 days. Patient satisfaction in term of urine flow, urinary control, improvement in frequency and nocturia were comparable in both groups. UTI and re-admission was more in hemi resection group. At the end of 2 years follow-up, there is no statistical difference between the safety and efficacy

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.

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...

Huge Left Ventricular Thrombus and Apical Ballooning associated with Recurrent Massive Strokes in a Septic Shock Patient

Directory of Open Access Journals (Sweden)

Hyun-Jung Lee

2016-02-01

Full Text Available The most feared complication of left ventricular thrombus (LVT is the occurrence of systemic thromboembolic events, especially in the brain. Herein, we report a patient with severe sepsis who suffered recurrent devastating embolic stroke. Transthoracic echocardiography revealed apical ballooning of the left ventricle with a huge LVT, which had not been observed in chest computed tomography before the stroke. This case emphasizes the importance of serial cardiac evaluation in patients with stroke and severe medical illness.

Sleeping money: investigating the huge surpluses of social health insurance in China.

Science.gov (United States)

Liu, JunQiang; Chen, Tao

2013-12-01

The spreading of social health insurance (SHI) worldwide poses challenges for fledging public administrators. Inefficiency, misuse and even corruption threaten the stewardship of those newly established health funds. This article examines a tricky situation faced by China's largest SHI program: the basic health insurance (BHI) scheme for urban employees. BHI accumulated a 406 billion yuan surplus by 2009, although the reimbursement level was still low. Using a provincial level panel database, we find that the huge BHI surpluses are related to the (temporarily) decreasing dependency ratio, the steady growth of average wages, the extension of BHI coverage, and progress in social insurance agency building. The financial situations of local governments and risk pooling level also matter. Besides, medical savings accounts result in about one third of BHI surpluses. Although these findings are not causal, lessons drawn from this study can help to improve the governance and performance of SHI programs in developing countries.

Propranolol in treatment of huge and complicated infantile hemangiomas in egyptian children.

Science.gov (United States)

Hassan, Basheir A; Shreef, Khalid S

2014-01-01

Background. Infantile hemangiomas (IHs) are the most common benign tumours of infancy. Propranolol has recently been reported to be a highly effective treatment for IHs. This study aimed to evaluate the efficacy and side effects of propranolol for treatment of complicated cases of IHs. Patients and Methods. This prospective clinical study included 30 children with huge or complicated IHs; their ages ranged from 2 months to 1 year. They were treated by oral propranolol. Treatment outcomes were clinically evaluated. Results. Superficial cutaneous hemangiomas began to respond to propranolol therapy within one to two weeks after the onset of treatment. The mean treatment period that was needed for the occurrence of complete resolution was 9.4 months. Treatment with propranolol was well tolerated and had few side effects. No rebound growth of the tumors was noted when propranolol dosing stopped except in one case. Conclusion. Propranolol is a promising treatment for IHs without obvious side effects. However, further studies with longer follow-up periods are needed.

Huge Varicose Inferior Mesenteric Vein: an Unanticipated 99mTc-labeled Red Blood Cell Scintigraphy Finding

International Nuclear Information System (INIS)

Hoseinzadeh, Samaneh; Shafiei, Babak; Salehian, Mohamadtaghi; Neshandar Asli, Isa; Ghodoosi, Iraj

2010-01-01

Ectopic varices (EcV) are enlarged portosystemic venous collaterals, which usually develop secondary to portal hypertension (PHT). Mesocaval collateral vessels are unusual pathways to decompress the portal system. Here we report the case of a huge varicose inferior mesenteric vein (IMV) that drained into peri rectal collateral veins, demonstrated by 99m Tc-labeled red blood cell (RBC) scintigraphy performed for lower gastrointestinal (GI) bleeding in a 14-year-old girl. This case illustrates the crucial role of 99m Tc-labeled RBC scintigraphy for the diagnosis of rare ectopic lower GI varices.

Construction of Neural Networks for Realization of Localized Deep Learning

Directory of Open Access Journals (Sweden)

Charles K. Chui

2018-05-01

Full Text Available The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis and online advertisement, speech and handwriting recognition, computer vision and natural language processing, time series forecasting, and search engines. However, theoretical development of deep learning is still at its infancy. The objective of this paper is to introduce a deep neural network (also called deep-net approach to localized manifold learning, with each hidden layer endowed with a specific learning task. For the purpose of illustrations, we only focus on deep-nets with three hidden layers, with the first layer for dimensionality reduction, the second layer for bias reduction, and the third layer for variance reduction. A feedback component is also designed to deal with outliers. The main theoretical result in this paper is the order O(m-2s/(2s+d of approximation of the regression function with regularity s, in terms of the number m of sample points, where the (unknown manifold dimension d replaces the dimension D of the sampling (Euclidean space for shallow nets.

Introduction: From pathogenesis to therapy, deep endometriosis remains a source of controversy.

Science.gov (United States)

Donnez, Jacques

2017-12-01

Deep endometriosis remains a source of controversy. A number of theories may explain its pathogenesis and many arguments support the hypothesis that genetic or epigenetic changes are a prerequisite for development of lesions into deep endometriosis. Deep endometriosis is frequently responsible for pelvic pain, dysmenorrhea, and/or deep dyspareunia, but can also cause obstetrical complications. Diagnosis may be improved by high-quality imaging. Therapeutic approaches are a source of contention as well. In this issue's Views and Reviews, medical and surgical strategies are discussed, and it is emphasized that treatment should be designed according to a patient's symptoms and individual needs. It is also vital that referral centers have the knowledge and experience to treat deep endometriosis medically and/or surgically. The debate must continue because emerging trends in therapy need to be followed and investigated for optimal management. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Deep cleaning of a metallurgical zinc leaching residue and recovery of valuable metals

Science.gov (United States)

Xing, Peng; Ma, Bao-zhong; Zeng, Peng; Wang, Cheng-yan; Wang, Ling; Zhang, Yong-lu; Chen, Yong-qiang; Wang, Shuo; Wang, Qiu-yin

2017-11-01

Huge quantities of zinc leaching residues (ZLRs) generated from zinc production are dumped continuously around the world and pose a potential environmental threat because of their considerable amounts of entrained heavy metals (mainly lead). Most ZLRs have not been properly treated and the valuable metals in them have not yet been effectively recovered. Herein, the deep cleaning of a ZLR and recovery of valuable metals via a hydrometallurgical route were investigated. The cleaning process consists of two essential stages: acid leaching followed by calcium chloride leaching. The optimum conditions for extracting zinc, copper, and indium by acid leaching were a sulfuric acid concentration of 200 g·L-1, a liquid/solid ratio of 4:1 (mL/g), a leaching time of 2 h, and a temperature of 90°C. For lead and silver extractions, the optimum conditions were a calcium chloride concentration of 400 g·L-1, a pH value of 1.0, a leaching time of 1 h, and a temperature of 30°C. After calcium chloride leaching, silver and lead were extracted out and the lead was finally recovered as electrolytic lead by electrowinning. The anglesite phase, which poses the greatest potential environmental hazard, was removed from the ZLR after deep cleaning, thus reducing the cost of environmental management of ZLRs. The treatment of chlorine and spent electrolyte generated in the process was discussed.

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm.

Science.gov (United States)

Kim, Seong Gon; Harwani, Mrudul; Grama, Ananth; Chaterji, Somali

2016-12-08

We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90). We train EP-DNN using p300 binding sites, as enhancers, and TSS and random non-DHS sites, as non-enhancers. We perform same-cell and cross-cell predictions to quantify the validation rate and compare against two state-of-the-art methods, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy with a validation rate of 91.6%, relative to 85.3% for DEEP-ENCODE and 85.5% for RFECS, for a given number of enhancer predictions and also scales better for a larger number of enhancer predictions. Moreover, our H1 → IMR90 predictions turn out to be more accurate than IMR90 → IMR90, potentially because H1 exhibits a richer signature set and our EP-DNN model is expressive enough to extract these subtleties. Our work shows how to leverage the full expressivity of deep learning models, using multiple hidden layers, while avoiding overfitting on the training data. We also lay the foundation for exploration of cross-cell enhancer predictions, potentially reducing the need for expensive experimentation.

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm

Science.gov (United States)

Kim, Seong Gon; Harwani, Mrudul; Grama, Ananth; Chaterji, Somali

2016-12-01

We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90). We train EP-DNN using p300 binding sites, as enhancers, and TSS and random non-DHS sites, as non-enhancers. We perform same-cell and cross-cell predictions to quantify the validation rate and compare against two state-of-the-art methods, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy with a validation rate of 91.6%, relative to 85.3% for DEEP-ENCODE and 85.5% for RFECS, for a given number of enhancer predictions and also scales better for a larger number of enhancer predictions. Moreover, our H1 → IMR90 predictions turn out to be more accurate than IMR90 → IMR90, potentially because H1 exhibits a richer signature set and our EP-DNN model is expressive enough to extract these subtleties. Our work shows how to leverage the full expressivity of deep learning models, using multiple hidden layers, while avoiding overfitting on the training data. We also lay the foundation for exploration of cross-cell enhancer predictions, potentially reducing the need for expensive experimentation.

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.

Deep Space Telecommunications

Science.gov (United States)

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

2000-01-01

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

Greedy Deep Dictionary Learning

OpenAIRE

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

2016-01-01

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

Rhodopsin in the Dark Hot Sea: Molecular Analysis of Rhodopsin in a Snailfish, Careproctus rhodomelas, Living near the Deep-Sea Hydrothermal Vent.

Directory of Open Access Journals (Sweden)

Rie Sakata

Full Text Available Visual systems in deep-sea fishes have been previously studied from a photobiological aspect; however, those of deep-sea fish inhabiting the hydrothermal vents are far less understood due to sampling difficulties. In this study, we analyzed the visual pigment of a deep-sea snailfish, Careproctus rhodomelas, discovered and collected only near the hydrothermal vents of oceans around Japan. Proteins were solubilized from the C. rhodomelas eyeball and subjected to spectroscopic analysis, which revealed the presence of a pigment characterized by an absorption maximum (λmax at 480 nm. Immunoblot analysis of the ocular protein showed a rhodopsin-like immunoreactivity. We also isolated a retinal cDNA encoding the entire coding sequence of putative C. rhodomelas rhodopsin (CrRh. HEK293EBNA cells were transfected with the CrRh cDNA and the proteins extracted from the cells were subjected to spectroscopic analysis. The recombinant CrRh showed the absorption maximum at 480 nm in the presence of 11-cis retinal. Comparison of the results from the eyeball extract and the recombinant CrRh strongly suggests that CrRh has an A1-based 11-cis-retinal chromophore and works as a photoreceptor in the C. rhodomelas retina, and hence that C. rhodomelas responds to dim blue light much the same as other deep-sea fishes. Because hydrothermal vent is a huge supply of viable food, C. rhodomelas likely do not need to participate diel vertical migration and may recognize the bioluminescence produced by aquatic animals living near the hydrothermal vents.

Carbon Dioxide Capture by Deep Eutectic Solvent Impregnated Sea Mango Activated Carbon

Science.gov (United States)

Zulkurnai, N. Z.; Ali, U. F. Md.; Ibrahim, N.; Manan, N. S. Abdul

2018-03-01

The increment amount of the CO2 emission by years has become a major concern worldwide due to the global warming issue. However, the influence modification of activated carbon (AC) has given a huge revolution in CO2 adsorption capture compare to the unmodified AC. In the present study, the Deep Eutectic Solvent (DES) modified surface AC was used for Carbon Dioxide (CO2) capture in the fixed-bed column. The AC underwent pre-carbonization and carbonization processes at 519.8 °C, respectively, with flowing of CO2 gas and then followed by impregnation with 53.75% phosphoric acid (H3PO4) at 1:2 precursor-to-activant ratios. The prepared AC known as sea mango activated carbon (SMAC) was impregnated with DES at 1:2 solid-to-liquid ratio. The DES is composing of choline chloride and urea with ratio 1:2 choline chloride to urea. The optimum adsorption capacity of SMAC was 33.46 mgco2/gsol and 39.40 mgco2/gsol for DES modified AC (DESAC).

Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.

Science.gov (United States)

Ghafoorian, Mohsen; Karssemeijer, Nico; Heskes, Tom; van Uden, Inge W M; Sanchez, Clara I; Litjens, Geert; de Leeuw, Frank-Erik; van Ginneken, Bram; Marchiori, Elena; Platel, Bram

2017-07-11

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with handcrafted features as well as CNNs that do not integrate location information. On a test set of 50 scans, the best configuration of our networks obtained a Dice score of 0.792, compared to 0.805 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value = 0.06).

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 ...

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.

Designing A General Deep Web Harvester by Harvestability Factor

NARCIS (Netherlands)

Khelghati, Mohammadreza; van Keulen, Maurice; Hiemstra, Djoerd

2014-01-01

To make deep web data accessible, harvesters have a crucial role. Targeting different domains and websites enhances the need of a general-purpose harvester which can be applied to different settings and situations. To develop such a harvester, a large number of issues should be addressed. To have

Key Factors to Determine the Borehole Spacing in a Deep Borehole Disposal for HLW

International Nuclear Information System (INIS)

Lee, Jongyoul; Choi, Heuijoo; Lee, Minsoo; Kim, Geonyoung; Kim, Kyeongsoo

2015-01-01

Deep fluids also resist vertical movement because they are density stratified and reducing conditions will sharply limit solubility of most dose critical radionuclides at the depth. Finally, high ionic strengths of deep fluids will prevent colloidal transport. 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 concept for deep borehole disposal of spent fuels or high level radioactive wastes which has been developed by some countries according to the rapid advance in the development of drilling technology, as an alternative method to the deep geological disposal method, was reviewed. After then an analysis on key factors for the distance between boreholes for the disposal of HLW was carried out. In this paper, the general concept for deep borehole disposal of spent fuels or HLW wastes, as an alternative method to the deep geological disposal method, were reviewed. After then an analysis on key factors for the determining the distance between boreholes for the disposal of HLW was carried out. These results can be used for the development of the HLW deep borehole disposal system

Key Factors to Determine the Borehole Spacing in a Deep Borehole Disposal for HLW

Energy Technology Data Exchange (ETDEWEB)

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

2015-05-15

Deep fluids also resist vertical movement because they are density stratified and reducing conditions will sharply limit solubility of most dose critical radionuclides at the depth. Finally, high ionic strengths of deep fluids will prevent colloidal transport. 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 concept for deep borehole disposal of spent fuels or high level radioactive wastes which has been developed by some countries according to the rapid advance in the development of drilling technology, as an alternative method to the deep geological disposal method, was reviewed. After then an analysis on key factors for the distance between boreholes for the disposal of HLW was carried out. In this paper, the general concept for deep borehole disposal of spent fuels or HLW wastes, as an alternative method to the deep geological disposal method, were reviewed. After then an analysis on key factors for the determining the distance between boreholes for the disposal of HLW was carried out. These results can be used for the development of the HLW deep borehole disposal system.

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 ...

Neural network analysis of head-flow curves in deep well pumps

International Nuclear Information System (INIS)

Goelcue, Mustafa

2006-01-01

In impellers with splitter blades, the difficulty in calculation of the flow area of the impeller is because of the unknown flow rate occurring in the two separate areas when the splitter blades are added. Experimental studies were made to investigate the effects of splitter blade length on deep well pump performance for different numbers of blades. Head-flow curves of deep well pump impellers with splitter blades were investigated using artificial neural networks (ANNs). Gradient descent (GD), Gradient descent with momentum (GDM) and Levenberg-Marquardt (LM) learning algorithms were used in the networks. Experimental studies were completed to obtain training and test data. Blade number (z), non-dimensional splitter blade length (L-bar ) and flow rate (Q) were used as the input layer, while the output is head (H m ). For the testing data, the root mean squared error (RMSE), fraction of variance (R 2 ) and mean absolute percentage error (MAPE) were found to be 0.1285, 0.9999 and 1.6821%, respectively. With these results, we believe that the ANN can be used for prediction of head-flow curves as an appropriate method in deep well pump impellers with splitter blades.

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 ...

String fields, higher spins and number theory

CERN Document Server

Polyakov, Dimitri

2018-01-01

The book aims to analyze and explore deep and profound relations between string field theory, higher spin gauge theories and holography the disciplines that have been on the cutting edge of theoretical high energy physics and other fields. These intriguing relations and connections involve some profound ideas in number theory, which appear to be part of a unifying language to describe these connections.

Intelligent Detection of Structure from Remote Sensing Images Based on Deep Learning Method

Science.gov (United States)

Xin, L.

2018-04-01

Utilizing high-resolution remote sensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remote sensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remote sensing image samples and verifies the feasibility of building extraction via experiments.

Topics in deep inelastic scattering

International Nuclear Information System (INIS)

Wandzura, S.M.

1977-01-01

Several topics in deep inelastic lepton--nucleon scattering are discussed, with emphasis on the structure functions appearing in polarized experiments. The major results are: infinite set of new sum rules reducing the number of independent spin dependent structure functions (for electroproduction) from two to one; the application of the techniques of Nachtmann to extract the coefficients appearing in the Wilson operator product expansion; and radiative corrections to the Wilson coefficients of free field theory. Also discussed are the use of dimensional regularization to simplify the calculation of these radiative corrections

Sustainable development of deep-water seaport: the case of Lithuania.

Science.gov (United States)

Burskyte, Vilma; Belous, Olga; Stasiskiene, Zaneta

2011-06-01

In 2003, the Japan International Cooperation Agency carried out a development feasibility study of Klaipeda Seaport (Lithuania). The focus in this study was the evaluation of environmental impacts of the port expansion because it is located in an ecologically sensitive area. While the Japanese researchers focused on the environmental impact analysis, they did not provide unambiguous conclusions. The problems remained unresolved and required further, more detailed consideration and deeper analysis. Environmental sustainability in seaports is an issue of timely importance in many countries given the rapid increase in port-to-port traffic and harbor capacity. This paper explores the situation in Klaipeda Seaport (Lithuania) which is the northernmost ice-free port on the Eastern coast of the Baltic Sea and its challenges in terms of environmental aspects and current pollution situation. This port plays an important role in the economic development of the region and in creating a sustainable society, i.e., a society that continues to develop economically without increasing its impact on our living environment and where the possible reduction of its current impact can be huge due to the fact that the seaport is a place where transport and logistics intersect and constitute large-scale industrial estates. Increasingly, they also turn towards sustainability. Society faces the need for radical change because of increasing technological progress and increasing environmental impact. Environmental and public issues must be addressed by a systemic approach to find harmony among all the subsystems. Therefore, the authors of the article performed an assessment of the deep-water port of Klaipeda sustainable development opportunities tackling the following tasks: (1) Assessing Klaipeda port and the projected deep-water port of the current environment state; (2) Assessing the impact of the water quality of Klaipeda port, depending on the intensity of activity; (3) Assessing the

Going Deeper or Flatter: Connecting Deep Mapping, Flat Ontologies and the Democratizing of Knowledge

Directory of Open Access Journals (Sweden)

Selina Springett

2015-10-01

Full Text Available The concept of “deep mapping”, as an approach to place, has been deployed as both a descriptor of a specific suite of creative works and as a set of aesthetic practices. While its definition has been amorphous and adaptive, a number of distinct, yet related, manifestations identify as, or have been identified by, the term. In recent times, it has garnered attention beyond literary discourse, particularly within the “spatial” turn of representation in the humanities and as a result of expanded platforms of data presentation. This paper takes a brief look at the practice of “deep mapping”, considering it as a consciously performative act and tracing a number of its various manifestations. It explores how deep mapping is a reflection of epistemological trends in ontological practices of connectivity and the “flattening” of knowledge systems. In particular those put forward by post structural and cultural theorists, such as Bruno Latour, Gilles Deleuze, and Felix Guattari, as well as by theorists who associate with speculative realism. The concept of deep mapping as an aesthetic, methodological, and ideological tool, enables an approach to place that democratizes knowledge by crossing temporal, spatial, and disciplinary boundaries.

Propranolol in Treatment of Huge and Complicated Infantile Hemangiomas in Egyptian Children

Directory of Open Access Journals (Sweden)

Basheir A. Hassan

2014-01-01

Full Text Available Background. Infantile hemangiomas (IHs are the most common benign tumours of infancy. Propranolol has recently been reported to be a highly effective treatment for IHs. This study aimed to evaluate the efficacy and side effects of propranolol for treatment of complicated cases of IHs. Patients and Methods. This prospective clinical study included 30 children with huge or complicated IHs; their ages ranged from 2 months to 1 year. They were treated by oral propranolol. Treatment outcomes were clinically evaluated. Results. Superficial cutaneous hemangiomas began to respond to propranolol therapy within one to two weeks after the onset of treatment. The mean treatment period that was needed for the occurrence of complete resolution was 9.4 months. Treatment with propranolol was well tolerated and had few side effects. No rebound growth of the tumors was noted when propranolol dosing stopped except in one case. Conclusion. Propranolol is a promising treatment for IHs without obvious side effects. However, further studies with longer follow-up periods are needed.

Statistical-Mechanical Analysis of Pre-training and Fine Tuning in Deep Learning

Science.gov (United States)

Ohzeki, Masayuki

2015-03-01

In this paper, we present a statistical-mechanical analysis of deep learning. We elucidate some of the essential components of deep learning — pre-training by unsupervised learning and fine tuning by supervised learning. We formulate the extraction of features from the training data as a margin criterion in a high-dimensional feature-vector space. The self-organized classifier is then supplied with small amounts of labelled data, as in deep learning. Although we employ a simple single-layer perceptron model, rather than directly analyzing a multi-layer neural network, we find a nontrivial phase transition that is dependent on the number of unlabelled data in the generalization error of the resultant classifier. In this sense, we evaluate the efficacy of the unsupervised learning component of deep learning. The analysis is performed by the replica method, which is a sophisticated tool in statistical mechanics. We validate our result in the manner of deep learning, using a simple iterative algorithm to learn the weight vector on the basis of belief propagation.

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

Science.gov (United States)

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

2016-12-21

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

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.

Inverse Analysis to Formability Design in a Deep Drawing Process

Science.gov (United States)

Buranathiti, Thaweepat; Cao, Jian

Deep drawing process is an important process adding values to flat sheet metals in many industries. An important concern in the design of a deep drawing process generally is formability. This paper aims to present the connection between formability and inverse analysis (IA), which is a systematical means for determining an optimal blank configuration for a deep drawing process. In this paper, IA is presented and explored by using a commercial finite element software package. A number of numerical studies on the effect of blank configurations to the quality of a part produced by a deep drawing process were conducted and analyzed. The quality of the drawing processes is numerically analyzed by using an explicit incremental nonlinear finite element code. The minimum distance between elemental principal strains and the strain-based forming limit curve (FLC) is defined as tearing margin to be the key performance index (KPI) implying the quality of the part. The initial blank configuration has shown that it plays a highly important role in the quality of the product via the deep drawing process. In addition, it is observed that if a blank configuration is not greatly deviated from the one obtained from IA, the blank can still result a good product. The strain history around the bottom fillet of the part is also observed. The paper concludes that IA is an important part of the design methodology for deep drawing processes.

Deep inelastic processes. Phenomenology. Quark-parton model

International Nuclear Information System (INIS)

Ioffe, B.L.; Lipatov, L.N.; Khoze, V.A.

1983-01-01

Main theoretical approaches and experimental results related to deep inelastic processes are systematically outlined: electroproduction, neutrino scattering on nucleon, electron-positron pairs annihilation into hadron γγ collisions, production of lepton pairs in hadron collisions with a large effective mass or hadrons with large transverse momenta. Kinematics and phenomenology, space-time description of deep inelastic processes, sum rules, parton and quark-parton models are considered. The experiment is briefly discussed in the book. It is performed from the stand point of comparing it with the theory, experimental data are given as of June, 1982. Since the time of accomplishing the study on the manuscript a number of new experimental results not changing however the statements made in the book appeared. Principal consists in experiments with colliding proton-antiproton beams in CERN, which resulted in discovery of intermediate W-bozon

Huge Varicose Inferior Mesenteric Vein: an Unanticipated {sup 99m}Tc-labeled Red Blood Cell Scintigraphy Finding

Energy Technology Data Exchange (ETDEWEB)

Hoseinzadeh, Samaneh; Shafiei, Babak; Salehian, Mohamadtaghi; Neshandar Asli, Isa; Ghodoosi, Iraj [Shaheed Beheshti Medical University, Tehran (Iran, Islamic Republic of)

2010-09-15

Ectopic varices (EcV) are enlarged portosystemic venous collaterals, which usually develop secondary to portal hypertension (PHT). Mesocaval collateral vessels are unusual pathways to decompress the portal system. Here we report the case of a huge varicose inferior mesenteric vein (IMV) that drained into peri rectal collateral veins, demonstrated by {sup 99m}Tc-labeled red blood cell (RBC) scintigraphy performed for lower gastrointestinal (GI) bleeding in a 14-year-old girl. This case illustrates the crucial role of {sup 99m}Tc-labeled RBC scintigraphy for the diagnosis of rare ectopic lower GI varices.

Polychaete Annelid (segmented worms) Species Composition in the Deep Gulf of Mexico following the Deep Water Horizon (DWH) Oil Spill

Science.gov (United States)

QU, F.; Rowe, G.

2012-12-01

Sediments 5 to 9 km from the Deep Water Horizon (DWH) Oil Spill site were sampled using a 0.2 m2 box corer 5 months after the event to assess the effects of the oil spill on polychaete annelid (segmented worms) community structure. Numbers of species, abundance, and biodiversity indices were all significantly lower than pre-spill values from similar depths in the eastern Gulf of Mexico (GoM). All of the five dominant species were different. Non-selective deposit feeders and selective deposit feeders were still the most frequent feeding guilds, but their abundances decreased significantly after the event. A large number of carnivorous Sigalionidae may be a response to an accumulation of PAHs on the sediment. Multivariate analyses (CLUSTER and multidimensional scaling (MDS)) illustrate the differences between assemblages near the DWH and those from prior studies in similar deep GoM habitats. In sum, the polychaete populations appeared to be at an early stage of succession in the recovery from the spill or they could be a resident assemblage that is the natural characteristic infauna in or adjacent to natural seeps of fossil hydrocarbons.

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

Directory of Open Access Journals (Sweden)

Magdalena Guardiola

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

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

[Effects of deep plowing time during the fallow period on water storage-consumption characteristics and wheat yield in dry-land soil.

Science.gov (United States)

Dang, Jian You; Pei, Xue Xia; Zhang, Ding Yi; Wang, Jiao Ai; Zhang, Jing; Wu, Xue Ping

2016-09-01

Through a three-year field trail, effects of deep plowing time during the fallow period on water storage of 0-200 cm soil before sowing, water consumption of growth period, and growth and development of wheat were investigated. Results demonstrated that soil water storage (SWS) of the fallow period was influenced by deep plowing time, precipitation, and rainfall distribution. With postponing the time of deep plowing in the fallow period, SWS was increased firstly, and then decreased. SWS with deep plowing in early or middle of August was 23.9-45.8 mm more than that with deep plowing in mid-July. It would benefit SWS when more precipitation occurred in the fallow period or more rainfall was distributed in August and September. Deep plowing at a proper time could facilitate SWS, N and P absorption of wheat, and the number of stems before winter and the spike number. The yield of wheat with deep plowing in early or middle August was 3.67%-18.2% higher than that with deep plowing in mid-July, and it was positively correlated with water storage of 0-200 cm soil during the fallow period and SWS of each soil layer during the wheat growth period. However, this correlation coefficient would be weakened by adequate rainfall in spring, the critical growing period for wheat. The time of deep plowing mainly affected the water consumption at soil layer of 60-140 cm during wheat growth. Under current farming conditions of south Shanxi, the increased grain yield of wheat could be achieved by combining the measures of high wheat stubble and wheat straw covering for holding soil water and deep plowing between the Beginning of Autumn (August 6th) and the Limit of Heat (August 21st) for promoting soil water penetration characteristics to improve the number of stems before winter and spike.

Fiber Orientation Estimation Guided by a Deep Network.

Science.gov (United States)

Ye, Chuyang; Prince, Jerry L

2017-09-01

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent diffusion signals. To estimate the mixture fractions of the dictionary atoms, a deep network is designed to solve the sparse reconstruction problem. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding a dense basis of FOs is used and a weighted ℓ 1 -norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and typical clinical dMRI data. The results demonstrate the benefit of using a deep network for FO estimation.

Deep Multi-Task Learning for Tree Genera Classification

Science.gov (United States)

Ko, C.; Kang, J.; Sohn, G.

2018-05-01

The goal for our paper is to classify tree genera using airborne Light Detection and Ranging (LiDAR) data with Convolution Neural Network (CNN) - Multi-task Network (MTN) implementation. Unlike Single-task Network (STN) where only one task is assigned to the learning outcome, MTN is a deep learning architect for learning a main task (classification of tree genera) with other tasks (in our study, classification of coniferous and deciduous) simultaneously, with shared classification features. The main contribution of this paper is to improve classification accuracy from CNN-STN to CNN-MTN. This is achieved by introducing a concurrence loss (Lcd) to the designed MTN. This term regulates the overall network performance by minimizing the inconsistencies between the two tasks. Results show that we can increase the classification accuracy from 88.7 % to 91.0 % (from STN to MTN). The second goal of this paper is to solve the problem of small training sample size by multiple-view data generation. The motivation of this goal is to address one of the most common problems in implementing deep learning architecture, the insufficient number of training data. We address this problem by simulating training dataset with multiple-view approach. The promising results from this paper are providing a basis for classifying a larger number of dataset and number of classes in the future.

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.

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.

Computational foundations of the visual number sense.

Science.gov (United States)

Stoianov, Ivilin Peev; Zorzi, Marco

2017-01-01

We provide an emergentist perspective on the computational mechanism underlying numerosity perception, its development, and the role of inhibition, based on our deep neural network model. We argue that the influence of continuous visual properties does not challenge the notion of number sense, but reveals limit conditions for the computation that yields invariance in numerosity perception. Alternative accounts should be formalized in a computational model.

Google DeepMind and healthcare in an age of algorithms.

Science.gov (United States)

Powles, Julia; Hodson, Hal

2017-01-01

Data-driven tools and techniques, particularly machine learning methods that underpin artificial intelligence, offer promise in improving healthcare systems and services. One of the companies aspiring to pioneer these advances is DeepMind Technologies Limited, a wholly-owned subsidiary of the Google conglomerate, Alphabet Inc. In 2016, DeepMind announced its first major health project: a collaboration with the Royal Free London NHS Foundation Trust, to assist in the management of acute kidney injury. Initially received with great enthusiasm, the collaboration has suffered from a lack of clarity and openness, with issues of privacy and power emerging as potent challenges as the project has unfolded. Taking the DeepMind-Royal Free case study as its pivot, this article draws a number of lessons on the transfer of population-derived datasets to large private prospectors, identifying critical questions for policy-makers, industry and individuals as healthcare moves into an algorithmic age.

Biological responses to disturbance from simulated deep-sea polymetallic nodulemining

NARCIS (Netherlands)

Jones, D.O.B.; Kaiser, S.; Sweetman, A.K.; Smith, C.R.; Menot, L.; Vink, A.; Trueblood, D.; Greinert, J.; Billett, D.S.M.; Martinez Arbizu, P.; Radziejewska, T.; Singh, R.; Ingole, B.; Stratmann, T.; Simon-Lledó, E.; Durden, J.M.; Clack, M.R.

2017-01-01

Commercial-scale mining for polymetallic nodules could have a major impact on the deepseaenvironment, but the effects of these mining activities on deep-sea ecosystems are verypoorly known. The first commercial test mining for polymetallic nodules was carried out in1970. Since then a number of

Deep 3 GHz number counts from a P(D) fluctuation analysis

Science.gov (United States)

Vernstrom, T.; Scott, Douglas; Wall, J. V.; Condon, J. J.; Cotton, W. D.; Fomalont, E. B.; Kellermann, K. I.; Miller, N.; Perley, R. A.

2014-05-01

Radio source counts constrain galaxy populations and evolution, as well as the global star formation history. However, there is considerable disagreement among the published 1.4-GHz source counts below 100 μJy. Here, we present a statistical method for estimating the μJy and even sub-μJy source count using new deep wide-band 3-GHz data in the Lockman Hole from the Karl G. Jansky Very Large Array. We analysed the confusion amplitude distribution P(D), which provides a fresh approach in the form of a more robust model, with a comprehensive error analysis. We tested this method on a large-scale simulation, incorporating clustering and finite source sizes. We discuss in detail our statistical methods for fitting using Markov chain Monte Carlo, handling correlations, and systematic errors from the use of wide-band radio interferometric data. We demonstrated that the source count can be constrained down to 50 nJy, a factor of 20 below the rms confusion. We found the differential source count near 10 μJy to have a slope of -1.7, decreasing to about -1.4 at fainter flux densities. At 3 GHz, the rms confusion in an 8-arcsec full width at half-maximum beam is ˜ 1.2 μJy beam-1, and a radio background temperature ˜14 mK. Our counts are broadly consistent with published evolutionary models. With these results, we were also able to constrain the peak of the Euclidean normalized differential source count of any possible new radio populations that would contribute to the cosmic radio background down to 50 nJy.

An emergentist perspective on the origin of number sense.

Science.gov (United States)

Zorzi, Marco; Testolin, Alberto

2017-02-19

The finding that human infants and many other animal species are sensitive to numerical quantity has been widely interpreted as evidence for evolved, biologically determined numerical capacities across unrelated species, thereby supporting a 'nativist' stance on the origin of number sense. Here, we tackle this issue within the 'emergentist' perspective provided by artificial neural network models, and we build on computer simulations to discuss two different approaches to think about the innateness of number sense. The first, illustrated by artificial life simulations, shows that numerical abilities can be supported by domain-specific representations emerging from evolutionary pressure. The second assumes that numerical representations need not be genetically pre-determined but can emerge from the interplay between innate architectural constraints and domain-general learning mechanisms, instantiated in deep learning simulations. We show that deep neural networks endowed with basic visuospatial processing exhibit a remarkable performance in numerosity discrimination before any experience-dependent learning, whereas unsupervised sensory experience with visual sets leads to subsequent improvement of number acuity and reduces the influence of continuous visual cues. The emergent neuronal code for numbers in the model includes both numerosity-sensitive (summation coding) and numerosity-selective response profiles, closely mirroring those found in monkey intraparietal neurons. We conclude that a form of innatism based on architectural and learning biases is a fruitful approach to understanding the origin and development of number sense.This article is part of a discussion meeting issue 'The origins of numerical abilities'. © 2017 The Authors.

Efficacy of deep biopsy for subepithelial lesions in the upper gastrointestinal tract.

Science.gov (United States)

Vaicekauskas, Rolandas; Stanaitis, Juozas; Valantinas, Jonas

2016-01-01

Accurate diagnosis of subepithelial lesions (SELs) in the gastrointestinal tract depends on a variety of methods: endoscopy, endoscopic ultrasound and different types of biopsy. Making an error-free diagnosis is vital for the subsequent application of an appropriate treatment. To evaluate the efficacy of deep biopsy via the endoscopic submucosal dissection (ESD) technique for SELs in the upper gastrointestinal tract. It was a case series study. Deep biopsy via the ESD technique was completed in 38 patients between November 2012 and October 2014. Thirty-eight SELs in the upper gastrointestinal tract of varying size (very small ≤ 1 cm, small 1-2 cm and large ≥ 2 cm) by means of the ESD technique after an incision with an electrosurgical knife of the overlying layers and revealing a small part of the lesion were biopsied under direct endoscopic view. Deep biopsy via the ESD technique was diagnostic in 28 of 38 patients (73.3%; 95% CI: 59.7-89.7%). The diagnostic yield for SELs with a clear endophytic shape increased to 91.3%. An evident endophytic appearance of a subepithelial lesion, the mean number of biopsied samples (6.65 ±1.36) and the total size in length of all samples per case (19.88 ±8.07 mm) were the main criteria influencing the positiveness of deep biopsy in the diagnostic group compared to the nondiagnostic one (p = 0.001; p = 0.025; p = 0.008). Deep biopsy via the ESD technique is an effective and safe method for the diagnosis of SELs especially with a clear endophytic appearance in a large number of biopsied samples.

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.

Tropical teleconnections via the ocean and atmosphere induced by Southern Ocean deep convective events

Science.gov (United States)

Marinov, I.; Cabre, A.; Gunn, A.; Gnanadesikan, A.

2016-12-01

The current generation (CMIP5) of Earth System Models (ESMs) shows a huge variability in their ability to represent Southern Ocean (SO) deep-ocean convection and Antarctic Bottom Water, with a preference for open-sea convection in the Weddell and Ross gyres. A long control simulation in a coarse 3o resolution ESM (the GFDL CM2Mc model) shows a highly regular multi-decadal oscillation between periods of SO open sea convection and non-convective periods. This process also happens naturally, with different frequencies and durations of convection across most CMIP5 models under preindustrial forcing (deLavergne et al, 2014). Here we assess the impact of SO deep convection and resulting sea surface temperature (SST) anomalies on the tropical atmosphere and ocean via teleconnections, with a focus on interannual to multi-decadal timescales. We combine analysis of our low-resolution coupled model with inter-model analysis across historical CMIP5 simulations. SST cooling south of 60S during non-convective decades triggers a stronger, northward shifted SH Hadley cell, which results in intensified northward cross-equatorial moist heat transport and a poleward shift in the ITCZ. Resulting correlations between the cross-equatorial atmospheric heat transport and ITCZ location are in good agreement with recent theories (e.g. Frierson et al. 2013; Donohoe et al. 2014). Lagged correlations between a SO convective index and cross-equatorial heat transports (in the atmosphere and ocean), as well as various tropical (and ENSO) climate indices are analyzed. In the ocean realm, we find that non-convective decades result in weaker AABW formation and weaker ACC but stronger Antarctic Intermediate Water (AAIW) formation, likely as a result of stronger SO westerlies (more positive SAM). The signals of AABW and AAIW are seen in the tropics on short timescales of years to decades in the temperature, heat storage and heat transport anomalies and also in deep and intermediate ocean oxygen. Most

Isolated guitar transcription using a deep belief network

Directory of Open Access Journals (Sweden)

Gregory Burlet

2017-03-01

Full Text Available Music transcription involves the transformation of an audio recording to common music notation, colloquially referred to as sheet music. Manually transcribing audio recordings is a difficult and time-consuming process, even for experienced musicians. In response, several algorithms have been proposed to automatically analyze and transcribe the notes sounding in an audio recording; however, these algorithms are often general-purpose, attempting to process any number of instruments producing any number of notes sounding simultaneously. This paper presents a polyphonic transcription algorithm that is constrained to processing the audio output of a single instrument, specifically an acoustic guitar. The transcription system consists of a novel note pitch estimation algorithm that uses a deep belief network and multi-label learning techniques to generate multiple pitch estimates for each analysis frame of the input audio signal. Using a compiled dataset of synthesized guitar recordings for evaluation, the algorithm described in this work results in an 11% increase in the f-measure of note transcriptions relative to Zhou et al.’s (2009 transcription algorithm in the literature. This paper demonstrates the effectiveness of deep, multi-label learning for the task of polyphonic transcription.

DeepSimulator: a deep simulator for Nanopore sequencing

KAUST Repository

Li, Yu

2017-12-23

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

Deep learning 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....

Stellar Atmospheric Parameterization Based on Deep Learning

Science.gov (United States)

Pan, Ru-yang; Li, Xiang-ru

2017-07-01

Deep learning is a typical learning method widely studied in the fields of machine learning, pattern recognition, and artificial intelligence. This work investigates the problem of stellar atmospheric parameterization by constructing a deep neural network with five layers, and the node number in each layer of the network is respectively 3821-500-100-50-1. The proposed scheme is verified on both the real spectra measured by the Sloan Digital Sky Survey (SDSS) and the theoretic spectra computed with the Kurucz's New Opacity Distribution Function (NEWODF) model, to make an automatic estimation for three physical parameters: the effective temperature (Teff), surface gravitational acceleration (lg g), and metallic abundance (Fe/H). The results show that the stacked autoencoder deep neural network has a better accuracy for the estimation. On the SDSS spectra, the mean absolute errors (MAEs) are 79.95 for Teff/K, 0.0058 for (lg Teff/K), 0.1706 for lg (g/(cm·s-2)), and 0.1294 dex for the [Fe/H], respectively; On the theoretic spectra, the MAEs are 15.34 for Teff/K, 0.0011 for lg (Teff/K), 0.0214 for lg(g/(cm · s-2)), and 0.0121 dex for [Fe/H], respectively.

MRI Verification of a Case of Huge Infantile Rhabdomyoma.

Science.gov (United States)

Ramadani, Naser; Kreshnike, Kreshnike Dedushi; Muçaj, Sefedin; Kabashi, Serbeze; Hoxhaj, Astrit; Jerliu, Naim; Bejiçi, Ramush

2016-04-01

Cardiac rhabdomyoma is type of benign myocardial tumor that is the most common fetal cardiac tumor. Cardiac rhabdomyomas are usually detected before birth or during the first year of life. They account for over 60% of all primary cardiac tumors. A 6 month old child with coughing and obstruction in breathing, was hospitalized in the Pediatric Clinic in UCCK, Pristine. The difficulty of breathing was heard and the pathological noise of the heart was noticed from the pediatrician. In the echo of the heart at the posterior and apico-lateral part of the left ventricle a tumoral mass was presented with the dimensions of 56 × 54 mm that forwarded the contractions of the left ventricle, the mass involved also the left ventricle wall and was not vascularized. The right ventricle was deformed and with the shifting of the SIV on the right the contractility was preserved. Aorta, the left arch and AP were normal with laminar circulation. The pericard was presented free. Radiography of thoracic organs was made; it resulted on cardiomegaly and significant bronchovascular drawing. It was completed with an MRI and it resulted on: Cardiomegaly due to large tumoral mass lesion (60×34 mm) involving lateral wall of left ventricle. It was isointense to the muscle on T1W images, markedly hyperintense on T2W images. There were a few septa or bant like hypointensities within lesion. On postcontrast study it showed avid enhancement. The left ventricle volume was decreased. Mild pericardial effusion was also noted. Surgical intervention was performed and it resulted on the histopathological aspect as a huge infantile rhadbomyoma. In most cases no treatment is required and these lesions regress spontaneously. Patients with left ventricular outflow tract obstruction or refractory arrhythmias respond well to surgical excision. Rhabdomyomas are frequently diagnosed by means of fetal echocardiography during the prenatal period.

Research on fast Fourier transforms algorithm of huge remote sensing image technology with GPU and partitioning technology.

Science.gov (United States)

Yang, Xue; Li, Xue-You; Li, Jia-Guo; Ma, Jun; Zhang, Li; Yang, Jan; Du, Quan-Ye

2014-02-01

Fast Fourier transforms (FFT) is a basic approach to remote sensing image processing. With the improvement of capacity of remote sensing image capture with the features of hyperspectrum, high spatial resolution and high temporal resolution, how to use FFT technology to efficiently process huge remote sensing image becomes the critical step and research hot spot of current image processing technology. FFT algorithm, one of the basic algorithms of image processing, can be used for stripe noise removal, image compression, image registration, etc. in processing remote sensing image. CUFFT function library is the FFT algorithm library based on CPU and FFTW. FFTW is a FFT algorithm developed based on CPU in PC platform, and is currently the fastest CPU based FFT algorithm function library. However there is a common problem that once the available memory or memory is less than the capacity of image, there will be out of memory or memory overflow when using the above two methods to realize image FFT arithmetic. To address this problem, a CPU and partitioning technology based Huge Remote Fast Fourier Transform (HRFFT) algorithm is proposed in this paper. By improving the FFT algorithm in CUFFT function library, the problem of out of memory and memory overflow is solved. Moreover, this method is proved rational by experiment combined with the CCD image of HJ-1A satellite. When applied to practical image processing, it improves effect of the image processing, speeds up the processing, which saves the time of computation and achieves sound result.

Rich Representations with Exposed Semantics for Deep Visual Reasoning

Science.gov (United States)

2016-06-01

of a relationship between visual recognition, associative processing, and episodic memory and provides important clues into the neural mechanism...provides critical evidence of a relationship between visual recognition, associative processing, and episodic memory and provides important clues into...From - To) ;run.- ~01~ Final!Technical 4. TITLE AND SUBTITLE Sa. CONTRACT NUMBER Rich Representations with Exposed Semantics for Deep Visual

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.

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

Science.gov (United States)

Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L

2018-04-01

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.

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.

The origin of nuclear mass number dependence in EMC-effect

International Nuclear Information System (INIS)

Kurihara, Y.; Date, S.; Nakamura, A.; Sato, H.; Sumiyoshi, H.; Yoshinada, K.

1985-03-01

The origin of the mass number dependence of the nucleon structure functions extracted from the deep inelastic lepton-nucleus scattering is investigated by factorizing the structure function into A and x dependent parts. It is found that the mass number dependence is determined by the probability of exotic components in multi-nucleon overlap. This suggests that the deformation of the nucleon structure function is caused by the interaction among nucleons during their overlap. (author)

Age, growth rates, and paleoclimate studies of deep sea corals

Science.gov (United States)

Prouty, Nancy G; Roark, E. Brendan; Andrews, Allen; Robinson, Laura; Hill, Tessa; Sherwood, Owen; Williams, Branwen; Guilderson, Thomas P.; Fallon, Stewart

2015-01-01

Deep-water corals are some of the slowest growing, longest-lived skeletal accreting marine organisms. These habitat-forming species support diverse faunal assemblages that include commercially and ecologically important organisms. Therefore, effective management and conservation strategies for deep-sea corals can be informed by precise and accurate age, growth rate, and lifespan characteristics for proper assessment of vulnerability and recovery from perturbations. This is especially true for the small number of commercially valuable, and potentially endangered, species that are part of the black and precious coral fisheries (Tsounis et al. 2010). In addition to evaluating time scales of recovery from disturbance or exploitation, accurate age and growth estimates are essential for understanding the life history and ecology of these habitat-forming corals. Given that longevity is a key factor for population maintenance and fishery sustainability, partly due to limited and complex genetic flow among coral populations separated by great distances, accurate age structure for these deep-sea coral communities is essential for proper, long-term resource management.

Anatomic variation of the deep venous system and its relationship with deep vein thrombosis found on the lower extremity venograms that were obtained after artificial joint replacements

International Nuclear Information System (INIS)

Lee, Min Sun; Lee, Jee Eun; Hwang, Ji Young; Shim, Sung Shine; Yoo, Jeong Hyun; Suh, Jeong Soo; Park, Jae Young

2006-01-01

We wanted to evaluate the anatomic variations, the number of valves and the presence of deep vein thrombosis (DVT) on the lower extremity venograms obtained after artificial joint replacements, and we also wanted to determine the correlation of the incidence of DVT with the above-mentioned factors and the operation sites. From January to June 2004, conventional ascending contrast venographies of the lower extremities were performed in 119 patients at 7-10 days after artificial joint replacement, and all the patients were asymptomatic. Total knee replacement was done for 152 cases and total hip replacement was done for 34 cases. On all the venographic images of 186 limbs, the anatomic variations were classified and the presence of DVT was evaluated; the number of valves in the superficial femoral vein (SFV) and calf veins was counted. The sites of DVT were classified as calf, thigh and pelvis. Statistically, chi square tests and Fischer's exact tests were performed to determine the correlation of the incidence of DVT with the anatomic variations, the numbers of valves and the operation sites. Theoretically, there are 9 types of anatomical variation in the deep vein system of the lower extremity that can be classified, but only 7 types were observed in this study. The most frequent type was the normal single SFV type and this was noted in 117 cases (63%), and the others were all variations (69 cases, 37%). There was a 22.2% incidence of DVT (69 cases) in the normal single SFV type and 26.4% (17 cases) in the other variations. No significant difference was noted in the incidences of DVT between the two groups. In addition, no significant statistical differences were noted for the incidences of DVT between the single or variant multiple veins in the SFV and the popliteal vein (PV) respectively, between the different groups with small or large numbers of valves in the thigh and calf, respectively, and also between the different operation sites of the hip or knee

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

On the prediction of hydroelastic behaviors of a huge floating structure in waves. 2nd Report; Choogata futai no harochu dansei kyodo no suiteiho ni tsuite

Energy Technology Data Exchange (ETDEWEB)

Murai, M.; Kagemoto, H.; Fujino, M. [The University of Tokyo, Tokyo (Japan)

1997-08-01

On the hydroelastic behaviors of a huge floating structure, a mutual interaction theory based on the area division method is used for the analysis of a fluid problem and a mode analysis method is used for the analysis of deformation. On the continuous deformation of a floating structure, the structure is considered as a set of partial structures obtained when the plane shape was divided into squares and discretely handled as a series of rigid motions in the small partial structures obtained by dividing the partial structures more finely. The experimental result in a water tank and the distribution method at a singular point were compared on the deformation of the elastic floating structure estimated by calculation based on this formulation. The result showed that the estimation method on the hydroelastic problem proposed in this paper is valid. On the prediction of hydroelastic behaviors of a huge floating structure, various calculation examples indicate that the hydroelastic behavior is not only the relation between the structure length and wavelength, but also that the bending rigidity of a structure is a very important factor. For a huge floating structure in the 5,000 m class, up to shorter wavelength of about {lambda}/L = 1/100 must be investigated. 6 refs., 14 figs., 5 tabs.

DeepBlow - a Lagrangian plume model for deep water blowouts

International Nuclear Information System (INIS)

Johansen, Oeistein

2000-01-01

This paper presents a sub-sea blowout model designed with special emphasis on deep-water conditions. The model is an integral plume model based on a Lagrangian concept. This concept is applied to multiphase discharges in the formation of water, oil and gas in a stratified water column with variable currents. The gas may be converted to hydrate in combination with seawater, dissolved into the plume water, or leaking out of the plume due to the slip between rising gas bubbles and the plume trajectory. Non-ideal behaviour of the gas is accounted for by the introduction of pressure- and temperature-dependent compressibility z-factor in the equation of state. A number of case studies are presented in the paper. One of the cases (blowout from 100 m depth) is compared with observations from a field experiment conducted in Norwegian waters in June 1996. The model results are found to compare favourably with the field observations when dissolution of gas into seawater is accounted in the model. For discharges at intermediate to shallow depths (100-250 m), the two major processes limiting plume rise will be: (a) dissolution of gas into ambient water, or (b) bubbles rising out of the inclined plume. These processes tend to be self-enforcing, i.e., when a gas is lost by either of these processes, plume rise tends to slow down and more time will be available for dissolution. For discharges in deep waters (700-1500 m depth), hydrate formation is found to be a dominating process in limiting plume rise. (Author)

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.

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...

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

Science.gov (United States)

Young, Jonathan D; Cai, Chunhui; Lu, Xinghua

2017-10-03

One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system. Robust deep learning model selection identified a network architecture that is biologically plausible. Our model selection results indicated that the 1st hidden layer of our deep learning model should contain about 1300 hidden units to most effectively capture the covariance structure of the input data. This agrees with the estimated number of human transcription factors, which is approximately 1400. This result lends support to our hypothesis that the 1st hidden layer of a deep learning model trained on gene expression data may represent signals related to transcription factor activation. Using the 3rd hidden layer representation of each tumor as learned by our unsupervised deep learning model, we performed consensus clustering on all tumor samples-leading to the discovery of clusters of glioblastoma multiforme with differential survival. One of these clusters contained all of the glioblastoma samples with G-CIMP, a known methylation phenotype driven by the IDH1 mutation and associated with favorable prognosis, suggesting that the hidden units in the 3rd hidden layer representations captured a methylation signal without explicitly using methylation data as input. We also found differentially expressed genes and well-known mutations (NF1, IDH1, EGFR) that were uniquely correlated with each of these clusters. Exploring these unique genes and mutations will allow us to

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

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

[Deep learning and neuronal networks in ophthalmology : Applications in the field of optical coherence tomography].

Science.gov (United States)

Treder, M; Eter, N

2018-04-19

Deep learning is increasingly becoming the focus of various imaging methods in medicine. Due to the large number of different imaging modalities, ophthalmology is particularly suitable for this field of application. This article gives a general overview on the topic of deep learning and its current applications in the field of optical coherence tomography. For the benefit of the reader it focuses on the clinical rather than the technical aspects.

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.

Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images

Directory of Open Access Journals (Sweden)

Guandong Chen

2017-09-01

Full Text Available Polarimetric synthetic aperture radar (SAR remote sensing provides an outstanding tool in oil spill detection and classification, for its advantages in distinguishing mineral oil and biogenic lookalikes. Various features can be extracted from polarimetric SAR data. The large number and correlated nature of polarimetric SAR features make the selection and optimization of these features impact on the performance of oil spill classification algorithms. In this paper, deep learning algorithms such as the stacked autoencoder (SAE and deep belief network (DBN are applied to optimize the polarimetric feature sets and reduce the feature dimension through layer-wise unsupervised pre-training. An experiment was conducted on RADARSAT-2 quad-polarimetric SAR image acquired during the Norwegian oil-on-water exercise of 2011, in which verified mineral, emulsions, and biogenic slicks were analyzed. The results show that oil spill classification achieved by deep networks outperformed both support vector machine (SVM and traditional artificial neural networks (ANN with similar parameter settings, especially when the number of training data samples is limited.

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.

Geotechnical properties of deep-ocean sediments: a critical state approach

International Nuclear Information System (INIS)

Ho, E.W.L.

1988-11-01

The possible disposal of high-level radioactive waste using the sediments of the deep-ocean floor as repositories has initiated research to establish an understanding of the fundamental behaviour of deep-ocean sediments. The work described in this thesis consisted of a series of triaxial stress path tests using microcomputer controlled hydraulic triaxial cells to investigate the strength and stress-strain behaviour for mainly anisotropically (K o ) consolidated 'undisturbed' (tubed) and reconstituted specimens of deep-ocean sediments taken from two study areas in the North Atlantic Ocean. The test results have been analysed within the framework of critical state soil mechanics to investigate sediment characteristics such as the state boundary surface, drained and undrained strength and stress-strain behaviour. While marked anisotropic behaviour is found in a number of respects, the results indicate that analysis in a critical state framework is as valid as for terrestrial sediments. Differences in behaviour between tubed and reconstituted specimens have been observed and the effect of the presence of carbonate has been investigated. An attempt has been made to develop an elasto-plastic constitutive K o model based on critical state concepts. This model has been found to agree reasonably well with experimental data for kaolin and deep-ocean sediments. (author)

Construction of a system using a deep learning algorithm to count cell numbers in nanoliter wells for viable single-cell experiments.

Science.gov (United States)

Kamatani, Takashi; Fukunaga, Koichi; Miyata, Kaede; Shirasaki, Yoshitaka; Tanaka, Junji; Baba, Rie; Matsusaka, Masako; Kamatani, Naoyuki; Moro, Kazuyo; Betsuyaku, Tomoko; Uemura, Sotaro

2017-12-04

For single-cell experiments, it is important to accurately count the number of viable cells in a nanoliter well. We used a deep learning-based convolutional neural network (CNN) on a large amount of digital data obtained as microscopic images. The training set consisted of 103 019 samples, each representing a microscopic grayscale image. After extensive training, the CNN was able to classify the samples into four categories, i.e., 0, 1, 2, and more than 2 cells per well, with an accuracy of 98.3% when compared to determination by two trained technicians. By analyzing the samples for which judgments were discordant, we found that the judgment by technicians was relatively correct although cell counting was often difficult by the images of discordant samples. Based on the results, the system was further enhanced by introducing a new algorithm in which the highest outputs from CNN were used, increasing the accuracy to higher than 99%. Our system was able to classify the data even from wells with a different shape. No other tested machine learning algorithm showed a performance higher than that of our system. The presented CNN system is expected to be useful for various single-cell experiments, and for high-throughput and high-content screening.

Biogenesis: number mysticism in protein thinking.

Science.gov (United States)

Klotz, I M

1993-10-01

Historically, great minds have been tantalized by the idea that integers contain hidden, subtle meanings that could give us deep insights into natural (and supernatural) phenomena. Numerological analysis has been used in religion, mythology, and the sciences. In the field of proteins, integers played a stimulating role during early struggles to unravel structure, but they ultimately proved constrictive and misleading. In contrast, the introduction of imaginary (or complex) numbers into the algebra and numerical analysis of ligand-protein affinities can open new perspectives into such interactions.

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

Science.gov (United States)

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with

Multiple huge epiphrenic esophageal diverticula with motility disease treated with video-assisted thoracoscopic and hand-assisted laparoscopic esophagectomy: a case report

OpenAIRE

Taniguchi, Yoshiki; Takahashi, Tsuyoshi; Nakajima, Kiyokazu; Higashi, Shigeyoshi; Tanaka, Koji; Miyazaki, Yasuhiro; Makino, Tomoki; Kurokawa, Yukinori; Yamasaki, Makoto; Takiguchi, Shuji; Mori, Masaki; Doki, Yuichiro

2017-01-01

Background Epiphrenic esophageal diverticulum is a rare condition that is often associated with a concomitant esophageal motor disorder. Some patients have the chief complaints of swallowing difficulty and gastroesophageal reflux; traditionally, such diverticula have been resected via right thoracotomy. Here, we describe a case with huge multiple epiphrenic diverticula with motility disorder, which were successfully resected using a video-assisted thoracic and laparoscopic procedure. Case pre...

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.

ECT, rTMS, and deepTMS in pharmacoresistant drug-free patients with unipolar depression: a comparative review

Directory of Open Access Journals (Sweden)

Salviati M

2012-01-01

Full Text Available Amedeo Minichino¹, Francesco Saverio Bersani¹, Enrico Capra¹, Rossella Pannese¹, Celeste Bonanno², Massimo Salviati¹, Roberto Delle Chiaie¹, Massimo Biondi¹¹Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, ²Aldo Moro University of Bari, Bari, ItalyBackground: Biological treatments are considered as additional options for the treatment of resistant unipolar depression. Controversial data exist about the efficacy and tolerability of three of the most used somatic treatments: electroconvulsive therapy (ECT, transcranial magnetic stimulation (rTMS, and deep transcranial magnetic stimulation (deepTMS. The aim of this review is to investigate and compare the efficacy and tolerability of these three techniques in drug-free patients with pharmacoresistant unipolar depression.Methods: Three independent reviewers extracted data and assessed the quality of methodological reporting of selected studies. The first outcome was the clinical response to the three different techniques defined as a percentage improvement of Hamilton Depression Rating Scale (HDRS. The second outcome was the evaluation of their neuropsychological effects. The third outcome was the evaluation of the number of remitted patients; remission was defined as an absolute HDRS-24 score of ≤11 or as an absolute HDRS-17 score of ≤8. Tolerability was the fourth outcome; it was evaluated by examining the number of dropped-out patients.Results: The comparative evaluation of HDRS percentage variations shows ECT as the most effective method after 4 weeks of therapy; on the other hand, a better efficacy is obtainable by deepTMS after 2 weeks of therapy. DeepTMS is the technique that gives the best improvement of cognitive performances. The percentage of remitted patients obtained with ECT treatment is the same obtained in the deepTMS group. Both techniques have a remitted patients percentage two times larger than the rTMS. DeepTMS shows a tolerability

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.

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.

MRI Verification of a Case of Huge Infantile Rhabdomyoma

Science.gov (United States)

Ramadani, Naser; Kreshnike, Kreshnike Dedushi; Muçaj, Sefedin; Kabashi, Serbeze; Hoxhaj, Astrit; Jerliu, Naim; Bejiçi, Ramush

2016-01-01

Introduction: Cardiac rhabdomyoma is type of benign myocardial tumor that is the most common fetal cardiac tumor. Cardiac rhabdomyomas are usually detected before birth or during the first year of life. They account for over 60% of all primary cardiac tumors. Case report: A 6 month old child with coughing and obstruction in breathing, was hospitalized in the Pediatric Clinic in UCCK, Pristine. The difficulty of breathing was heard and the pathological noise of the heart was noticed from the pediatrician. In the echo of the heart at the posterior and apico-lateral part of the left ventricle a tumoral mass was presented with the dimensions of 56 × 54 mm that forwarded the contractions of the left ventricle, the mass involved also the left ventricle wall and was not vascularized. The right ventricle was deformed and with the shifting of the SIV on the right the contractility was preserved. Aorta, the left arch and AP were normal with laminar circulation. The pericard was presented free. Radiography of thoracic organs was made; it resulted on cardiomegaly and significant bronchovascular drawing. It was completed with an MRI and it resulted on: Cardiomegaly due to large tumoral mass lesion (60×34 mm) involving lateral wall of left ventricle. It was isointense to the muscle on T1W images, markedly hyperintense on T2W images. There were a few septa or bant like hypointensities within lesion. On postcontrast study it showed avid enhancement. The left ventricle volume was decreased. Mild pericardial effusion was also noted. Surgical intervention was performed and it resulted on the histopathological aspect as a huge infantile rhadbomyoma. Conclusion: In most cases no treatment is required and these lesions regress spontaneously. Patients with left ventricular outflow tract obstruction or refractory arrhythmias respond well to surgical excision. Rhabdomyomas are frequently diagnosed by means of fetal echocardiography during the prenatal period. PMID:27147810

Origins, characteristics, controls, and economic viabilities of deep- basin gas resources

Science.gov (United States)

Price, L.C.

1995-01-01

Dry-gas deposits (methane ???95% of the hydrocarbon (HC) gases) are thought to originate from in-reservoir thermal cracking of oil and C2+ HC gases to methane. However, because methanes from Anadarko Basin dry-gas deposits do not carry the isotopic signature characteristics of C15+ HC destruction, an origin of these methanes from this process is considered improbable. Instead, the isotopic signature of these methanes suggests that they were cogenerated with C15+ HC's. Only a limited resource of deep-basin gas deposits may be expected by the accepted model for the origin of dry-gas deposits because of a limited number of deep-basin oil deposits originally available to be thermally converted to dry gas. However, by the models of this paper (inefficient source-rock oil and gas expulsion, closed fluid systems in petroleum-basin depocenters, and most dry-gas methane cogenerated with C15+ HC's), very large, previously unrecognized, unconventional, deep-basin gas resources are expected. -from Author

Process Simulation of Aluminium Sheet Metal Deep Drawing at Elevated Temperatures

International Nuclear Information System (INIS)

Winklhofer, Johannes; Trattnig, Gernot; Lind, Christoph; Sommitsch, Christof; Feuerhuber, Hannes

2010-01-01

Lightweight design is essential for an economic and environmentally friendly vehicle. Aluminium sheet metal is well known for its ability to improve the strength to weight ratio of lightweight structures. One disadvantage of aluminium is that it is less formable than steel. Therefore complex part geometries can only be realized by expensive multi-step production processes. One method for overcoming this disadvantage is deep drawing at elevated temperatures. In this way the formability of aluminium sheet metal can be improved significantly, and the number of necessary production steps can thereby be reduced. This paper introduces deep drawing of aluminium sheet metal at elevated temperatures, a corresponding simulation method, a characteristic process and its optimization. The temperature and strain rate dependent material properties of a 5xxx series alloy and their modelling are discussed. A three dimensional thermomechanically coupled finite element deep drawing simulation model and its validation are presented. Based on the validated simulation model an optimised process strategy regarding formability, time and cost is introduced.

Sunspot drawings handwritten character recognition method based on deep learning

Science.gov (United States)

Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

2016-05-01

High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

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.

Investigation of the Factors Influencing on Downloading Music by College Students with Using Structural Equations

OpenAIRE

Hamed Aabasi

2014-01-01

Influencing factors of marketplaces and technology have left a deep effect on the music industry. Insisting on the threats coming from P2P technologies, agents of this industry in some countries are continuing to seek for punitive laws against those who present a huge number of music to others to copy. In Iran, however, few laws have been regulated and multimedia files available on the internet have found a wholly new and extremely aggressive form of media behaviors. This study is aimed at de...

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.

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

OpenAIRE

Li, Guohui; Zhang, Songling; Yang, Hong

2017-01-01

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

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.

Comparison between Laying Hen Performance in the Cage System and the Deep Litter System on a Diet Free from Animal Protein

Directory of Open Access Journals (Sweden)

E. Voslářová

2006-01-01

Full Text Available Battery cage systems for housing laying hens are being replaced by alternative systems including the deep litter system. At the same time, the substitution of meat and bone meal by vegetable matter in poultry feed mixtures is sought in the nutrition of laying hens. In the experiment, we compared the performance of laying hens of the ISA BROWN hybrid in both the cage system and the deep litter system, on a diet with the meat and bone meal content replaced by vegetable feeds (based on lupin. In the first group, 36 laying hens were kept in the deep litter system; in the second group, 36 laying hens were kept in cages. Over the period of nine months, the number of eggs laid, their weight, shell quality, the clinical state of the laying hens and incidence of their mortality were monitored daily. We found that in the cage system a higher number of eggs was obtained; a lower mean egg weight (p p p p p > 0.05, and the number of laying hens which died was lower (p < 0.05 in comparison with the deep litter system. The results of the experiment demonstrate that, with the substitution of meat and bone meal by vegetable matter in the feed mixtures for laying hens, there are differences between the performance of laying hens from the deep litter system as compared to the laying hens from the cage system. The deep litter system better meets the requirements for the welfare of laying hens; however, it provides a lower yield.

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...

[Transcatheter embolization for huge pulmonary arteriovenous fistula using metallic "spider" and spring embolus--application of hand-made metallic "spider" using partial monorail technique].

Science.gov (United States)

Hirota, S; Sako, M; Fujita, Y; Hasegawa, Y; Sugimoto, K; Suzuki, Y; Kono, M

1992-07-25

We performed transcatheter embolization in two cases with huge pulmonary arteriovenous fistula (AVF) using a metallic "spider" and spring embolus. Conventional spring embolus or detachable balloon could not be used in these cases. Metallic spider was indicated for pulmonary AVF with a feeding artery diameter of more than 16 mm to prevent embolus passing through the AVF. In the first case, we used large handmade metallic spiders of 25 mm in diameter followed by embolization by numerous spring coils. At that time, a partial monorail technique was newly devised to carry the large metallic spider into the feeding artery, otherwise the spider could not pass into a 9F catheter. After embolization, symptoms and PaO2 in arterial blood improved remarkably in both cases. In the second case, a spring coil migrated into the normal pulmonary artery, but no infarction resulted. In conclusion, the metallic spider was very useful for embolization of hugee pulmonary AVF to avoid the embolus passing through and to tangle spring coils together with it. If commercially available "spiders" are too small, ones can be made easily.

A Fast, High Quality, and Reproducible Parallel Lagged-Fibonacci Pseudorandom Number Generator

Science.gov (United States)

Mascagni, Michael; Cuccaro, Steven A.; Pryor, Daniel V.; Robinson, M. L.

1995-07-01

We study the suitability of the additive lagged-Fibonacci pseudo-random number generator for parallel computation. This generator has relatively short period with respect to the size of its seed. However, the short period is more than made up for with the huge number of full-period cycles it contains. These different full period cycles are called equivalence classes. We show how to enumerate the equivalence classes and how to compute seeds to select a given equivalence class, In addition, we present some theoretical measures of quality for this generator when used in parallel. Next, we conjecture on the size of these measures of quality for this generator. Extensive empirical evidence supports this conjecture. In addition, a probabilistic interpretation of these measures leads to another conjecture similarly supported by empirical evidence. Finally we give an explicit parallelization suitable for a fully reproducible asynchronous MIMD implementation.

On the estimation method of hydrodynamic forces acting on a huge floating structure; Choogata futai ni hataraku haryoku ryutairyoku no suiteiho ni kansuru kenkyu

Energy Technology Data Exchange (ETDEWEB)

Kagemoto, H.; Fujino, M.; Zhu, T. [The University of Tokyo, Tokyo (Japan)

1996-12-31

A floating structure such as an international airport is anticipated to have a length of about 5,000 m and a width of about 1,000 m. A singular point method may be used as a method to estimate force that such a floating body is subjected to from waves. In order to derive a solution with practically sufficient accuracy, 1250 elements are required in the length direction and 250 elements in the width direction, or a total of 312,500 elements. Calculating this number of elements should use finally a linear equation system handling complex coefficients comprising 312,500 elements, which would require a huge amount of calculation time. This paper proposes a method to derive solution on wave forces acting on a super-large floating structure or fluid force coefficients such as added mass coefficients and decay coefficients at a practically workable calculation amount and still without degrading the accuracy. The structure was assumed to be a box-shaped structure. Strengths of the singular points to be distributed on each element were assumed to be almost constant except for edges in lateral, oblique and longitudinal waves. Under this assumption, the interior of the floating structure excepting its edges was represented by several large elements to have reduced the number of elements. A calculation method proposed based on this conception was verified of its effectiveness. 2 refs., 25 figs., 3 tabs.

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)

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...

PC-SAFT modeling of CO2 solubilities in hydrophobic deep eutectic solvents

NARCIS (Netherlands)

Dietz, C.H.J.T.; van Osch, D.J.G.P.; Kroon, M.C.; Sadowski, G.; van Sint Annaland, M.; Gallucci, F.; Zubeir, L.F.; Held, C.

2017-01-01

The PC-SAFT 'pseudo-pure' approach was used for the modeling of CO2 solubilities in various hydrophobic deep eutectic solvents (DESs) for the first time. Only liquid density data were used to obtain the segment number, the temperature-independent segment diameter and the dispersion-energy parameter,

Detailed Load Analysis of the baseline 5MW DeepWind Concept

DEFF Research Database (Denmark)

Verelst, David Robert; Aagaard Madsen, Helge; Kragh, Knud Abildgaard

This report presents an overview of the design of the DeepWind vertical axis oating wind turbine. One could present this as the "nal design", however, it is hoped that more design iterations will follow in the future, but under the umbrella of new and dierent projects. The state of the design...... that is reported here will be called version 2.2.0. The numbering system has just been introduced at the present design version, but the rst 5MW design called the "baseline design" [1] was developed in 2011 and this will therefore be called version 1.0.0. In this report, the design loads of the DeepWind 5 MW...

Interaction Deep Excavation Adjacent Structure Numerical Two and Three Dimensional Modeling

International Nuclear Information System (INIS)

Abdallah, M.; Chehade, F. H.; Chehade, W.; Fawaz, A.

2011-01-01

Urban development often requires the construction of deep excavations near to buildings or other structures. We have to study complex material structure interactions where we should take into consideration several particularities. In this paper, we perform a numerical modeling with the finite element method, using PLAXIS software, of the interaction deep excavation-diaphragm wall-soil-structure in the case of non linear soil behavior. We focus our study on a comparison of the results given respectively by two and three dimensional modelings. This allows us to give some recommendations concerning the validity of twodimensional study. We perform a parametric study according to the initial loading on the structure and the struts number. (author)

Unveiling the Biodiversity of Deep-Sea Nematodes through Metabarcoding: Are We Ready to Bypass the Classical Taxonomy?

Science.gov (United States)

Dell'Anno, Antonio; Carugati, Laura; Corinaldesi, Cinzia; Riccioni, Giulia; Danovaro, Roberto

2015-01-01

Nematodes inhabiting benthic deep-sea ecosystems account for >90% of the total metazoan abundances and they have been hypothesised to be hyper-diverse, but their biodiversity is still largely unknown. Metabarcoding could facilitate the census of biodiversity, especially for those tiny metazoans for which morphological identification is difficult. We compared, for the first time, different DNA extraction procedures based on the use of two commercial kits and a previously published laboratory protocol and tested their suitability for sequencing analyses of 18S rDNA of marine nematodes. We also investigated the reliability of Roche 454 sequencing analyses for assessing the biodiversity of deep-sea nematode assemblages previously morphologically identified. Finally, intra-genomic variation in 18S rRNA gene repeats was investigated by Illumina MiSeq in different deep-sea nematode morphospecies to assess the influence of polymorphisms on nematode biodiversity estimates. Our results indicate that the two commercial kits should be preferred for the molecular analysis of biodiversity of deep-sea nematodes since they consistently provide amplifiable DNA suitable for sequencing. We report that the morphological identification of deep-sea nematodes matches the results obtained by metabarcoding analysis only at the order-family level and that a large portion of Operational Clustered Taxonomic Units (OCTUs) was not assigned. We also show that independently from the cut-off criteria and bioinformatic pipelines used, the number of OCTUs largely exceeds the number of individuals and that 18S rRNA gene of different morpho-species of nematodes displayed intra-genomic polymorphisms. Our results indicate that metabarcoding is an important tool to explore the diversity of deep-sea nematodes, but still fails in identifying most of the species due to limited number of sequences deposited in the public databases, and in providing quantitative data on the species encountered. These aspects

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.

On the complexity of neural network classifiers: a comparison between shallow and deep architectures.

Science.gov (United States)

Bianchini, Monica; Scarselli, Franco

2014-08-01

Recently, researchers in the artificial neural network field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with common sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.

Anatomical variations within the deep posterior compartment of the leg and important clinical consequences.

Science.gov (United States)

Hislop, M; Tierney, P

2004-09-01

The management of musculoskeletal conditions makes up a large part of a sports medicine practitioner's practice. A thorough knowledge of anatomy is an essential component of the armament necessary to decipher the large number of potential conditions that may confront these practitioners. To cloud the issue further, anatomical variations may be present, such as supernumerary muscles, thickened fascial bands or variant courses of nerves and blood vessels, which can themselves manifest as acute or chronic conditions that lead to significant morbidity or limitation of activity. There are a number of contentious areas within the literature surrounding the anatomy of the leg, particularly involving the deep posterior compartment. Conditions such as chronic exertional compartment syndrome, tibial periostitis (shin splints), peripheral nerve entrapment and tarsal tunnel syndrome may all be affected by subtle anatomical variations. This paper primarily focuses on the deep posterior compartment of the leg and uses the gross dissection of cadaveric specimens to describe definitively the anatomy of the deep posterior compartment. Variant fascial attachments of flexor digitorum longus are documented and potential clinical sequelae such as chronic exertional compartment syndrome and tarsal tunnel syndrome are discussed.

Smooth Horizonless Geometries Deep Inside the Black-Hole Regime.

Science.gov (United States)

Bena, Iosif; Giusto, Stefano; Martinec, Emil J; Russo, Rodolfo; Shigemori, Masaki; Turton, David; Warner, Nicholas P

2016-11-11

We construct the first family of horizonless supergravity solutions that have the same mass, charges, and angular momenta as general supersymmetric rotating D1-D5-P black holes in five dimensions. This family includes solutions with arbitrarily small angular momenta, deep within the regime of quantum numbers and couplings for which a large classical black hole exists. These geometries are well approximated by the black-hole solution, and in particular exhibit the same near-horizon throat. Deep in this throat, the black-hole singularity is resolved into a smooth cap. We also identify the holographically dual states in the N=(4,4) D1-D5 orbifold conformal field theory (CFT). Our solutions are among the states counted by the CFT elliptic genus, and provide examples of smooth microstate geometries within the ensemble of supersymmetric black-hole microstates.

A Huge Capital Drop with Compression of Femoral Vessels Associated with Hip Osteoarthritis

Directory of Open Access Journals (Sweden)

Tomoya Takasago

2015-01-01

Full Text Available A capital drop is a type of osteophyte at the inferomedial portion of the femoral head commonly observed in hip osteoarthritis (OA, secondary to developmental dysplasia. Capital drop itself is typically asymptomatic; however, symptoms can appear secondary to impinge against the acetabulum or to irritation of the surrounding tissues, such as nerves, vessels, and tendons. We present here a case of unilateral leg edema in a patient with hip OA, caused by a huge bone mass occurring at the inferomedial portion of the femoral head that compressed the femoral vessels. We diagnosed this bone mass as a capital drop secondary to hip OA after confirming that the mass occurred at least after the age of 63 years based on a previous X-ray. We performed early resection and total hip arthroplasty since the patient’s hip pain was due to both advanced hip OA and compression of the femoral vessels; moreover, we aimed to prevent venous thrombosis secondary to vascular compression considering the advanced age and the potent risk of thrombosis in the patient. A large capital drop should be considered as a cause of vascular compression in cases of unilateral leg edema in OA patients.

Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition

Directory of Open Access Journals (Sweden)

Sheng Shen

2018-04-01

Full Text Available The accuracy of underwater acoustic targets recognition via limited ship radiated noise can be improved by a deep neural network trained with a large number of unlabeled samples. However, redundant features learned by deep neural network have negative effects on recognition accuracy and efficiency. A compressed deep competitive network is proposed to learn and extract features from ship radiated noise. The core idea of the algorithm includes: (1 Competitive learning: By integrating competitive learning into the restricted Boltzmann machine learning algorithm, the hidden units could share the weights in each predefined group; (2 Network pruning: The pruning based on mutual information is deployed to remove the redundant parameters and further compress the network. Experiments based on real ship radiated noise show that the network can increase recognition accuracy with fewer informative features. The compressed deep competitive network can achieve a classification accuracy of 89.1 % , which is 5.3 % higher than deep competitive network and 13.1 % higher than the state-of-the-art signal processing feature extraction methods.

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

Science.gov (United States)

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

2018-01-01

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

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

Directory of Open Access Journals (Sweden)

Roberto Danovaro

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

A novel deep learning approach for classification of EEG motor imagery signals.

Science.gov (United States)

Tabar, Yousef Rezaei; Halici, Ugur

2017-02-01

Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

Exploring the Earth Using Deep Learning Techniques

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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

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 soil carbon stock in Chinese Loess Plateau and its turnover

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Song, C.; Han, G.; Yingchun, S.; Liu, C. Q.

2017-12-01

The loess plateau in northwestern China has been regarded as a huge carbon stock in China. However, so far, the mechanisms of carbon cycle in deep loess is still not well known. Hence, we established a field experiment site of carbon cycle in deep loess at Qiushe village, Lingtai county, Gansu province, and observed: (1) the hydro-chemical composition, DIC (Dissolved Inorganic Carbon), DOC (Dissolved Organic Carbon), and POC (Particulate Organic Carbon) in spring water discharging from loess section in Qiushe village, Lingtai county, Gansu province of Northwestern China; and (2) soil CO2 concentration and its lateral fluxes in loess section. The results showed that: (i) The DIC and DOC concentration in groundwater of loess area is 5.25 5.45mmol/L, and 0.59 0.62 mg/L, respectively, while POC concentration is high due to the mixture of loess particle matter. According to the ion balance of carbonate weathering reaction, the 2.82 mmol CO2 can be absorbed by carbonate weathering when 1 L rainfall can infiltrate into the loess until below the zero flux plane. (2) CO2 concentration in loess is higher than in atmosphere and reaches the maximum of 4180 μmol·mol-1 in S14, different loess/paleosol fails to display an instinct trend. The δ13C value of CO2 ranged from -21.31 ‰ to -15.37 ‰, and had a positive relationship with 1/[CO2] (r = 0.74), suggesting that CO2 in loess is not only relative to decomposed organic carbon by microbe, and also to the balance system among CaCO3-H2O-CO2 in the interface between saturated and unsaturated zone. The comparison between the lateral flux of CO2 in loess profile and the vertical CO2 flux in ground surface reveal that ignoring the lateral flux of CO2 may lead to a severe underestimation of soil carbon emission in mountainous area. So the geomorphological surficial area should be used instead of acreage in relative models to avoid the underestimation during estimating the soil carbon emission. (3) At the annual scale, the carbon

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...

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

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.

Development of anomaly detection models for deep subsurface monitoring

Science.gov (United States)

Sun, A. Y.

2017-12-01

Deep subsurface repositories are used for waste disposal and carbon sequestration. Monitoring deep subsurface repositories for potential anomalies is challenging, not only because the number of sensor networks and the quality of data are often limited, but also because of the lack of labeled data needed to train and validate machine learning (ML) algorithms. Although physical simulation models may be applied to predict anomalies (or the system's nominal state for that sake), the accuracy of such predictions may be limited by inherent conceptual and parameter uncertainties. The main objective of this study was to demonstrate the potential of data-driven models for leakage detection in carbon sequestration repositories. Monitoring data collected during an artificial CO2 release test at a carbon sequestration repository were used, which include both scalar time series (pressure) and vector time series (distributed temperature sensing). For each type of data, separate online anomaly detection algorithms were developed using the baseline experiment data (no leak) and then tested on the leak experiment data. Performance of a number of different online algorithms was compared. Results show the importance of including contextual information in the dataset to mitigate the impact of reservoir noise and reduce false positive rate. The developed algorithms were integrated into a generic Web-based platform for real-time anomaly detection.

Crystal structure of Clostridium botulinum whole hemagglutinin reveals a huge triskelion-shaped molecular complex.

Science.gov (United States)

Amatsu, Sho; Sugawara, Yo; Matsumura, Takuhiro; Kitadokoro, Kengo; Fujinaga, Yukako

2013-12-06

Clostridium botulinum HA is a component of the large botulinum neurotoxin complex and is critical for its oral toxicity. HA plays multiple roles in toxin penetration in the gastrointestinal tract, including protection from the digestive environment, binding to the intestinal mucosal surface, and disruption of the epithelial barrier. At least two properties of HA contribute to these roles: the sugar-binding activity and the barrier-disrupting activity that depends on E-cadherin binding of HA. HA consists of three different proteins, HA1, HA2, and HA3, whose structures have been partially solved and are made up mainly of β-strands. Here, we demonstrate structural and functional reconstitution of whole HA and present the complete structure of HA of serotype B determined by x-ray crystallography at 3.5 Å resolution. This structure reveals whole HA to be a huge triskelion-shaped molecule. Our results suggest that whole HA is functionally and structurally separable into two parts: HA1, involved in recognition of cell-surface carbohydrates, and HA2-HA3, involved in paracellular barrier disruption by E-cadherin binding.

Integrated study of Mediterranean deep canyons: Novel results and future challenges

Science.gov (United States)

Canals, M.; Company, J. B.; Martín, D.; Sànchez-Vidal, A.; Ramírez-Llodrà, E.

2013-11-01

This volume compiles a number of scientific papers resulting from a sustained multidisciplinary research effort of the deep-sea ecosystem in the Mediterranean Sea. This started 20 years ago and peaked over the last few years thanks to a number of Spanish and European projects such as PROMETEO, DOS MARES, REDECO, GRACCIE, HERMES, HERMIONE and PERSEUS, amongst others. The geographic focus of most papers is on the NW Mediterranean Sea including the Western Gulf of Lion and the North Catalan margin, with a special attention to submarine canyons, in particular the Blanes and Cap de Creus canyons. This introductory article to the Progress in Oceanography special issue on “Mediterranean deep canyons” provides background information needed to better understand the individual papers forming the volume, comments previous reference papers related to the main topics here addressed, and finally highlights the existing relationships between atmospheric forcing, oceanographic processes, seafloor physiography, ecosystem response, and litter and chemical pollution. This article also aims at constituting a sort of glue, in terms of existing knowledge and concepts and novel findings, linking together the other twenty papers in the volume, also including some illustrative figures. The main driving ideas behind this special issue, particularly fitting to the study area of the NW Mediterranean Sea, could be summarized as follows: (i) the atmosphere and the deep-sea ecosystem are connected through oceanographic processes originating in the coastal area and the ocean surface, which get activated at the occasion of high-energy events leading to fast transfers of matter and energy to the deep; (ii) shelf indented submarine canyons play a pivotal role in such transfers, which involve dense water, sedimentary particles, organic matter, litter and chemical pollutants; (iii) lateral inputs (advection) from the upper continental margin contributes significantly to the formation of

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.

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.

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.

Hydrogen-terminated mesoporous silicon monoliths with huge surface area as alternative Si-based visible light-active photocatalysts

KAUST Repository

Li, Ting

2016-07-21

Silicon-based nanostructures and their related composites have drawn tremendous research interest in solar energy storage and conversion. Mesoporous silicon with a huge surface area of 400-900 m2 g-1 developed by electrochemical etching exhibits excellent photocatalytic ability and stability after 10 cycles in degrading methyl orange under visible light irradiation, owing to its unique mesoporous network, abundant surface hydrides and efficient light harvesting. This work showcases the profound effects of surface area, crystallinity, pore topology on charge migration/recombination and mass transportation. Therein the ordered 1D channel array has outperformed the interconnected 3D porous network by greatly accelerating the mass diffusion and enhancing the accessibility of the active sites on the extensive surfaces. © 2016 The Royal Society of Chemistry.

Partitioned learning of deep Boltzmann machines for SNP data.

Science.gov (United States)

Hess, Moritz; Lenz, Stefan; Blätte, Tamara J; Bullinger, Lars; Binder, Harald

2017-10-15

Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen the joint distribution of SNPs, followed by training several DBMs on SNP partitions that were identified by the screening. Aggregate features representing SNP patterns and the corresponding SNPs are extracted from the DBMs by a combination of statistical tests and sparse regression. In simulated case-control data, we show how this can uncover complex SNP patterns and augment results from univariate approaches, while maintaining type 1 error control. Time-to-event endpoints are considered in an application with acute myeloid leukemia patients, where SNP patterns are modeled after a pre-screening based on gene expression data. The proposed approach identified three SNPs that seem to jointly influence survival in a validation dataset. This indicates the added value of jointly investigating SNPs compared to standard univariate analyses and makes partitioned learning of DBMs an interesting complementary approach when analyzing SNP data. A Julia package is provided at 'http://github.com/binderh/BoltzmannMachines.jl'. binderh@imbi.uni-freiburg.de. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Huge Inverse Magnetization Generated by Faraday Induction in Nano-Sized Au@Ni Core@Shell Nanoparticles.

Science.gov (United States)

Kuo, Chen-Chen; Li, Chi-Yen; Lee, Chi-Hung; Li, Hsiao-Chi; Li, Wen-Hsien

2015-08-25

We report on the design and observation of huge inverse magnetizations pointing in the direction opposite to the applied magnetic field, induced in nano-sized amorphous Ni shells deposited on crystalline Au nanoparticles by turning the applied magnetic field off. The magnitude of the induced inverse magnetization is very sensitive to the field reduction rate as well as to the thermal and field processes before turning the magnetic field off, and can be as high as 54% of the magnetization prior to cutting off the applied magnetic field. Memory effect of the induced inverse magnetization is clearly revealed in the relaxation measurements. The relaxation of the inverse magnetization can be described by an exponential decay profile, with a critical exponent that can be effectively tuned by the wait time right after reaching the designated temperature and before the applied magnetic field is turned off. The key to these effects is to have the induced eddy current running beneath the amorphous Ni shells through Faraday induction.

Huge Inverse Magnetization Generated by Faraday Induction in Nano-Sized Au@Ni Core@Shell Nanoparticles

Science.gov (United States)

Kuo, Chen-Chen; Li, Chi-Yen; Lee, Chi-Hung; Li, Hsiao-Chi; Li, Wen-Hsien

2015-01-01

We report on the design and observation of huge inverse magnetizations pointing in the direction opposite to the applied magnetic field, induced in nano-sized amorphous Ni shells deposited on crystalline Au nanoparticles by turning the applied magnetic field off. The magnitude of the induced inverse magnetization is very sensitive to the field reduction rate as well as to the thermal and field processes before turning the magnetic field off, and can be as high as 54% of the magnetization prior to cutting off the applied magnetic field. Memory effect of the induced inverse magnetization is clearly revealed in the relaxation measurements. The relaxation of the inverse magnetization can be described by an exponential decay profile, with a critical exponent that can be effectively tuned by the wait time right after reaching the designated temperature and before the applied magnetic field is turned off. The key to these effects is to have the induced eddy current running beneath the amorphous Ni shells through Faraday induction. PMID:26307983

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.

Hydrogen-bearing iron peroxide and its implications to the deep Earth

Science.gov (United States)

Liu, J.; Hu, Q.; Kim, D. Y.; Wu, Z.; Wang, W.; Alp, E. E.; Yang, L.; Xiao, Y.; Meng, Y.; Chow, P.; Greenberg, E.; Prakapenka, V. B.; Mao, H. K.; Mao, W. L.

2017-12-01

Hydrous materials subducted into the deep mantle may play a significant role in the geophysical and geochemical processes of the lower mantle through geological time, but their roles have not become clear yet in the region. Hydrogen-bearing iron peroxide (FeO2Hx) was recently discovered to form through dehydrogenation of goethite (e.g., FeOOH) and the reaction between hematite (Fe2O3) and water under deep lower mantle conditions. We conducted synchrotron Mössbauer, X-ray absorption, and X-ray emission spectroscopy measurements to investigate the electronic spin and valence states of iron in hydrogen-bearing iron peroxide (FeO2Hx) in-situ at high pressures. Combined with theoretical calculations and other high-pressure experiments (i.e., nuclear resonant inelastic x-ray scattering spectroscopy and X-ray diffraction coupled with laser-heated diamond-anvil cell techniques), we find that the intriguing properties of FeO2Hx could shed light on the origin of a number of the observed geochemical and geophysical anomalies in the deep Earth.

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.

Invertebrate population genetics across Earth's largest habitat: The deep-sea floor.

Science.gov (United States)

Taylor, M L; Roterman, C N

2017-10-01

Despite the deep sea being the largest habitat on Earth, there are just 77 population genetic studies of invertebrates (115 species) inhabiting non-chemosynthetic ecosystems on the deep-sea floor (below 200 m depth). We review and synthesize the results of these papers. Studies reveal levels of genetic diversity comparable to shallow-water species. Generally, populations at similar depths were well connected over 100s-1,000s km, but studies that sampled across depth ranges reveal population structure at much smaller scales (100s-1,000s m) consistent with isolation by adaptation across environmental gradients, or the existence of physical barriers to connectivity with depth. Few studies were ocean-wide (under 4%), and 48% were Atlantic-focused. There is strong emphasis on megafauna and commercial species with research into meiofauna, "ecosystem engineers" and other ecologically important species lacking. Only nine papers account for ~50% of the planet's surface (depths below 3,500 m). Just two species were studied below 5,000 m, a quarter of Earth's seafloor. Most studies used single-locus mitochondrial genes revealing a common pattern of non-neutrality, consistent with demographic instability or selective sweeps; similar to deep-sea hydrothermal vent fauna. The absence of a clear difference between vent and non-vent could signify that demographic instability is common in the deep sea, or that selective sweeps render single-locus mitochondrial studies demographically uninformative. The number of population genetics studies to date is miniscule in relation to the size of the deep sea. The paucity of studies constrains meta-analyses where broad inferences about deep-sea ecology could be made. © 2017 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

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.

Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis.

Science.gov (United States)

Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir M; Helvie, Mark A; Richter, Caleb; Cha, Kenny

2018-05-01

Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, we propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in digital breast tomosynthesis (DBT). The objective is to prune the number of tunable parameters while preserving the classification accuracy. In the first stage transfer learning, 19 632 augmented regions-of-interest (ROIs) from 2454 mass lesions on mammograms were used to train a pre-trained DCNN on ImageNet. In the second stage transfer learning, the DCNN was used as a feature extractor followed by feature selection and random forest classification. The pathway evolution was performed using genetic algorithm in an iterative approach with tournament selection driven by count-preserving crossover and mutation. The second stage was trained with 9120 DBT ROIs from 228 mass lesions using leave-one-case-out cross-validation. The DCNN was reduced by 87% in the number of neurons, 34% in the number of parameters, and 95% in the number of multiply-and-add operations required in the convolutional layers. The test AUC on 89 mass lesions from 94 independent DBT cases before and after pruning were 0.88 and 0.90, respectively, and the difference was not statistically significant (p  >  0.05). The proposed DCNN compression approach can reduce the number of required operations by 95% while maintaining the classification performance. The approach can be extended to other deep neural networks and imaging tasks where transfer learning is appropriate.

Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

Science.gov (United States)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-05-01

Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, we propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in digital breast tomosynthesis (DBT). The objective is to prune the number of tunable parameters while preserving the classification accuracy. In the first stage transfer learning, 19 632 augmented regions-of-interest (ROIs) from 2454 mass lesions on mammograms were used to train a pre-trained DCNN on ImageNet. In the second stage transfer learning, the DCNN was used as a feature extractor followed by feature selection and random forest classification. The pathway evolution was performed using genetic algorithm in an iterative approach with tournament selection driven by count-preserving crossover and mutation. The second stage was trained with 9120 DBT ROIs from 228 mass lesions using leave-one-case-out cross-validation. The DCNN was reduced by 87% in the number of neurons, 34% in the number of parameters, and 95% in the number of multiply-and-add operations required in the convolutional layers. The test AUC on 89 mass lesions from 94 independent DBT cases before and after pruning were 0.88 and 0.90, respectively, and the difference was not statistically significant (p  >  0.05). The proposed DCNN compression approach can reduce the number of required operations by 95% while maintaining the classification performance. The approach can be extended to other deep neural networks and imaging tasks where transfer learning is appropriate.

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)

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

Science.gov (United States)

Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

2018-02-15

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. 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. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

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...

High-speed railway real-time localization auxiliary method based on deep neural network

Science.gov (United States)

Chen, Dongjie; Zhang, Wensheng; Yang, Yang

2017-11-01

High-speed railway intelligent monitoring and management system is composed of schedule integration, geographic information, location services, and data mining technology for integration of time and space data. Assistant localization is a significant submodule of the intelligent monitoring system. In practical application, the general access is to capture the image sequences of the components by using a high-definition camera, digital image processing technique and target detection, tracking and even behavior analysis method. In this paper, we present an end-to-end character recognition method based on a deep CNN network called YOLO-toc for high-speed railway pillar plate number. Different from other deep CNNs, YOLO-toc is an end-to-end multi-target detection framework, furthermore, it exhibits a state-of-art performance on real-time detection with a nearly 50fps achieved on GPU (GTX960). Finally, we realize a real-time but high-accuracy pillar plate number recognition system and integrate natural scene OCR into a dedicated classification YOLO-toc model.

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.

Revealing Holobiont Structure and Function of Three Red Sea Deep-Sea Corals

KAUST Repository

Yum, Lauren

2014-12-01

Deep-sea corals have long been regarded as cold-water coral; however a reevaluation of their habitat limitations has been suggested after the discovery of deep-sea coral in the Red Sea where temperatures exceed 20˚C. To gain further insight into the biology of deep-sea corals at these temperatures, the work in this PhD employed a holotranscriptomic approach, looking at coral animal host and bacterial symbiont gene expression in Dendrophyllia sp., Eguchipsammia fistula, and Rhizotrochus sp. sampled from the deep Red Sea. Bacterial community composition was analyzed via amplicon-based 16S surveys and cultured bacterial strains were subjected to bioprospecting in order to gauge the pharmaceutical potential of coralassociated microbes. Coral host transcriptome data suggest that coral can employ mitochondrial hypometabolism, anaerobic glycolysis, and surface cilia to enhance mass transport rates to manage the low oxygen and highly oligotrophic Red Sea waters. In the microbial community associated with these corals, ribokinases and retron-type reverse transcriptases are abundantly expressed. In its first application to deep-sea coral associated microbial communities, 16S-based next-generation sequencing found that a single operational taxonomic unit can comprise the majority of sequence reads and that a large number of low abundance populations are present, which cannot be visualized with first generation sequencing. Bioactivity testing of selected bacterial isolates was surveyed over 100 cytological parameters with high content screening, covering several major organelles and key proteins involved in a variety of signaling cascades. Some of these cytological profiles were similar to those of several reference pharmacologically active compounds, which suggest that the bacteria isolates produce compounds with similar mechanisms of action as the reference compounds. The sum of this work offers several mechanisms by which Red Sea deep-sea corals cope with environmental

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Science.gov (United States)

Maskey, Manil; Cecil, Dan; Ramachandran, Rahul; Miller, Jeffrey J.

2018-01-01

Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning

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

Stimulating the self: The influence of conceptual frameworks on reactions to deep brain stimulation

NARCIS (Netherlands)

Mecacci, G.; Haselager, W.F.G.

2014-01-01

Deep brain stimulation (DBS) is generally considered to have great practical potential. Yet along with its remarkable efficacy, which is currently being tested in application to many pathologies, come a certain number of complications. In particular, there seem to be several adverse psychological

Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers

NARCIS (Netherlands)

Kruithof, M.C.; Bouma, H.; Fischer, N.M.; Schutte, K.

2016-01-01

Object recognition is important to understand the content of video and allow flexible querying in a large number of cameras, especially for security applications. Recent benchmarks show that deep convolutional neural networks are excellent approaches for object recognition. This paper describes an

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....

Trace Fossil Evidence of Trematode-Bivalve Parasite-Host Interactions in Deep Time.

Science.gov (United States)

Huntley, John Warren; De Baets, Kenneth

2015-01-01

Parasitism is one of the most pervasive phenomena amongst modern eukaryotic life and yet, relative to other biotic interactions, almost nothing is known about its history in deep time. Digenean trematodes (Platyhelminthes) are complex life cycle parasites, which have practically no body fossil record, but induce the growth of characteristic malformations in the shells of their bivalve hosts. These malformations are readily preserved in the fossil record, but, until recently, have largely been overlooked by students of the fossil record. In this review, we present the various malformations induced by trematodes in bivalves, evaluate their distribution through deep time in the phylogenetic and ecological contexts of their bivalve hosts and explore how various taphonomic processes have likely biased our understanding of trematodes in deep time. Trematodes are known to negatively affect their bivalve hosts in a number of ways including castration, modifying growth rates, causing immobilization and, in some cases, altering host behaviour making the host more susceptible to their own predators. Digeneans are expected to be significant agents of natural selection. To that end, we discuss how bivalves may have adapted to their parasites via heterochrony and suggest a practical methodology for testing such hypotheses in deep time. Copyright © 2015 Elsevier Ltd. All rights reserved.

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...

Physiologically anaerobic microorganisms of the deep subsurface

International Nuclear Information System (INIS)

Stevens, S.E. Jr.; Chung, K.T.

1993-10-01

Anaerobic bacteria were isolated from deep subsurface sediment samples taken at study sites in Idaho (INEL) and Washington (HR) by culturing on dilute and concentrated medium. Morphologically distinct colonies were purified, and their responses to 21 selected physiological tests were determined. Although the number of isolates was small (18 INEL, 27 HR) some general patterns could be determined. Most strains could utilize all the carbon sources, however the glycerol and melizitose utilization was positive for 50% or less of the HR isolates. Catalase activity (27.78% at INEL, 74.07% at HR) and tryptophan metabolism (11.12% at INEL, 40.74% at HR) were significantly different between the two study sites. MPN and viable counts indicate that sediments near the water table yield the greatest numbers of anaerobes. Deeper sediments also appear to be more selective with the greatest number of viable counts on low-nutrient mediums. Likewise, only strictly obligate anaerobes were found in the deepest sediment samples. Selective media indicated the presence of methanogens, acetogens, and sulfate reducers at only the HR site

Deep Feature Consistent Variational Autoencoder

OpenAIRE

Hou, Xianxu; Shen, Linlin; Sun, Ke; Qiu, Guoping

2016-01-01

We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural net...

Clinical analysis of 28 patients with deep neck infection

International Nuclear Information System (INIS)

Takeda, Shoichiro; Kobayashi, Taisuke; Nakamura, Koshiro

2008-01-01

Although the number of patients with deep neck infection has been decreasing with the development of antibiotics, this condition sometimes shows a poor prognosis. Early diagnosis and appropriate treatment are important for good results. In this study, 28 patients (21 males, 7 females) with deep neck infection treated between April 1991 and March 2004 at Department of Otolaryngology, Ehime Prefectural Central Hospital were investigated retrospectively. Twenty-seven of those patients resulted in good recovery, while one patient died the day after admission because of disseminated intravascular coagulation (DIC). Surgical drainage was performed for 19 of 28 patients and tracheostomy or postoperative endotracheal intubation was performed in eight patients. The average interval between admission and surgery was 1.2 days, which was shorter than that in other previous reports. Early diagnosis using CT with enhancement and surgical drainage are important in order to prevent progression of abscess into the mediastinum, which is sometimes fatal. Tracheostomy or postoperative endotracheal intubation is necessary when upper aiway stenosis is occurs. (author)

Syllidae (Annelida: Phyllodocida) from the deep Mediterranean Sea, with the description of three new species.

Science.gov (United States)

Langeneck, Joachim; Musco, Luigi; Busoni, Giulio; Conese, Ilaria; Aliani, Stefano; Castelli, Alberto

2018-01-03

Despite almost two centuries of research, the diversity of Mediterranean deep-sea environments remain still largely unexplored. This is particularly true for the polychaete family Syllidae. We report herein 14 species; among them, we describe Erinaceusyllis barbarae n. sp., Exogone sophiae n. sp. and Prosphaerosyllis danovaroi n. sp. and report Parexogone wolfi San Martín, 1991, Exogone lopezi San Martín, Ceberio Aguirrezabalaga, 1996 and Anguillosyllis Day, 1963 for the first time from the Western Mediterranean, the latter based on a single individual likely belonging to an undescribed species. Moreover, we re-establish Syllis profunda Cognetti, 1955 based on type and new material. Present data, along with a critical analysis of available literature, show that Syllidae are highly diverse in deep Mediterranean environments, even though they are rarely reported, probably due to the scarce number of studies devoted to the size-fraction of benthos including deep-sea syllids. Most deep-sea Syllidae have wide distributions, which do not include shallow-waters. 100 m depth apparently represents the boundary between the assemblages dominated by generalist shallow water syllids like Exogone naidina Ørsted, 1843 and Syllis parapari San Martín López, 2000, and those deep-water assemblages characterised by strictly deep-water species like Parexogone campoyi San Martín, Ceberio Aguirrezabalaga, 1996, Parexogone wolfi San Martín, 1991 and Syllis sp. 1 (= Langerhansia caeca Katzmann, 1973).

Risk assessment under deep uncertainty: A methodological comparison

International Nuclear Information System (INIS)

Shortridge, Julie; Aven, Terje; Guikema, Seth

2017-01-01

Probabilistic Risk Assessment (PRA) has proven to be an invaluable tool for evaluating risks in complex engineered systems. However, there is increasing concern that PRA may not be adequate in situations with little underlying knowledge to support probabilistic representation of uncertainties. As analysts and policy makers turn their attention to deeply uncertain hazards such as climate change, a number of alternatives to traditional PRA have been proposed. This paper systematically compares three diverse approaches for risk analysis under deep uncertainty (qualitative uncertainty factors, probability bounds, and robust decision making) in terms of their representation of uncertain quantities, analytical output, and implications for risk management. A simple example problem is used to highlight differences in the way that each method relates to the traditional risk assessment process and fundamental issues associated with risk assessment and description. We find that the implications for decision making are not necessarily consistent between approaches, and that differences in the representation of uncertain quantities and analytical output suggest contexts in which each method may be most appropriate. Finally, each methodology demonstrates how risk assessment can inform decision making in deeply uncertain contexts, informing more effective responses to risk problems characterized by deep uncertainty. - Highlights: • We compare three diverse approaches to risk assessment under deep uncertainty. • A simple example problem highlights differences in analytical process and results. • Results demonstrate how methodological choices can impact risk assessment results.

Deep ice coring at Dome Fuji Station, Antarctica

Directory of Open Access Journals (Sweden)

Yoshiyuki Fujii

1999-03-01

Full Text Available Deep ice coring was carried out at Dome Fuji Station, Antarctica in 1995 and 1996 following a pilot borehole drilled and cased with FRP pipes in 1993,and reached 2503.52m in December 1996. Total numbers of ice coring runs below the pilot borehole and chip collection were 1369 and 837 respectively. The mean coring depths per run and per day were 1.75m and 8.21m respectively. We report the outline of the coring operation, the system, coring method, and troubles encountered during the coring work.

How Stressful Is "Deep Bubbling"?

Science.gov (United States)

Tyrmi, Jaana; Laukkanen, Anne-Maria

2017-03-01

Water resistance therapy by phonating through a tube into the water is used to treat dysphonia. Deep submersion (≥10 cm in water, "deep bubbling") is used for hypofunctional voice disorders. Using it with caution is recommended to avoid vocal overloading. This experimental study aimed to investigate how strenuous "deep bubbling" is. Fourteen subjects, half of them with voice training, repeated the syllable [pa:] in comfortable speaking pitch and loudness, loudly, and in strained voice. Thereafter, they phonated a vowel-like sound both in comfortable loudness and loudly into a glass resonance tube immersed 10 cm into the water. Oral pressure, contact quotient (CQ, calculated from electroglottographic signal), and sound pressure level were studied. The peak oral pressure P(oral) during [p] and shuttering of the outer end of the tube was measured to estimate the subglottic pressure P(sub) and the mean P(oral) during vowel portions to enable calculation of transglottic pressure P(trans). Sensations during phonation were reported with an open-ended interview. P(sub) and P(oral) were higher in "deep bubbling" and P(trans) lower than in loud syllable phonation, but the CQ did not differ significantly. Similar results were obtained for the comparison between loud "deep bubbling" and strained phonation, although P(sub) did not differ significantly. Most of the subjects reported "deep bubbling" to be stressful only for respiratory and lip muscles. No big differences were found between trained and untrained subjects. The CQ values suggest that "deep bubbling" may increase vocal fold loading. Further studies should address impact stress during water resistance exercises. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Evaluation of the DeepWind concept

DEFF Research Database (Denmark)

Schmidt Paulsen, Uwe; Borg, Michael; Gonzales Seabra, Luis Alberto

The report describes the DeepWind 5 MW conceptual design as a baseline for results obtained in the scientific and technical work packages of the DeepWind project. A comparison of DeepWi nd with existing VAWTs and paper projects are carried out and the evaluation of the concept in terms of cost...

Simulator Studies of the Deep Stall

Science.gov (United States)

White, Maurice D.; Cooper, George E.

1965-01-01

Simulator studies of the deep-stall problem encountered with modern airplanes are discussed. The results indicate that the basic deep-stall tendencies produced by aerodynamic characteristics are augmented by operational considerations. Because of control difficulties to be anticipated in the deep stall, it is desirable that adequate safeguards be provided against inadvertent penetrations.

Deep Learning

DEFF Research Database (Denmark)

Jensen, Morten Bornø; Bahnsen, Chris Holmberg; Nasrollahi, Kamal

2018-01-01

I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning.......I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning....

Deep Compaction Control of Sandy Soils

Directory of Open Access Journals (Sweden)

Bałachowski Lech

2015-02-01

Full Text Available Vibroflotation, vibratory compaction, micro-blasting or heavy tamping are typical improvement methods for the cohesionless deposits of high thickness. The complex mechanism of deep soil compaction is related to void ratio decrease with grain rearrangements, lateral stress increase, prestressing effect of certain number of load cycles, water pressure dissipation, aging and other effects. Calibration chamber based interpretation of CPTU/DMT can be used to take into account vertical and horizontal stress and void ratio effects. Some examples of interpretation of soundings in pre-treated and compacted sands are given. Some acceptance criteria for compaction control are discussed. The improvement factors are analysed including the normalised approach based on the soil behaviour type index.

Deep learning for plasma tomography using the bolometer system at JET

Energy Technology Data Exchange (ETDEWEB)

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

2017-01-15

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

Deep ensemble learning of sparse regression models for brain disease diagnosis.

Science.gov (United States)

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Deep learning for plasma tomography using the bolometer system at JET

International Nuclear Information System (INIS)

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

2017-01-01

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

Jetting from impact of a spherical drop with a deep layer

Science.gov (United States)

Zhang, Li; Toole, Jameson; Fazzaa, Kamel; Deegan, Robert; Deegan Group Team; X-Ray Science Division, Advanced Photon Source Collaboration

2011-11-01

We performed an experimental study of jets during the impact of a spherical drop with a deep layer of same liquid. Using high speed optical and X-ray imaging, we observe two types of jets: the so-called ejecta sheet which emerges almost immediately after impact and the lamella which emerges later. For high Reynolds number the two jets are distinct, while for low Reynolds number the two jets combine into a single continuous jet. We also measured the emergence time, speed, and position of the ejecta sheet and found simple scaling relations for these quantities.

Reproductive traits of tropical deep-water pandalid shrimps ( Heterocarpus ensifer) from the SW Gulf of Mexico

Science.gov (United States)

Briones-Fourzán, Patricia; Barradas-Ortíz, Cecilia; Negrete-Soto, Fernando; Lozano-Álvarez, Enrique

2010-08-01

Heterocarpus ensifer is a tropical deep-water pandalid shrimp whose reproductive features are poorly known. We examined reproductive traits of a population of H. ensifer inhabiting the continental slope (311-715 m in depth) off the Yucatan Peninsula, Mexico (SW Gulf of Mexico). Size range of the total sample ( n=816) was 10.4-38.9 mm carapace length. Females grow larger than males, but both sexes mature at 57% of their maximum theoretical size and at ˜30% of their total lifespan. Among adult females, the proportion of ovigerous females was high in all seasons, indicating year-round reproduction. Most females carrying embryos in advanced stages of development had ovaries in advanced stages of maturation, indicating production of successive spawns. In the autumn, however, the proportion of ovigerous females and the condition index of these females were lower compared to other seasons. This pattern potentially reflects a reduction in food resources following the summer minimum in particulate organic carbon flux to the deep benthos, as reported in previous studies. Spawns consisting of large numbers (16024±5644, mean±SD) of small eggs (0.045±0.009 mm 3) are consistent with extended planktotrophic larval development, an uncommon feature in deep-water carideans. Egg number increased as a power function of female size but with substantial variability, and egg size varied widely within and between females. There was no apparent trade-off between egg number and egg size and neither of these two variables was influenced by female condition. These results indicate iteroparity and a high and variable reproductive effort, reflecting a reproductive strategy developed to compensate for high larval mortality. The present study provides a baseline to compare reproductive traits between Atlantic populations of this tropical deep-water pandalid.

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

Science.gov (United States)

Wachinger, Christian; Reuter, Martin; Klein, Tassilo

2018-04-15

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

Deep Learning and Its Applications in Biomedicine.

Science.gov (United States)

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

2018-02-01

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

Robotic Detection of Marine Litter Using Deep Visual Detection Models

OpenAIRE

Fulton, Michael; Hong, Jungseok; Islam, Md Jahidul; Sattar, Junaed

2018-01-01

Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs) could very well contribute to the solution of this problem by finding and eventually removing trash. A step towards this goal is the successful detection of trash in underwater environments. This paper evaluates a number of deep-learning algorithms to the task of visually detecting trash in realistic underwater envi...

Deep Water Acoustics

Science.gov (United States)

2016-06-28

the Deep Water project and participate in the NPAL Workshops, including Art Baggeroer (MIT), J. Beron- Vera (UMiami), M. Brown (UMiami), T...Kathleen E . Wage. The North Pacific Acoustic Laboratory deep-water acoustic propagation experiments in the Philippine Sea. J. Acoust. Soc. Am., 134(4...estimate of the angle α during PhilSea09, made from ADCP measurements at the site of the DVLA. Sim. A B1 B2 B3 C D E F Prof. # 0 4 4 4 5 10 16 20 α

Multiple huge epiphrenic esophageal diverticula with motility disease treated with video-assisted thoracoscopic and hand-assisted laparoscopic esophagectomy: a case report.

Science.gov (United States)

Taniguchi, Yoshiki; Takahashi, Tsuyoshi; Nakajima, Kiyokazu; Higashi, Shigeyoshi; Tanaka, Koji; Miyazaki, Yasuhiro; Makino, Tomoki; Kurokawa, Yukinori; Yamasaki, Makoto; Takiguchi, Shuji; Mori, Masaki; Doki, Yuichiro

2017-12-01

Epiphrenic esophageal diverticulum is a rare condition that is often associated with a concomitant esophageal motor disorder. Some patients have the chief complaints of swallowing difficulty and gastroesophageal reflux; traditionally, such diverticula have been resected via right thoracotomy. Here, we describe a case with huge multiple epiphrenic diverticula with motility disorder, which were successfully resected using a video-assisted thoracic and laparoscopic procedure. A 63-year-old man was admitted due to dysphagia, heartburn, and vomiting. An esophagogram demonstrated an S-shaped lower esophagus with multiple epiphrenic diverticula (75 × 55 mm and 30 × 30 mm) and obstruction by the lower esophageal sphincter (LES). Esophageal manometry showed normal peristaltic contractions in the esophageal body, whereas the LES pressure was high (98.6 mmHg). The pressure vector volume of LES was 23,972 mmHg 2  cm. Based on these findings, we diagnosed huge multiple epiphrenic diverticula with a hypertensive lower esophageal sphincter and judged that resection might be required. We performed lower esophagectomy with gastric conduit reconstruction using a video-assisted thoracic and hand-assisted laparoscopic procedure. The postoperative course was uneventful, and the esophagogram demonstrated good passage, with no leakage, stenosis, or diverticula. The most common causes of mid-esophageal and epiphrenic diverticula are motility disorders of the esophageal body; appropriate treatment should be considered based on the morphological and motility findings.

Relations between Goals, Self-Efficacy, Critical Thinking and Deep Processing Strategies: A Path Analysis

Science.gov (United States)

Phan, Huy Phuong

2009-01-01

Research exploring students' academic learning has recently amalgamated different motivational theories within one conceptual framework. The inclusion of achievement goals, self-efficacy, deep processing and critical thinking has been cited in a number of studies. This article discusses two empirical studies that examined these four theoretical…

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.

Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

Science.gov (United States)

Cabrera-Vives, Guillermo; Reyes, Ignacio; Förster, Francisco; Estévez, Pablo A.; Maureira, Juan-Carlos

2017-02-01

We introduce Deep-HiTS, a rotation-invariant convolutional neural network (CNN) model for classifying images of transient candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RFs). We show that our CNN significantly outperforms the RF model, reducing the error by almost half. Furthermore, for a fixed number of approximately 2000 allowed false transient candidates per night, we are able to reduce the misclassified real transients by approximately one-fifth. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope. We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS. Deep-HiTS is licensed under the terms of the GNU General Public License v3.0.

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

Science.gov (United States)

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

2008-12-01

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

Huge Inverse Magnetization Generated by Faraday Induction in Nano-Sized Au@Ni Core@Shell Nanoparticles

Directory of Open Access Journals (Sweden)

Chen-Chen Kuo

2015-08-01

Full Text Available We report on the design and observation of huge inverse magnetizations pointing in the direction opposite to the applied magnetic field, induced in nano-sized amorphous Ni shells deposited on crystalline Au nanoparticles by turning the applied magnetic field off. The magnitude of the induced inverse magnetization is very sensitive to the field reduction rate as well as to the thermal and field processes before turning the magnetic field off, and can be as high as 54% of the magnetization prior to cutting off the applied magnetic field. Memory effect of the induced inverse magnetization is clearly revealed in the relaxation measurements. The relaxation of the inverse magnetization can be described by an exponential decay profile, with a critical exponent that can be effectively tuned by the wait time right after reaching the designated temperature and before the applied magnetic field is turned off. The key to these effects is to have the induced eddy current running beneath the amorphous Ni shells through Faraday induction.

WFIRST: Science from Deep Field Surveys

Science.gov (United States)

Koekemoer, Anton; Foley, Ryan; WFIRST Deep Field Working Group

2018-01-01

WFIRST will enable deep field imaging across much larger areas than those previously obtained with Hubble, opening up completely new areas of parameter space for extragalactic deep fields including cosmology, supernova and galaxy evolution science. The instantaneous field of view of the Wide Field Instrument (WFI) is about 0.3 square degrees, which would for example yield an Ultra Deep Field (UDF) reaching similar depths at visible and near-infrared wavelengths to that obtained with Hubble, over an area about 100-200 times larger, for a comparable investment in time. Moreover, wider fields on scales of 10-20 square degrees could achieve depths comparable to large HST surveys at medium depths such as GOODS and CANDELS, and would enable multi-epoch supernova science that could be matched in area to LSST Deep Drilling fields or other large survey areas. Such fields may benefit from being placed on locations in the sky that have ancillary multi-band imaging or spectroscopy from other facilities, from the ground or in space. The WFIRST Deep Fields Working Group has been examining the science considerations for various types of deep fields that may be obtained with WFIRST, and present here a summary of the various properties of different locations in the sky that may be considered for future deep fields with WFIRST.

Deep video deblurring

KAUST Repository

Su, Shuochen

2016-11-25

Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on aligning nearby frames. However, aligning images is a computationally expensive and fragile procedure, and methods that aggregate information must therefore be able to identify which regions have been accurately aligned and which have not, a task which requires high level scene understanding. In this work, we introduce a deep learning solution to video deblurring, where a CNN is trained end-to-end to learn how to accumulate information across frames. To train this network, we collected a dataset of real videos recorded with a high framerate camera, which we use to generate synthetic motion blur for supervision. We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.

Theoretical description of laser melt pool dynamics, Task order number B239634, Quarter 3 report

Energy Technology Data Exchange (ETDEWEB)

Dykhne, A.

1995-05-10

Melting of solid matter under laser radiation is realized in almost every process of laser technology. The present paper addresses melted material flows in cases when melt zones are shallow, i.e., the zone width is appreciably greater than or of the same order as its depth. Such conditions are usually realized when hardening, doping or perforating thin plates or when using none-deep penetration. Melted material flowing under conditions of deep penetration, drilling of deep openings and cutting depends on a number of additional factors (as compared to the shallow-pool case), namely, formation of a vapor and gas cavern in the sample and propagation of the laser beam through the cavern. These extra circumstances complicate hydrodynamic consideration of the liquid bath and will be addressed is the paper to follow.

TOPIC MODELING: CLUSTERING OF DEEP WEBPAGES

OpenAIRE

Muhunthaadithya C; Rohit J.V; Sadhana Kesavan; E. Sivasankar

2015-01-01

The internet is comprised of massive amount of information in the form of zillions of web pages.This information can be categorized into the surface web and the deep web. The existing search engines can effectively make use of surface web information.But the deep web remains unexploited yet. Machine learning techniques have been commonly employed to access deep web content.

DeepSimulator: a deep simulator for Nanopore sequencing

KAUST Repository

Li, Yu; Han, Renmin; Bi, Chongwei; Li, Mo; Wang, Sheng; Gao, Xin

2017-01-01

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

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...

[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.

Lepton-number-charged scalars and neutrino beamstrahlung

Science.gov (United States)

Berryman, Jeffrey M.; de Gouvêa, André; Kelly, Kevin J.; Zhang, Yue

2018-04-01

Experimentally, baryon number minus lepton number, B -L , appears to be a good global symmetry of nature. We explore the consequences of the existence of gauge-singlet scalar fields charged under B -L -dubbed lepton-number-charged scalars (LeNCSs)—and postulate that these couple to the standard model degrees of freedom in such a way that B -L is conserved even at the nonrenormalizable level. In this framework, neutrinos are Dirac fermions. Including only the lowest mass-dimension effective operators, some of the LeNCSs couple predominantly to neutrinos and may be produced in terrestrial neutrino experiments. We examine several existing constraints from particle physics, astrophysics, and cosmology to the existence of a LeNCS carrying B -L charge equal to two, and discuss the emission of LeNCSs via "neutrino beamstrahlung," which occurs every once in a while when neutrinos scatter off of ordinary matter. We identify regions of the parameter space where existing and future neutrino experiments, including the Deep Underground Neutrino Experiment, are at the frontier of searches for such new phenomena.

DEEPre: sequence-based enzyme EC number prediction by deep learning

KAUST Repository

Li, Yu

2017-10-20

Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manuallycrafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre\\'s ability to capture the functional difference of enzyme isoforms.The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre.

DEEPre: sequence-based enzyme EC number prediction by deep learning

KAUST Repository

Li, Yu; Wang, Sheng; Umarov, Ramzan; Xie, Bingqing; Fan, Ming; Li, Lihua; Gao, Xin

2017-01-01

Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manuallycrafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre's ability to capture the functional difference of enzyme isoforms.The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre.

Deep wells integrated with microfluidic valves for stable docking and storage of cells.

Science.gov (United States)

Jang, Yun-Ho; Kwon, Cheong Hoon; Kim, Sang Bok; Selimović, Seila; Sim, Woo Young; Bae, Hojae; Khademhosseini, Ali

2011-02-01

In this paper, we describe a microfluidic mechanism that combines microfluidic valves and deep wells for cell localization and storage. Cells are first introduced into the device via externally controlled flow. Activating on-chip valves was used to interrupt the flow and to sediment the cells floating above the wells. Thus, valves could be used to localize the cells in the desired locations. We quantified the effect of valves in the cell storage process by comparing the total number of cells stored with and without valve activation. We hypothesized that in deep wells external flows generate low shear stress regions that enable stable, long-term docking of cells. To assess this hypothesis we conducted numerical calculations to understand the influence of well depth on the forces acting on cells. We verified those predictions experimentally by comparing the fraction of stored cells as a function of the well depth and input flow rate upon activation of the valves. As expected, upon reintroduction of the flow the cells in the deep wells were not moved whereas those in shallow wells were washed away. Taken together, our paper demonstrates that deep wells and valves can be combined to enable a broad range of cell studies. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

[Observation on therapeutic effect of electroacupuncture at Tianshu (ST 25) with deep needling technique on functional constipation].

Science.gov (United States)

Wang, Cheng-Wei; He, Hong-Bo; Li, Ning; Wen, Qian; Liu, Zhi-Shun

2010-09-01

To probe into a better therapeutic method for functional constipation. Ninety-five cases of functional constipation were randomly divided into deep puncture at ST 25 group (48 cases), shallow puncture at ST 25 group (24 cases) and medication group (23 cases). In deep puncture at ST 25 group, Tianshu (ST 25) was punctured deeply to the peritoneum, with electric stimulation. In shallow puncture at ST 25 group, Tianshu (ST 25) was punctured shallowly, 5 mm beneath the skin, with electric stimulation. In medication group, Duphalac was administered orally. These cases were treated continuously for 4 weeks in 3 groups and followed up for 6 months. It was to observe the numbers of person who had defecation 4 times a week, difference in weekly defecation frequency and the difference in the Cleveland Clinic Score (CCS). In deep puncture at ST 25 group, the frequency of weekly defecation and the numbers of person who had defecation 4 times a week increased and CCS decreased, which were similar to the efficacy in shallow puncture at ST 25 group (all P > 0.05). But the efficacy of both ST 25 groups was superior to that in medication group (both P deep puncture at ST 25 group acted more quickly than either shallow puncture at ST 25 group or medication group and its efficacy remained much longer. The deep puncture at ST 25 with electric stimulation presents similar efficacy on functional constipation as shallow puncture at ST 25, but it acts more quickly than shallow puncture at ST 25, both of them are more advantageous than medication and the long-term efficacy is better.

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.

The deep ocean under climate change

Science.gov (United States)

Levin, Lisa A.; Le Bris, Nadine

2015-11-01

The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems.

Bacterial niche-specific genome expansion is coupled with highly frequent gene disruptions in deep-sea sediments

KAUST Repository

Wang, Yong; Yang, Jiang Ke; Lee, On On; Li, Tie Gang; Al-Suwailem, Abdulaziz M.; Danchin, Antoine; Qian, Pei-Yuan

2011-01-01

The complexity and dynamics of microbial metagenomes may be evaluated by genome size, gene duplication and the disruption rate between lineages. In this study, we pyrosequenced the metagenomes of microbes obtained from the brine and sediment of a deep-sea brine pool in the Red Sea to explore the possible genomic adaptations of the microbes in response to environmental changes. The microbes from the brine and sediments (both surface and deep layers) of the Atlantis II Deep brine pool had similar communities whereas the effective genome size varied from 7.4 Mb in the brine to more than 9 Mb in the sediment. This genome expansion in the sediment samples was due to gene duplication as evidenced by enrichment of the homologs. The duplicated genes were highly disrupted, on average by 47.6% and 70% for the surface and deep layers of the Atlantis II Deep sediment samples, respectively. The disruptive effects appeared to be mainly due to point mutations and frameshifts. In contrast, the homologs from the Atlantis II Deep brine sample were highly conserved and they maintained relatively small copy numbers. Likely, the adaptation of the microbes in the sediments was coupled with pseudogenizations and possibly functional diversifications of the paralogs in the expanded genomes. The maintenance of the pseudogenes in the large genomes is discussed. © 2011 Wang et al.

Bacterial niche-specific genome expansion is coupled with highly frequent gene disruptions in deep-sea sediments

KAUST Repository

Wang, Yong

2011-12-21

The complexity and dynamics of microbial metagenomes may be evaluated by genome size, gene duplication and the disruption rate between lineages. In this study, we pyrosequenced the metagenomes of microbes obtained from the brine and sediment of a deep-sea brine pool in the Red Sea to explore the possible genomic adaptations of the microbes in response to environmental changes. The microbes from the brine and sediments (both surface and deep layers) of the Atlantis II Deep brine pool had similar communities whereas the effective genome size varied from 7.4 Mb in the brine to more than 9 Mb in the sediment. This genome expansion in the sediment samples was due to gene duplication as evidenced by enrichment of the homologs. The duplicated genes were highly disrupted, on average by 47.6% and 70% for the surface and deep layers of the Atlantis II Deep sediment samples, respectively. The disruptive effects appeared to be mainly due to point mutations and frameshifts. In contrast, the homologs from the Atlantis II Deep brine sample were highly conserved and they maintained relatively small copy numbers. Likely, the adaptation of the microbes in the sediments was coupled with pseudogenizations and possibly functional diversifications of the paralogs in the expanded genomes. The maintenance of the pseudogenes in the large genomes is discussed. © 2011 Wang et al.

Bacterial niche-specific genome expansion is coupled with highly frequent gene disruptions in deep-sea sediments.

Directory of Open Access Journals (Sweden)

Yong Wang

Full Text Available The complexity and dynamics of microbial metagenomes may be evaluated by genome size, gene duplication and the disruption rate between lineages. In this study, we pyrosequenced the metagenomes of microbes obtained from the brine and sediment of a deep-sea brine pool in the Red Sea to explore the possible genomic adaptations of the microbes in response to environmental changes. The microbes from the brine and sediments (both surface and deep layers of the Atlantis II Deep brine pool had similar communities whereas the effective genome size varied from 7.4 Mb in the brine to more than 9 Mb in the sediment. This genome expansion in the sediment samples was due to gene duplication as evidenced by enrichment of the homologs. The duplicated genes were highly disrupted, on average by 47.6% and 70% for the surface and deep layers of the Atlantis II Deep sediment samples, respectively. The disruptive effects appeared to be mainly due to point mutations and frameshifts. In contrast, the homologs from the Atlantis II Deep brine sample were highly conserved and they maintained relatively small copy numbers. Likely, the adaptation of the microbes in the sediments was coupled with pseudogenizations and possibly functional diversifications of the paralogs in the expanded genomes. The maintenance of the pseudogenes in the large genomes is discussed.

SNL Document number 16-5277, 2/3/82 and Amendment number 02, 2/18/83

International Nuclear Information System (INIS)

Feller, R.J.

1985-01-01

This report summarizes results of studies carried out as part of the Low-Level Marine Disposal feasibility assessment. The studies are designed to: establish whether broadly reactive antibodies from shallow-water organisms will react with and recognize similar organisms from the deep sea; prepare antibodies to deep-sea organism for which no shallow-water antibody exists; and identify trophic connections among deep-sea organisms by means of immunological analysis. 3 figures, 8 tables

High-level radioactive waste disposal in the deep ocean

International Nuclear Information System (INIS)

Hill, H.W.

1977-01-01

A joint programme has begun between the Fisheries Laboratory, Lowestoft and the Institute of Oceanographic Sciences, Wormley to study the dispersion of radioactivity in the deep ocean arising from the possible dumping of high level waste on the sea bed in vitrified-glass form which would permit slow leakage over a long term scale. The programme consists firstly of the development of a simple diffusion/advection model for the dispersion of radioactivity in a closed and finite ocean, which overcomes many of the criticisms of the earlier model proposed by Webb and Morley. Preliminary results from this new model are comparable to those of the Webb-Morley model for radio isotopes with half-lives of 10-300 years but are considerably more restrictive outside this range, particularly for those which are much longer-lived. The second part of the programme, towards which the emphasis is directed, concerns the field programme planned to measure the advection and diffusion parameters in the deeper layers of the ocean to provide realistic input parameters to the model and increase our fundamental understanding of the environment in which the radioactive materials may be released. The first cruises of the programme will take place in late 1976 and involve deep current meter deployments and float dispersion experiments around the present NEA dump site with some sediment sampling, so that adsorption experiments can be started on typical deep sea sediments. The programme will expand the number of long-term deep moored stations over the next five years and include further float experiments, CTD profiling, and other physical oceanography. In the second half of the 5-year programme, attempts will be made to measure diffusion parameters in the deeper layers of the ocean using radioactive tracers

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

Are deep strategic learners better suited to PBL? A preliminary study.

Science.gov (United States)

Papinczak, Tracey

2009-08-01

The aim of this study was to determine if medical students categorized as having deep and strategic approaches to their learning find problem-based learning (PBL) enjoyable and supportive of their learning, and achieve well in the first-year course. Quantitative and qualitative data were gathered from first-year medical students (N = 213). All students completed the Medical Course Learning Questionnaire at the commencement and completion of their first year of medical studies. The instrument measured a number of different aspects of learning, including approaches to learning, preferences for different learning environments, self-efficacy, and perceptions of learning within PBL tutorials. Qualitative data were collected from written responses to open questions. Results of students' performance on two forms of examinations were obtained for those giving permission (N = 68). Two-step cluster analysis of the cohort's responses to questions about their learning approaches identified five clusters, three of which represented coherent combinations of learning approaches (deep, deep and strategic, and surface apathetic) and two clusters which had unusual or dissonant combinations. Deep, strategic learners represented 25.8% of the cohort. They were more efficacious, preferred learning environments which support development of understanding and achieved significantly higher scores on the written examination. Strongly positive comments about learning in PBL tutorials were principally described by members of this cluster. This preliminary study employed a technique to categorize a student cohort into subgroups on the basis of their approaches to learning. One, the deep and strategic learners, appeared to be less vulnerable to the stresses of PBS in a medical course. While variation between individual learners will always be considerable, this analysis has enabled classification of a student group that may be less likely to find PBL problematic. Implications for practice and

Deep smarts.

Science.gov (United States)

Leonard, Dorothy; Swap, Walter

2004-09-01

When a person sizes up a complex situation and rapidly comes to a decision that proves to be not just good but brilliant, you think, "That was smart." After you watch him do this a few times, you realize you're in the presence of something special. It's not raw brainpower, though that helps. It's not emotional intelligence, either, though that, too, is often involved. It's deep smarts. Deep smarts are not philosophical--they're not"wisdom" in that sense, but they're as close to wisdom as business gets. You see them in the manager who understands when and how to move into a new international market, in the executive who knows just what kind of talk to give when her organization is in crisis, in the technician who can track a product failure back to an interaction between independently produced elements. These are people whose knowledge would be hard to purchase on the open market. Their insight is based on know-how more than on know-what; it comprises a system view as well as expertise in individual areas. Because deep smarts are experienced based and often context specific, they can't be produced overnight or readily imported into an organization. It takes years for an individual to develop them--and no time at all for an organization to lose them when a valued veteran walks out the door. They can be taught, however, with the right techniques. Drawing on their forthcoming book Deep Smarts, Dorothy Leonard and Walter Swap say the best way to transfer such expertise to novices--and, on a larger scale, to make individual knowledge institutional--isn't through PowerPoint slides, a Web site of best practices, online training, project reports, or lectures. Rather, the sage needs to teach the neophyte individually how to draw wisdom from experience. Companies have to be willing to dedicate time and effort to such extensive training, but the investment more than pays for itself.

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

Science.gov (United States)

Sadeghi, Zahra

2016-09-01

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

Statistical properties of deep inelastic reactions

International Nuclear Information System (INIS)

Moretto, L.G.

1983-08-01

The multifaceted aspects of deep-inelastic heavy-ion collisions are discussed in terms of the statistical equilibrium limit. It is shown that a conditional statistical equilibrium, where a number of degrees of freedom are thermalized while others are still relaxing, prevails in most of these reactions. The individual degrees of freedom that have been explored experimentally are considered in their statistical equilibrium limit, and the extent to which they appear to be thermalized is discussed. The interaction between degrees of freedom on their way towards equilibrium is shown to create complex feedback phenomena that may lead to self-regulation. A possible example of self-regulation is shown for the process of energy partition between fragments promoted by particle exchange. 35 references

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.

Determination of deep-level impurities and their effects on the small-single and LF noise properties of ion-implanted GaAs MESFETs

International Nuclear Information System (INIS)

Sriram, S.; Kim, B.; Ghosh, P.K.; Das, M.B.; Pennsylvania State Univ., University Park; Pennsylvania State Univ., University Park

1982-01-01

A large number of deep levels, with energies ranging from Esub(c)-0.19eV to Esub(c)-0.9eV, have been identified and characterized using ion-implanted MESFET's on undoped and Cr-doped LEC-grown semi-insulating GaAs substrates. Measurement techniques used include deep level transient (DLTS) and steady state spectroscopic (DLSS) methods. Large capture cross-section values are obtained for levels below Esub(c)-0.5eV, possibly due to high electric field. Spectral densities of LF noise with distinct bulges have been shown to be related to deep levels. In some samples, natural deep level related oscillations have been observed and their ionization energies have been determined. (author)

Origin and biogeography of the deep-water Mediterranean Hydromedusae including the description of two new species collected in submarine canyons of Northwestern Mediterranean

Directory of Open Access Journals (Sweden)

J. M. Gili

1998-06-01

Full Text Available Two new species of hydromedusae (Foersteria antoniae and Cunina simplex are described from plankton collected in sediment traps placed in the Lacaze-Duthiers Submarine Canyon and along Banyuls-sur-Mer coast (northwestern Mediterranean. The Mediterranean hydromedusan deep-water fauna contains 41 species which represent 45.5 % of the world-wide deep-sea hydromedusae fauna (90 and 20% of the total number of Mediterranean hydromedusae (204. The Mediterranean deep-water hydromedusan fauna is characterised by a large percentage of holoplanktonic species (61%, mainly Trachymedusae. Nevertheless, contrary to the general opinion, the percentage of meroplanktonic species is equally high. The most original features of this fauna lies however in the importance of the number of endemic species (22% and in the fact that the majority of them are meroplanktonic Leptomedusae with a supposed bathybenthic stage. Some of the endemic species could still represent relics of the primitive Tethys fauna having survived to the Messinian crisis. The origin of the Mediterranean deep-water hydromedusan fauna is discussed and a general hypothesis is proposed.

The deep ocean under climate change.

Science.gov (United States)

Levin, Lisa A; Le Bris, Nadine

2015-11-13

The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems. Copyright © 2015, American Association for the Advancement of Science.

Analysis of Low-Biomass Microbial Communities in the Deep Biosphere.

Science.gov (United States)

Morono, Y; Inagaki, F

2016-01-01

Over the past few decades, the subseafloor biosphere has been explored by scientific ocean drilling to depths of about 2.5km below the seafloor. Although organic-rich anaerobic sedimentary habitats in the ocean margins harbor large numbers of microbial cells, microbial populations in ultraoligotrophic aerobic sedimentary habitats in the open ocean gyres are several orders of magnitude less abundant. Despite advances in cultivation-independent molecular ecological techniques, exploring the low-biomass environment remains technologically challenging, especially in the deep subseafloor biosphere. Reviewing the historical background of deep-biosphere analytical methods, the importance of obtaining clean samples and tracing contamination, as well as methods for detecting microbial life, technological aspects of molecular microbiology, and detecting subseafloor metabolic activity will be discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

SEDS: THE SPITZER EXTENDED DEEP SURVEY. SURVEY DESIGN, PHOTOMETRY, AND DEEP IRAC SOURCE COUNTS

International Nuclear Information System (INIS)

Ashby, M. L. N.; Willner, S. P.; Fazio, G. G.; Huang, J.-S.; Hernquist, L.; Hora, J. L.; Arendt, R.; Barmby, P.; Barro, G.; Faber, S.; Guhathakurta, P.; Bell, E. F.; Bouwens, R.; Cattaneo, A.; Croton, D.; Davé, R.; Dunlop, J. S.; Egami, E.; Finlator, K.; Grogin, N. A.

2013-01-01

The Spitzer Extended Deep Survey (SEDS) is a very deep infrared survey within five well-known extragalactic science fields: the UKIDSS Ultra-Deep Survey, the Extended Chandra Deep Field South, COSMOS, the Hubble Deep Field North, and the Extended Groth Strip. SEDS covers a total area of 1.46 deg 2 to a depth of 26 AB mag (3σ) in both of the warm Infrared Array Camera (IRAC) bands at 3.6 and 4.5 μm. Because of its uniform depth of coverage in so many widely-separated fields, SEDS is subject to roughly 25% smaller errors due to cosmic variance than a single-field survey of the same size. SEDS was designed to detect and characterize galaxies from intermediate to high redshifts (z = 2-7) with a built-in means of assessing the impact of cosmic variance on the individual fields. Because the full SEDS depth was accumulated in at least three separate visits to each field, typically with six-month intervals between visits, SEDS also furnishes an opportunity to assess the infrared variability of faint objects. This paper describes the SEDS survey design, processing, and publicly-available data products. Deep IRAC counts for the more than 300,000 galaxies detected by SEDS are consistent with models based on known galaxy populations. Discrete IRAC sources contribute 5.6 ± 1.0 and 4.4 ± 0.8 nW m –2 sr –1 at 3.6 and 4.5 μm to the diffuse cosmic infrared background (CIB). IRAC sources cannot contribute more than half of the total CIB flux estimated from DIRBE data. Barring an unexpected error in the DIRBE flux estimates, half the CIB flux must therefore come from a diffuse component.

The deep lymphatic anatomy of the hand.

Science.gov (United States)

Ma, Chuan-Xiang; Pan, Wei-Ren; Liu, Zhi-An; Zeng, Fan-Qiang; Qiu, Zhi-Qiang

2018-04-03

The deep lymphatic anatomy of the hand still remains the least described in medical literature. Eight hands were harvested from four nonembalmed human cadavers amputated above the wrist. A small amount of 6% hydrogen peroxide was employed to detect the lymphatic vessels around the superficial and deep palmar vascular arches, in webs from the index to little fingers, the thenar and hypothenar areas. A 30-gauge needle was inserted into the vessels and injected with a barium sulphate compound. Each specimen was dissected, photographed and radiographed to demonstrate deep lymphatic distribution of the hand. Five groups of deep collecting lymph vessels were found in the hand: superficial palmar arch lymph vessel (SPALV); deep palmar arch lymph vessel (DPALV); thenar lymph vessel (TLV); hypothenar lymph vessel (HTLV); deep finger web lymph vessel (DFWLV). Each group of vessels drained in different directions first, then all turned and ran towards the wrist in different layers. The deep lymphatic drainage of the hand has been presented. The results will provide an anatomical basis for clinical management, educational reference and scientific research. Copyright © 2018 Elsevier GmbH. All rights reserved.

Energy Release Through Internal Wave Breaking

NARCIS (Netherlands)

van Haren, H.; Gostiaux, L.

2012-01-01

The sun inputs huge amounts of heat to the ocean, heat that would stay near the ocean's surface if it were not mechanically mixed into the deep. Warm water is less dense than cold water, so that heated surface waters "float" on top of the cold deep waters. Only active mechanical turbulent mixing can

Cutting the Umbilical: New Technological Perspectives in Benthic Deep-Sea Research

Directory of Open Access Journals (Sweden)

Angelika Brandt

2016-05-01

Full Text Available Many countries are very active in marine research and operate their own research fleets. In this decade, a number of research vessels have been renewed and equipped with the most modern navigation systems and tools. However, much of the research gear used for biological sampling, especially in the deep-sea, is outdated and dependent on wired operations. The deployment of gear can be very time consuming and, thus, expensive. The present paper reviews wire-dependent, as well as autonomous research gear for biological sampling at the deep seafloor. We describe the requirements that new gear could fulfil, including the improvement of spatial and temporal sampling resolution, increased autonomy, more efficient sample conservation methodologies for morphological and molecular studies and the potential for extensive in situ real-time studies. We present applicable technologies from robotics research, which could be used to develop novel autonomous marine research gear, which may be deployed independently and/or simultaneously with traditional wired equipment. A variety of technological advancements make such ventures feasible and timely. In proportion to the running costs of modern research vessels, the development of such autonomous devices might be already paid off after a discrete number of pioneer expeditions.

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

Science.gov (United States)

Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

2018-02-08

Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

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.

Deep inelastic electron and muon scattering

International Nuclear Information System (INIS)

Taylor, R.E.

1975-07-01

From the review of deep inelastic electron and muon scattering it is concluded that the puzzle of deep inelastic scattering versus annihilation was replaced with the challenge of the new particles, that the evidence for the simplest quark-algebra models of deep inelastic processes is weaker than a year ago. Definite evidence of scale breaking was found but the specific form of that scale breaking is difficult to extract from the data. 59 references

Fast, Distributed Algorithms in Deep Networks

Science.gov (United States)

2016-05-11

shallow networks, additional work will need to be done in order to allow for the application of ADMM to deep nets. The ADMM method allows for quick...Quock V Le, et al. Large scale distributed deep networks. In Advances in Neural Information Processing Systems, pages 1223–1231, 2012. [11] Ken-Ichi...A TRIDENT SCHOLAR PROJECT REPORT NO. 446 Fast, Distributed Algorithms in Deep Networks by Midshipman 1/C Ryan J. Burmeister, USN

Deep Learning from Crowds

DEFF Research Database (Denmark)

Rodrigues, Filipe; Pereira, Francisco Camara

Over the last few years, deep learning has revolutionized the field of machine learning by dramatically improving the stateof-the-art in various domains. However, as the size of supervised artificial neural networks grows, typically so does the need for larger labeled datasets. Recently...... networks from crowds. We begin by describing an EM algorithm for jointly learning the parameters of the network and the reliabilities of the annotators. Then, a novel general-purpose crowd layer is proposed, which allows us to train deep neural networks end-to-end, directly from the noisy labels......, crowdsourcing has established itself as an efficient and cost-effective solution for labeling large sets of data in a scalable manner, but it often requires aggregating labels from multiple noisy contributors with different levels of expertise. In this paper, we address the problem of learning deep neural...

Risk factors are different for deep and lobar remote hemorrhages after intravenous thrombolysis.

Directory of Open Access Journals (Sweden)

Luis Prats-Sanchez

Full Text Available Remote parenchymal haemorrhage (rPH after intravenous thrombolysis is defined as hemorrhages that appear in brain regions without visible ischemic damage, remote from the area of ischemia causing the initial stroke symptom. The pathophysiology of rPH is not clear and may be explained by different underlying mechanisms. We hypothesized that rPH may have different risk factors according to the bleeding location. We report the variables that we found associated with deep and lobar rPH after intravenous thrombolysis.This is a descriptive study of patients with ischemic stroke who were treated with intravenous thrombolysis. These patients were included in a multicenter prospective registry. We collected demographic, clinical and radiological data. We evaluated the number and distribution of cerebral microbleeds (CMB from Magnetic Resonance Imaging. We excluded patients treated endovascularly, patients with parenchymal hemorrhage without concomitant rPH and stroke mimics. We compared the variables from patients with deep or lobar rPH with those with no intracranial hemorrhage.We studied 934 patients (mean age 73.9±12.6 years and 52.8% were men. We observed rPH in 34 patients (3.6%; 9 (0.9% were deep and 25 (2.7% lobar. No hemorrhage was observed in 900 (96.6% patients. Deep rPH were associated with hypertensive episodes within first 24 hours after intravenous thrombolysis (77.7% vs 23.3%, p1 CMB (30.7% vs 4.4%, p = 0.003, lobar CMB (53.8% vs 3.0%, p<0.001 and severe leukoaraiosis (76.9% vs 42%, p = 0.02.A high blood pressure within the first 24 hours after intravenous thrombolysis is associated with deep rPH, whereas lobar rPH are associated with imaging markers of amyloid deposition. Thus, our results suggest that deep and lobar rPH after intravenous thrombolysis may have different mechanisms.

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

DEFF Research Database (Denmark)

Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

2018-01-01

conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

DEFF Research Database (Denmark)

Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

2018-01-01

This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditi...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....

Deep Learning in Gastrointestinal Endoscopy.

Science.gov (United States)

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.

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...

The Nature of Thinking, Shallow and Deep

Directory of Open Access Journals (Sweden)

Gary L. Brase

2014-05-01

Full Text Available Because the criteria for success differ across various domains of life, no single normative standard will ever work for all types of thinking. One method for dealing with this apparent dilemma is to propose that the mind is made up of a large number of specialized modules. This review describes how this multi-modular framework for the mind overcomes several critical conceptual and theoretical challenges to our understanding of human thinking, and hopefully clarifies what are (and are not some of the implications based on this framework. In particular, an evolutionarily informed deep rationality conception of human thinking can guide psychological research out of clusters of ad hoc models which currently occupy some fields. First, the idea of deep rationality helps theoretical frameworks in terms of orienting themselves with regard to time scale references, which can alter the nature of rationality assessments. Second, the functional domains of deep rationality can be hypothesized (non-exhaustively to include the areas of self-protection, status, affiliation, mate acquisition, mate retention, kin care, and disease avoidance. Thus, although there is no single normative standard of rationality across all of human cognition, there are sensible and objective standards by which we can evaluate multiple, fundamental, domain-specific motives underlying human cognition and behavior. This review concludes with two examples to illustrate the implications of this framework. The first example, decisions about having a child, illustrates how competing models can be understood by realizing that different fundamental motives guiding people’s thinking can sometimes be in conflict. The second example is that of personifications within modern financial markets (e.g., in the form of corporations, which are entities specifically constructed to have just one fundamental motive. This single focus is the source of both the strengths and flaws in how such entities

The nature of thinking, shallow and deep.

Science.gov (United States)

Brase, Gary L

2014-01-01

Because the criteria for success differ across various domains of life, no single normative standard will ever work for all types of thinking. One method for dealing with this apparent dilemma is to propose that the mind is made up of a large number of specialized modules. This review describes how this multi-modular framework for the mind overcomes several critical conceptual and theoretical challenges to our understanding of human thinking, and hopefully clarifies what are (and are not) some of the implications based on this framework. In particular, an evolutionarily informed "deep rationality" conception of human thinking can guide psychological research out of clusters of ad hoc models which currently occupy some fields. First, the idea of deep rationality helps theoretical frameworks in terms of orienting themselves with regard to time scale references, which can alter the nature of rationality assessments. Second, the functional domains of deep rationality can be hypothesized (non-exhaustively) to include the areas of self-protection, status, affiliation, mate acquisition, mate retention, kin care, and disease avoidance. Thus, although there is no single normative standard of rationality across all of human cognition, there are sensible and objective standards by which we can evaluate multiple, fundamental, domain-specific motives underlying human cognition and behavior. This review concludes with two examples to illustrate the implications of this framework. The first example, decisions about having a child, illustrates how competing models can be understood by realizing that different fundamental motives guiding people's thinking can sometimes be in conflict. The second example is that of personifications within modern financial markets (e.g., in the form of corporations), which are entities specifically constructed to have just one fundamental motive. This single focus is the source of both the strengths and flaws in how such entities behave.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

Science.gov (United States)

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Preliminary analyses of the deep geoenvironmental characteristics for the deep borehole disposal of high-level radioactive waste in Korea

Energy Technology Data Exchange (ETDEWEB)

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

2016-06-15

Spent fuels from nuclear power plants, as well as high-level radioactive waste from the recycling of spent fuels, should be safely isolated from human environment for an extremely long time. Recently, meaningful studies on the development of deep borehole radioactive waste disposal system in 3-5 km depth have been carried out in USA and some countries in Europe, due to great advance in deep borehole drilling technology. In this paper, domestic deep geoenvironmental characteristics are preliminarily investigated to analyze the applicability of deep borehole disposal technology in Korea. To do this, state-of-the art technologies in USA and some countries in Europe are reviewed, and geological and geothermal data from the deep boreholes for geothermal usage are analyzed. Based on the results on the crystalline rock depth, the geothermal gradient and the spent fuel types generated in Korea, a preliminary deep borehole concept including disposal canister and sealing system, is suggested.

Preliminary analyses of the deep geoenvironmental characteristics for the deep borehole disposal of high-level radioactive waste in Korea

International Nuclear Information System (INIS)

Lee, Jong Youl; Lee, Min Soo; Choi, Heui Joo; Kim, Geon Young; Kim, Kyung Su

2016-01-01

Spent fuels from nuclear power plants, as well as high-level radioactive waste from the recycling of spent fuels, should be safely isolated from human environment for an extremely long time. Recently, meaningful studies on the development of deep borehole radioactive waste disposal system in 3-5 km depth have been carried out in USA and some countries in Europe, due to great advance in deep borehole drilling technology. In this paper, domestic deep geoenvironmental characteristics are preliminarily investigated to analyze the applicability of deep borehole disposal technology in Korea. To do this, state-of-the art technologies in USA and some countries in Europe are reviewed, and geological and geothermal data from the deep boreholes for geothermal usage are analyzed. Based on the results on the crystalline rock depth, the geothermal gradient and the spent fuel types generated in Korea, a preliminary deep borehole concept including disposal canister and sealing system, is suggested

Toolkits and Libraries for Deep Learning.

Science.gov (United States)

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth

2017-08-01

Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

YouTube as a potential source of information on deep venous thrombosis.

Science.gov (United States)

Bademci, Mehmet Ş; Yazman, Serkan; Güneş, Tevfik; Ocakoglu, Gokhan; Tayfur, Kaptanderya; Gokalp, Orhan

2017-09-01

Background No work has been reported on the use of video websites to learn about deep vein thrombosis and the value of education using them. We examined the characteristics and scientific accuracy of videos related to deep vein thrombosis on YouTube. Methods YouTube was surveyed using no filter and the key words 'deep vein thrombosis' and 'leg vein clot' in June 2016. The videos evaluated were divided into three groups in terms of their scientific content, accuracy, and currency: useful, partly useful, and useless. Results Of the 1200 videos watched, 715 (59.58%) were excluded with the exclusion criteria. Although most of the videos uploaded (22.9%, n = 111) were created by physicians, the number of views for website-based videos was significantly higher (p = 0.002). When the uploaded videos were assessed in terms of their usefulness, videos from physicians and hospitals were statistically more useful than other videos (p < 0.001). Conclusions For videos created by medical professionals to be of higher quality, we believe they should be more up-to-date and comprehensive, and contain animations about treatment modalities and early diagnosis in particular.

Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks.

Science.gov (United States)

Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

2018-06-13

Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks. Copyright © 2018 Elsevier Inc. All rights reserved.

Deep-sea coral research and technology program: Alaska deep-sea coral and sponge initiative final report

Science.gov (United States)

Rooper, Chris; Stone, Robert P.; Etnoyer, Peter; Conrath, Christina; Reynolds, Jennifer; Greene, H. Gary; Williams, Branwen; Salgado, Enrique; Morrison, Cheryl L.; Waller, Rhian G.; Demopoulos, Amanda W.J.

2017-01-01

Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska’s marine waters. In some places, such as the central and western Aleutian Islands, deep-sea coral and sponge resources can be extremely diverse and may rank among the most abundant deep-sea coral and sponge communities in the world. Many different species of fishes and invertebrates are associated with deep-sea coral and sponge communities in Alaska. Because of their biology, these benthic invertebrates are potentially impacted by climate change and ocean acidification. Deepsea coral and sponge ecosystems are also vulnerable to the effects of commercial fishing activities. Because of the size and scope of Alaska’s continental shelf and slope, the vast majority of the area has not been visually surveyed for deep-sea corals and sponges. NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) sponsored a field research program in the Alaska region between 2012–2015, referred to hereafter as the Alaska Initiative. The priorities for Alaska were derived from ongoing data needs and objectives identified by the DSCRTP, the North Pacific Fishery Management Council (NPFMC), and Essential Fish Habitat-Environmental Impact Statement (EFH-EIS) process.This report presents the results of 15 projects conducted using DSCRTP funds from 2012-2015. Three of the projects conducted as part of the Alaska deep-sea coral and sponge initiative included dedicated at-sea cruises and fieldwork spread across multiple years. These projects were the eastern Gulf of Alaska Primnoa pacifica study, the Aleutian Islands mapping study, and the Gulf of Alaska fish productivity study. In all, there were nine separate research cruises carried out with a total of 109 at-sea days conducting research. The remaining projects either used data and samples collected by the three major fieldwork projects or were piggy-backed onto existing research programs at the Alaska Fisheries Science Center (AFSC).

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...

Diverse Portfolio of Scientific Instrumentation Initiatives of the Deep Carbon Observatory

Science.gov (United States)

Schiffries, Craig; Hazen, Robert; Hemley, Russell; Mangum, Andrea

2016-04-01

Advances in scientific instrumentation are important drivers of scientific discovery. The Deep Carbon Observatory (DCO) supports a diverse portfolio of scientific instrumentation initiatives worldwide as part of its ten-year quest to achieve a transformational understanding of the quantities, movements, origins, and forms of Earth's deep carbon. Substantial progress has been made in the development of a wide range of instruments, including: • Quantum cascade laser-infrared absorption spectrometer for clumped methane isotope thermometry (Shuhei Ono) • Large-radius high-mass-resolution multiple-collector isotope ratio mass spectrometer for analysis of rare isotopologues of methane and other gases (Edward Young, Douglas Rumble) • Volcanic field deployment of the laser isotope ratio-meter (Damien Weidmann) • Novel large-volume diamond anvil cell for neutron scattering (Malcolm Guthrie, Reinhard Boehler) • Novel synchrotron x-ray probes for deep carbon (Wendy Mao) • Ultrafast laser instrument for in situ measurements of elastic, electronic, and transport properties of carbon-bearing fluids and crystalline materials (Alexander Goncharov) • Combined instrument for molecular imaging in geochemistry (Andrew Steele) • Pressurized Underwater Sample Handler (Isabelle Daniel, Karyn Rogers) These and other DCO instrumentation projects are highly leveraged investments involving a large number of sponsors, partners, and collaborators.

An overview of latest deep water technologies

International Nuclear Information System (INIS)

Anon.

1995-01-01

The 8th Deep Offshore Technology Conference (DOT VIII, Rio de Janeiro, October 30 - November 3, 1995) has brought together renowned specialists in deep water development projects, as well as managers from oil companies and engineering/service companies to discuss state-of-the-art technologies and ongoing projects in the deep offshore. This paper is a compilation of the session summaries about sub sea technologies, mooring and dynamic positioning, floaters (Tension Leg Platforms (TLP) and Floating Production Storage and Off loading (FPSO)), pipelines and risers, exploration and drilling, and other deep water techniques. (J.S.)

Deep learning in neural networks: an overview.

Science.gov (United States)

Schmidhuber, Jürgen

2015-01-01

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

Combining shallow and deep processing for a robust, fast, deep-linguistic dependency parser

OpenAIRE

Schneider, G

2004-01-01

This paper describes Pro3Gres, a fast, robust, broad-coverage parser that delivers deep-linguistic grammatical relation structures as output, which are closer to predicate-argument structures and more informative than pure constituency structures. The parser stays as shallow as is possible for each task, combining shallow and deep-linguistic methods by integrating chunking and by expressing the majority of long-distance dependencies in a context-free way. It combines statistical and rule-base...

Repository and deep borehole disposition of plutonium

International Nuclear Information System (INIS)

Halsey, W.G.

1996-02-01

Control and disposition of excess weapons plutonium is a growing issue as both the US and Russia retire a large number of nuclear weapons> A variety of options are under consideration to ultimately dispose of this material. Permanent disposition includes tow broad categories: direct Pu disposal where the material is considered waste and disposed of, and Pu utilization, where the potential energy content of the material is exploited via fissioning. The primary alternative to a high-level radioactive waste repository for the ultimate disposal of plutonium is development of a custom geologic facility. A variety of geologic facility types have been considered, but the concept currently being assessed is the deep borehole

Searching for gluon number fluctuations effects in eA collisions

Energy Technology Data Exchange (ETDEWEB)

Kugeratski, M. S. [Universidade Federal de Santa Catarina, Campus Joinville, Rua Presidente Prudente de Moraes, 406, CEP 89218-000, Joinville, SC (Brazil); Gonçalves, V. P.; Santana Amaral, J. T. de [Instituto de Física e Matemática, Universidade Federal de Pelotas, Caixa Postal 354, CEP 96010-900, Pelotas, RS (Brazil)

2014-11-11

We propose to investigate the gluon number fluctuations effects in deep inelastic electron-ion scattering at high energies. We estimate the nuclear structure function F{sub 2}{sup A}(x,Q{sup 2}), as well the longitudinal and charm contributions, using a generalization for nuclear targets of the Golec-Biernat-Wusthoff (GBW) model which describes the electron proton HERA data. Here we consider that the nucleus at high energies acts as an amplifier of the physics of high parton densities. For a first investigation we study the scattering with Ca and Pb nuclei. Our preliminary results predict that the effects of gluon number fluctuations are small in the region of the future electron ion collider.

Mass transfer controlled reactions in packed beds at low Reynolds numbers

Energy Technology Data Exchange (ETDEWEB)

Fedkiw, P.S.

1978-12-01

The a priori prediction and correlation of mass-transfer rates in transport limited, packed-bed reactors at low Reynolds numbers is examined. The solutions to the governing equations for a flow-through porous electrode reactor indicate that these devices must operate at a low space velocity to suppress a large ohmic potential drop. Packed-bed data for the mass-transfer rate at such low Reynolds numbers were examined and found to be sparse, especially in liquid systems. Prior models to simulate the solid-void structure in a bed are reviewed. Here the bed was envisioned as an array of sinusoidal periodically constricted tubes (PCT). Use of this model has not appeared in the literature. The velocity field in such a tube should be a good approximation to the converging-diverging character of the velocity field in an actual bed. The creeping flow velocity profiles were calculated. These results were used in the convective-diffusion equation to find mass transfer rates at high Peclet number for both deep and shallow beds, for low Peclet numbers in a deep bed. All calculations assumed that the reactant concentration at the tube surface is zero. Mass-transfer data were experimentally taken in a transport controlled, flow-through porous electrode to test the theoretical calculations and to provide data resently unavailable for deeper beds. It was found that the sinusoidal PCT model could not fit the data of this work or that available in the literature. However, all data could be adequately described by a model which incorporates a channelingeffect. The bed was successfully modeled as an array of dual sized straight tubes.

A Question of Merit: Merit-Based Scholarship Programs Have Gained Huge Popularity in a Number of States, but Many Wonder at What (or Whose) Expense

Science.gov (United States)

Finken, Dee Anne

2004-01-01

Ten years ago, the state best known for its peaches launched a revolution that still reverberates in the halls of colleges and universities across the country. Faced with a plethora of poorly performing high-school students and a growing number of graduates fleeing the state for postsecondary study, Georgia unveiled its Helping Outstanding Pupils…

DeepVel: Deep learning for the estimation of horizontal velocities at the solar surface

Science.gov (United States)

Asensio Ramos, A.; Requerey, I. S.; Vitas, N.

2017-07-01

Many phenomena taking place in the solar photosphere are controlled by plasma motions. Although the line-of-sight component of the velocity can be estimated using the Doppler effect, we do not have direct spectroscopic access to the components that are perpendicular to the line of sight. These components are typically estimated using methods based on local correlation tracking. We have designed DeepVel, an end-to-end deep neural network that produces an estimation of the velocity at every single pixel, every time step, and at three different heights in the atmosphere from just two consecutive continuum images. We confront DeepVel with local correlation tracking, pointing out that they give very similar results in the time and spatially averaged cases. We use the network to study the evolution in height of the horizontal velocity field in fragmenting granules, supporting the buoyancy-braking mechanism for the formation of integranular lanes in these granules. We also show that DeepVel can capture very small vortices, so that we can potentially expand the scaling cascade of vortices to very small sizes and durations. The movie attached to Fig. 3 is available at http://www.aanda.org

Breeding crop plants with deep roots: their role in sustainable carbon, nutrient and water sequestration

Science.gov (United States)

Kell, Douglas B.

2011-01-01

Background The soil represents a reservoir that contains at least twice as much carbon as does the atmosphere, yet (apart from ‘root crops’) mainly just the above-ground plant biomass is harvested in agriculture, and plant photosynthesis represents the effective origin of the overwhelming bulk of soil carbon. However, present estimates of the carbon sequestration potential of soils are based more on what is happening now than what might be changed by active agricultural intervention, and tend to concentrate only on the first metre of soil depth. Scope Breeding crop plants with deeper and bushy root ecosystems could simultaneously improve both the soil structure and its steady-state carbon, water and nutrient retention, as well as sustainable plant yields. The carbon that can be sequestered in the steady state by increasing the rooting depths of crop plants and grasses from, say, 1 m to 2 m depends significantly on its lifetime(s) in different molecular forms in the soil, but calculations (http://dbkgroup.org/carbonsequestration/rootsystem.html) suggest that this breeding strategy could have a hugely beneficial effect in stabilizing atmospheric CO2. This sets an important research agenda, and the breeding of plants with improved and deep rooting habits and architectures is a goal well worth pursuing. PMID:21813565

A comparative study of deep learning models for medical image classification

Science.gov (United States)

Dutta, Suvajit; Manideep, B. C. S.; Rai, Shalva; Vijayarajan, V.

2017-11-01

Deep Learning(DL) techniques are conquering over the prevailing traditional approaches of neural network, when it comes to the huge amount of dataset, applications requiring complex functions demanding increase accuracy with lower time complexities. Neurosciences has already exploited DL techniques, thus portrayed itself as an inspirational source for researchers exploring the domain of Machine learning. DL enthusiasts cover the areas of vision, speech recognition, motion planning and NLP as well, moving back and forth among fields. This concerns with building models that can successfully solve variety of tasks requiring intelligence and distributed representation. The accessibility to faster CPUs, introduction of GPUs-performing complex vector and matrix computations, supported agile connectivity to network. Enhanced software infrastructures for distributed computing worked in strengthening the thought that made researchers suffice DL methodologies. The paper emphases on the following DL procedures to traditional approaches which are performed manually for classifying medical images. The medical images are used for the study Diabetic Retinopathy(DR) and computed tomography (CT) emphysema data. Both DR and CT data diagnosis is difficult task for normal image classification methods. The initial work was carried out with basic image processing along with K-means clustering for identification of image severity levels. After determining image severity levels ANN has been applied on the data to get the basic classification result, then it is compared with the result of DNNs (Deep Neural Networks), which performed efficiently because of its multiple hidden layer features basically which increases accuracy factors, but the problem of vanishing gradient in DNNs made to consider Convolution Neural Networks (CNNs) as well for better results. The CNNs are found to be providing better outcomes when compared to other learning models aimed at classification of images. CNNs are

Automatic spent fuel ID number reader (I)

International Nuclear Information System (INIS)

Tanabe, S.; Kawamoto, H.; Fujimaki, K.; Kobe, A.

1991-01-01

An effective and efficient technique has been developed for facilitating identification works of LWR spent fuel stored in large scale spent fuel storage pools of such as processing plants. Experience shows that there are often difficulties in the implementation of operator's nuclear material accountancy and control works as well as safeguards inspections conducted on spent fuel assemblies stored in deep water pool. This paper reports that the technique is realized as an automatic spent fuel ID number reader system installed on fuel handling machine. The ID number reader system consists of an optical sub-system and an image processing sub-system. Thousands of spent fuel assemblies stored in under water open racks in each storage pool could be identified within relatively short time (e.g. within several hours) by using this combination. Various performance tests were carried out on image processing sub-system in 1990 using TV images obtained from different types of spent fuel assemblies stored in various storage pools of PWR and BWR power stations

Deep Learning in Drug Discovery.

Science.gov (United States)

Gawehn, Erik; Hiss, Jan A; Schneider, Gisbert

2016-01-01

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Iris Transponder-Communications and Navigation for Deep Space

Science.gov (United States)

Duncan, Courtney B.; Smith, Amy E.; Aguirre, Fernando H.

2014-01-01

The Jet Propulsion Laboratory has developed the Iris CubeSat compatible deep space transponder for INSPIRE, the first CubeSat to deep space. Iris is 0.4 U, 0.4 kg, consumes 12.8 W, and interoperates with NASA's Deep Space Network (DSN) on X-Band frequencies (7.2 GHz uplink, 8.4 GHz downlink) for command, telemetry, and navigation. This talk discusses the Iris for INSPIRE, it's features and requirements; future developments and improvements underway; deep space and proximity operations applications for Iris; high rate earth orbit variants; and ground requirements, such as are implemented in the DSN, for deep space operations.

Context and Deep Learning Design

Science.gov (United States)

Boyle, Tom; Ravenscroft, Andrew

2012-01-01

Conceptual clarification is essential if we are to establish a stable and deep discipline of technology enhanced learning. The technology is alluring; this can distract from deep design in a surface rush to exploit the affordances of the new technology. We need a basis for design, and a conceptual unit of organization, that are applicable across…

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...

Too Deep or Not Too Deep?: A Propensity-Matched Comparison of the Analgesic Effects of a Superficial Versus Deep Serratus Fascial Plane Block for Ambulatory Breast Cancer Surgery.

Science.gov (United States)

Abdallah, Faraj W; Cil, Tulin; MacLean, David; Madjdpour, Caveh; Escallon, Jaime; Semple, John; Brull, Richard

2018-07-01

Serratus fascial plane block can reduce pain following breast surgery, but the question of whether to inject the local anesthetic superficial or deep to the serratus muscle has not been answered. This cohort study compares the analgesic benefits of superficial versus deep serratus plane blocks in ambulatory breast cancer surgery patients at Women's College Hospital between February 2014 and December 2016. We tested the joint hypothesis that deep serratus block is noninferior to superficial serratus block for postoperative in-hospital (pre-discharge) opioid consumption and pain severity. One hundred sixty-six patients were propensity matched among 2 groups (83/group): superficial and deep serratus blocks. The cohort was used to evaluate the effect of blocks on postoperative oral morphine equivalent consumption and area under the curve for rest pain scores. We considered deep serratus block to be noninferior to superficial serratus block if it were noninferior for both outcomes, within 15 mg morphine and 4 cm·h units margins. Other outcomes included intraoperative fentanyl requirements, time to first analgesic request, recovery room stay, and incidence of postoperative nausea and vomiting. Deep serratus block was associated with postoperative morphine consumption and pain scores area under the curve that were noninferior to those of the superficial serratus block. Intraoperative fentanyl requirements, time to first analgesic request, recovery room stay, and postoperative nausea and vomiting were not different between blocks. The postoperative in-hospital analgesia associated with deep serratus block is as effective (within an acceptable margin) as superficial serratus block following ambulatory breast cancer surgery. These new findings are important to inform both current clinical practices and future prospective studies.

Development and simulation of the air-jack for emergency like a huge disaster; Kyujoyo eajakki no kaihatsu to sono simyureshon

Energy Technology Data Exchange (ETDEWEB)

Katsuyama, Kunihisa.; Ogata, Yuji.; Wada, Yuji. [National Institute for Resources and Environment, Tsukuba (Japan); Hashizume, Kiyoshi.; Nishida, Kenjiro. [Nippon Kayaku Corp., Tokyo (Japan)

1999-02-28

When a disaster is so huge like Kobe earthquake, every energy line is killed. Even if we want to help the sufferers, we have no energy to move machines to help them. As collapsed houses are very heavy, we need machines to remove collapsed stuff. Explosives include a lot of energy in themselves. So, an air-jack which has explosives inside was developed to remove collapsed stuff on suffered people. A simple air-jack was made and tested. One concrete block, 50cm x 50cm x 50cm, was lifted by the simple air-jack. A simulation of lifting the concrete block was carried out with a programme ANSYS on the super computer. (author)

Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

Science.gov (United States)

He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang

2017-03-01

Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.

Origin of deep subgap states in amorphous indium gallium zinc oxide: Chemically disordered coordination of oxygen

International Nuclear Information System (INIS)

Sallis, S.; Williams, D. S.; Butler, K. T.; Walsh, A.; Quackenbush, N. F.; Junda, M.; Podraza, N. J.; Fischer, D. A.; Woicik, J. C.; White, B. E.; Piper, L. F. J.

2014-01-01

The origin of the deep subgap states in amorphous indium gallium zinc oxide (a-IGZO), whether intrinsic to the amorphous structure or not, has serious implications for the development of p-type transparent amorphous oxide semiconductors. We report that the deep subgap feature in a-IGZO originates from local variations in the oxygen coordination and not from oxygen vacancies. This is shown by the positive correlation between oxygen composition and subgap intensity as observed with X-ray photoelectron spectroscopy. We also demonstrate that the subgap feature is not intrinsic to the amorphous phase because the deep subgap feature can be removed by low-temperature annealing in a reducing environment. Atomistic calculations of a-IGZO reveal that the subgap state originates from certain oxygen environments associated with the disorder. Specifically, the subgap states originate from oxygen environments with a lower coordination number and/or a larger metal-oxygen separation.

Origin of deep subgap states in amorphous indium gallium zinc oxide: Chemically disordered coordination of oxygen

Energy Technology Data Exchange (ETDEWEB)

Sallis, S.; Williams, D. S. [Materials Science and Engineering, Binghamton University, Binghamton, New York 13902 (United States); Butler, K. T.; Walsh, A. [Center for Sustainable Technologies and Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY (United Kingdom); Quackenbush, N. F. [Department of Physics, Applied Physics, and Astronomy, Binghamton University, Binghamton, New York 13902 (United States); Junda, M.; Podraza, N. J. [Department of Physics and Astronomy, University of Toledo, Toledo, Ohio 43606 (United States); Fischer, D. A.; Woicik, J. C. [Materials Science and Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899 (United States); White, B. E.; Piper, L. F. J., E-mail: lpiper@binghamton.edu [Department of Physics, Applied Physics, and Astronomy, Binghamton University, Binghamton, New York 13902 (United States); Materials Science and Engineering, Binghamton University, Binghamton, New York 13902 (United States)

2014-06-09

The origin of the deep subgap states in amorphous indium gallium zinc oxide (a-IGZO), whether intrinsic to the amorphous structure or not, has serious implications for the development of p-type transparent amorphous oxide semiconductors. We report that the deep subgap feature in a-IGZO originates from local variations in the oxygen coordination and not from oxygen vacancies. This is shown by the positive correlation between oxygen composition and subgap intensity as observed with X-ray photoelectron spectroscopy. We also demonstrate that the subgap feature is not intrinsic to the amorphous phase because the deep subgap feature can be removed by low-temperature annealing in a reducing environment. Atomistic calculations of a-IGZO reveal that the subgap state originates from certain oxygen environments associated with the disorder. Specifically, the subgap states originate from oxygen environments with a lower coordination number and/or a larger metal-oxygen separation.

Ensemble Deep Learning for Biomedical Time Series Classification

Directory of Open Access Journals (Sweden)

Lin-peng Jin

2016-01-01

Full Text Available Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

Low energy neutron background in deep underground laboratories

Energy Technology Data Exchange (ETDEWEB)

Best, Andreas, E-mail: andreas.best@lngs.infn.it [INFN, Laboratori Nazionali del Gran Sasso (LNGS), 67100 Assergi (Italy); Department of Physics and The Joint Institute for Nuclear Astrophysics, University of Notre Dame, Notre Dame, IN 46556 (United States); Görres, Joachim [Department of Physics and The Joint Institute for Nuclear Astrophysics, University of Notre Dame, Notre Dame, IN 46556 (United States); Junker, Matthias [INFN, Laboratori Nazionali del Gran Sasso (LNGS), 67100 Assergi (Italy); Kratz, Karl-Ludwig [Department for Biogeochemistry, Max-Planck-Institute for Chemistry, 55020 Mainz (Germany); Laubenstein, Matthias [INFN, Laboratori Nazionali del Gran Sasso (LNGS), 67100 Assergi (Italy); Long, Alexander [Department of Physics and The Joint Institute for Nuclear Astrophysics, University of Notre Dame, Notre Dame, IN 46556 (United States); Nisi, Stefano [INFN, Laboratori Nazionali del Gran Sasso (LNGS), 67100 Assergi (Italy); Smith, Karl; Wiescher, Michael [Department of Physics and The Joint Institute for Nuclear Astrophysics, University of Notre Dame, Notre Dame, IN 46556 (United States)

2016-03-11

The natural neutron background influences the maximum achievable sensitivity in most deep underground nuclear, astroparticle and double-beta decay physics experiments. Reliable neutron flux numbers are an important ingredient in the design of the shielding of new large-scale experiments as well as in the analysis of experimental data. Using a portable setup of {sup 3}He counters we measured the thermal neutron flux at the Kimballton Underground Research Facility, the Soudan Underground Laboratory, on the 4100 ft and the 4850 ft levels of the Sanford Underground Research Facility, at the Waste Isolation Pilot Plant and at the Gran Sasso National Laboratory. Absolute neutron fluxes at these laboratories are presented.

Molecular analysis of deep subsurface bacteria

International Nuclear Information System (INIS)

Jimenez Baez, L.E.

1989-09-01

Deep sediments samples from site C10a, in Appleton, and sites, P24, P28, and P29, at the Savannah River Site (SRS), near Aiken, South Carolina were studied to determine their microbial community composition, DNA homology and mol %G+C. Different geological formations with great variability in hydrogeological parameters were found across the depth profile. Phenotypic identification of deep subsurface bacteria underestimated the bacterial diversity at the three SRS sites, since bacteria with the same phenotype have different DNA composition and less than 70% DNA homology. Total DNA hybridization and mol %G+C analysis of deep sediment bacterial isolates suggested that each formation is comprised of different microbial communities. Depositional environment was more important than site and geological formation on the DNA relatedness between deep subsurface bacteria, since more 70% of bacteria with 20% or more of DNA homology came from the same depositional environments. Based on phenotypic and genotypic tests Pseudomonas spp. and Acinetobacter spp.-like bacteria were identified in 85 million years old sediments. This suggests that these microbial communities might have been adapted during a long period of time to the environmental conditions of the deep subsurface

Bacteriophage lytic to Desulfovibrio aespoeensis isolated from deep groundwater.

Science.gov (United States)

Eydal, Hallgerd S C; Jägevall, Sara; Hermansson, Malte; Pedersen, Karsten

2009-10-01

Viruses were earlier found to be 10-fold more abundant than prokaryotes in deep granitic groundwater at the Aspö Hard Rock Laboratory (HRL). Using a most probable number (MPN) method, 8-30 000 cells of sulphate-reducing bacteria per ml were found in groundwater from seven boreholes at the Aspö HRL. The content of lytic phages infecting the indigenous bacterium Desulfovibrio aespoeensis in Aspö groundwater was analysed using the MPN technique for phages. In four of 10 boreholes, 0.2-80 phages per ml were found at depths of 342-450 m. Isolates of lytic phages were made from five cultures. Using transmission electron microscopy, these were characterized and found to be in the Podoviridae morphology group. The isolated phages were further analysed regarding host range and were found not to infect five other species of Desulfovibrio or 10 Desulfovibrio isolates with up to 99.9% 16S rRNA gene sequence identity to D. aespoeensis. To further analyse phage-host interactions, using a direct count method, growth of the phages and their host was followed in batch cultures, and the viral burst size was calculated to be approximately 170 phages per lytic event, after a latent period of approximately 70 h. When surviving cells from infected D. aespoeensis batch cultures were inoculated into new cultures and reinfected, immunity to the phages was found. The parasite-prey system found implies that viruses are important for microbial ecosystem diversity and activity, and for microbial numbers in deep subsurface groundwater.

Experimental tests of QCD: Deep inelastic scattering, e+e- annihilation and hard hadron-hadron scattering

International Nuclear Information System (INIS)

Hansl-Kozanecka, T.

1992-01-01

In this set of lectures the author examines phenomenological aspects of quantum chromodynamics (QCD) which are relevant for lepton-hadron, electron-positron, and hadron-hadron collisions. He points how the strength of the strong coupling constant, αs, makes QCD calculations converge much more slowly in powers of αs, and missing higher order terms must be carefully estimated. The most stringent test of QCD can be performed in deep inelastic lepton scattering and in e + e - annihilation. In deep inelastic scattering the virtual γ or W/Z are used as a probe of the nucleon structure. They couple to quarks, not gluons. Only the incoming and outgoing lepton have to be measured. The hadronic fluid state does not have to be analyzed. In e + e - annihilation the virtual γ or Z 0 decays to lepton and quark pairs. The branching ratio into quarks is a counter for the number of colours available, the detailed structure of the final state reflects the radiation of gluons as the initial quark-antiquark separate from each other. Quarks and gluons are observed here, though in the presence of hadron formation. Hard hadron-hadron, or parton-parton collisions provide cross sections dominated by the gluon component, which is only weakly measured in deep inelastic collisions. Recent experimental results in these three areas are reviewed, and compared to QCD calculations. Scaling violations and analysis of structure functions in deep inelastic scattering are reviewed. QCD in e + e - branching to hadrons is reviewed near the Z 0 resonance, and a number of cross sections and jet related properties which can be calculated as a function of the single parameter αs are reviewed. Hadron-hadron collisions are reviewed for three processes; jet production, direct photon production, and high p perpendicular W/Z boson production

Joint Training of Deep Boltzmann Machines

OpenAIRE

Goodfellow, Ian; Courville, Aaron; Bengio, Yoshua

2012-01-01

We introduce a new method for training deep Boltzmann machines jointly. Prior methods require an initial learning pass that trains the deep Boltzmann machine greedily, one layer at a time, or do not perform well on classifi- cation tasks.

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.

Deep boreholes; Tiefe Bohrloecher

Energy Technology Data Exchange (ETDEWEB)

Bracke, Guido [Gesellschaft fuer Anlagen- und Reaktorsicherheit gGmbH Koeln (Germany); Charlier, Frank [NSE international nuclear safety engineering gmbh, Aachen (Germany); Geckeis, Horst [Karlsruher Institut fuer Technologie (Germany). Inst. fuer Nukleare Entsorgung; and others

2016-02-15

The report on deep boreholes covers the following subject areas: methods for safe enclosure of radioactive wastes, requirements concerning the geological conditions of possible boreholes, reversibility of decisions and retrievability, status of drilling technology. The introduction covers national and international activities. Further chapters deal with the following issues: basic concept of the storage in deep bore holes, status of the drilling technology, safe enclosure, geomechanics and stability, reversibility of decisions, risk scenarios, compliancy with safe4ty requirements and site selection criteria, research and development demand.

Promoting Active Learning of Graduate Student by Deep Reading in Biochemistry and Microbiology Pharmacy Curriculum

Science.gov (United States)

Peng, Ren

2017-01-01

To promote graduate students' active learning, deep reading of high quality papers was done by graduate students enrolled in biochemistry and microbiology pharmacy curriculum offered by college of life science, Jiangxi Normal University from 2013 to 2015. The number of graduate students, who participated in the course in 2013, 2014, and 2015 were…

A Huge Morel-Lavallée Lesion Treated Using a Quilting Suture Method: A Case Report and Review of the Literature.

Science.gov (United States)

Seo, Bommie F; Kang, In Sook; Jeong, Yeon Jin; Moon, Suk Ho

2014-06-01

The Morel-Lavallée lesion is a collection of serous fluid that develops after closed degloving injuries and after surgical procedures particularly in the pelvis and abdomen. It is a persistent seroma and is usually resistant to conservative methods of treatment such as percutaneous drainage and compression. Various methods of curative treatment have been reported in the literature, such as application of fibrin sealant, doxycycline, or alcohol sclerodhesis. We present a case of a huge recurrent Morel-Lavallée lesion in the lower back and buttock region that was treated with quilting sutures, fibrin sealant, and compression, with a review of the literature. © The Author(s) 2014.

Use of deep seismic shooting to study graben-like troughs. [Urals

Energy Technology Data Exchange (ETDEWEB)

Makalovskiy, V.V.; Silayev, V.A.

1983-01-01

In the Southeast Perm Oblast, in the zone of articulation of the Russian platform and the Cisural trough, in order to study the structure of the graben-like troughs together with deep drilling, well seismic exploration is used by the method of deep seismic shooting (DSS). The DSS method developed by the Kamskiy department of the VNIGNI consists of blasting in the well shaft and recording of the elastic fluctuations on the Earth's surface. The use of the DSS made it possible to pinpoint structural details of the graben-like trough, and to clarify that this is in essence a zone of fracturing, where the lowered blocks alternated with elevated, and to establish the location and amplitude of the tectonic disorders. High geological information content, low labor intensity and rapidity of obtaining the results make it possible to recommend the DSS together with prospecting and exploratory drilling to study complexly constructed objects in order to reduce the number of unproductive wells.

Post-closure resaturation of a deep radioactive waste repository

International Nuclear Information System (INIS)

Cox, I.C.S.; Rodwell, W.R.

1989-03-01

The post-closure resaturation of a deep radioactive waste repository has been modelled for a number of generic disposal concepts. A combination of numerical ground water flow simulations and analytical calculations has been used to investigate the variation of repository fluid pressure and degree of water saturation with time, and to determine the factors influencing resaturation times. The host rock permeability was found to be the most important determining factor. For geological environments regarded as likely for a waste repository, resaturation is predicted to be a short term process compared with gas generation and contaminant migration timescales. (author)

A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition.

Science.gov (United States)

Fuentes, Alvaro; Yoon, Sook; Kim, Sang Cheol; Park, Dong Sun

2017-09-04

Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called "deep learning meta-architectures". We combine each of these meta-architectures with "deep feature extractors" such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant's surrounding area.

A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition

Directory of Open Access Journals (Sweden)

Alvaro Fuentes

2017-09-01

Full Text Available Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN, Region-based Fully Convolutional Network (R-FCN, and Single Shot Multibox Detector (SSD, which for the purpose of this work are called “deep learning meta-architectures”. We combine each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network (ResNet. We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant’s surrounding area.

DeepLoc: prediction of protein subcellular localization using deep learning

DEFF Research Database (Denmark)

Almagro Armenteros, Jose Juan; Sønderby, Casper Kaae; Sønderby, Søren Kaae

2017-01-01

The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from...... knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict...... current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc . Example code is available at https://github.com/JJAlmagro/subcellular_localization . The dataset is available at http...

Pre-cementation of deep shaft

Science.gov (United States)

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 sub-seafloor prokaryotes stimulated at interfaces over geological time RID B-1731-2010 RID A-1877-2008 RID D-2690-2009 RID A-2970-2010

DEFF Research Database (Denmark)

Parkes, RJ; Webster, G.; Cragg, BA

2005-01-01

in numbers of prokaryotes at depth were more restricted but also corresponded to increased activity; however, this time they were associated with repeating layers of diatom-rich sediments ( about 9 Myr old). These results show that deep sedimentary prokaryotes can have high activity, have changing diversity...... Pacific Ocean sites, margin and open ocean, both of which have deep, subsurface stimulation of prokaryotic processes associated with geochemical and/or sedimentary interfaces. At 90 m depth in the margin site, stimulation was such that prokaryote numbers were higher ( about 13-fold) and activity rates...

Process strategies for ultra-deep x-ray lithography at the Advanced Photon Source

International Nuclear Information System (INIS)

Mancini, D.C.; Moldovan, N.; Divan, R.; De Carlo, F.; Yaeger, J.

2001-01-01

For the past five years, we have been investigating and advancing processing capabilities for deep x-ray lithography (DXRL) using synchrotron radiation from a bending magnet at the Advanced Photon Source (APS), with an emphasis on ultra-deep structures (1mm to 1cm thick). The use of higher-energy x-rays has presented many challenges in developing optimal lithographic techniques for high-aspect ratio structures: mask requirements, resist preparation, exposure, development, and post-processing. Many problems are more severe for high-energy exposure of thicker films than for sub-millimeter structures and affect resolution, processing time, adhesion, damage, and residue. A number of strategies have been created to overcome the challenges and limitations of ultra-deep x-ray lithography (UDXRL), that have resulted in the current choices for mask, substrate, and process flow at the APS. We describe our current process strategies for UDXRL, how they address the challenges presented, and their current limitations. We note especially the importance of the process parameters for use of the positive tone resist PMMA for UDXRL, and compare to the use of negative tone resists such as SU-8 regarding throughput, resolution, adhesion, damage, and post-processing.

Cardiovascular responses during deep water running versus shallow water running in school children

Directory of Open Access Journals (Sweden)

Anerao Urja M, Shinde Nisha K, Khatri SM

2014-03-01

Full Text Available Overview: As the school going children especially the adolescents’ need workout routine; it is advisable that the routine is imbibed in the school’s class time table. In India as growing number of schools provide swimming as one of the recreational activities; school staff often fails to notice the boredom that is caused by the same activity. Deep as well as shallow water running can be one of the best alternatives to swimming. Hence the present study was conducted to find out the cardiovascular response in these individuals. Methods: This was a Prospective Cross-Sectional Comparative Study done in 72 healthy school going students (males grouped into 2 according to the interventions (Deep water running and Shallow water running. Cardiovascular parameters such as Heart rate (HR, Saturation of oxygen (SpO2, Maximal oxygen consumption (VO2max and Rate of Perceived Exertion (RPE were assessed. Results: Significant improvements in cardiovascular parameters were seen in both the groups i.e. by both the interventions. Conclusion: Deep water running and Shallow water running can be used to improve cardiac function in terms of various outcome measures used in the study.

Artificial neural network based modeling of performance characteristics of deep well pumps with splitter blade

International Nuclear Information System (INIS)

Goelcue, Mustafa

2006-01-01

Experimental studies were made to investigate the effects of splitter blade length (25%, 35%, 50%, 60% and 80% of the main blade length) on the pump characteristics of deep well pumps for different blade numbers (z=3, 4, 5, 6 and 7). In this study, an artificial neural network (ANN) was used for modeling the performance of deep well pumps with splitter blades. Two hundred and ten experimental results were used to train and test. Forty-two patterns have been randomly selected and used as the test data. The main parameters for the experiments are the blade number (z), non-dimensional splitter blade length (L-bar ), flow rate (Q, l/s), head (H m , m), efficiency (η, %) and power (P e , kW). z, L-bar and Q have been used as the input layer, and H m and η have also been used as the output layer. The best training algorithm and number of neurons were obtained. Training of the network was performed using the Levenberg-Marquardt (LM) algorithm. To determine the effect of the transfer function, different ANN models are trained, and the results of these ANN models are compared. Some statistical methods; fraction of variance (R 2 ) and root mean squared error (RMSE) values, have been used for comparison

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

Science.gov (United States)

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

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

Deep Complementary Bottleneck Features for Visual Speech Recognition

NARCIS (Netherlands)

Petridis, Stavros; Pantic, Maja

Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep

Enhancing the Radio Astronomy Capabilities at NASA's Deep Space Network

Science.gov (United States)

Lazio, Joseph; Teitelbaum, Lawrence; Franco, Manuel M.; Garcia-Miro, Cristina; Horiuchi, Shinji; Jacobs, Christopher; Kuiper, Thomas; Majid, Walid

2015-08-01

NASA's Deep Space Network (DSN) is well known for its role in commanding and communicating with spacecraft across the solar system that produce a steady stream of new discoveries in Astrophysics, Heliophysics, and Planetary Science. Equipped with a number of large antennas distributed across the world, the DSN also has a history of contributing to a number of leading radio astronomical projects. This paper summarizes a number of enhancements that are being implemented currently and that are aimed at increasing its capabilities to engage in a wide range of science observations. These enhancements include* A dual-beam system operating between 18 and 27 GHz (~ 1 cm) capable of conducting a variety of molecular line observations, searches for pulsars in the Galactic center, and continuum flux density (photometry) of objects such as nearby protoplanetary disks* Enhanced spectroscopy and pulsar processing backends for use at 1.4--1.9 GHz (20 cm), 18--27 GHz (1 cm), and 38--50 GHz (0.7 cm)* The DSN Transient Observatory (DTN), an automated, non-invasive backend for transient searching* Larger bandwidths (>= 0.5 GHz) for pulsar searching and timing; and* Improved data rates (2048 Mbps) and better instrumental response for very long baseline interferometric (VLBI) observations with the new DSN VLBI processor (DVP), which is providing unprecedented sensitivity for maintenance of the International Celestial Reference Frame (ICRF) and development of future versions.One of the results of these improvements is that the 70~m Deep Space Station 43 (DSS-43, Tidbinbilla antenna) is now the most sensitive radio antenna in the southern hemisphere. Proposals to use these systems are accepted from the international community.Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics & Space Administration.

Producing deep-water hydrocarbons

International Nuclear Information System (INIS)

Pilenko, Thierry

2011-01-01

Several studies relate the history and progress made in offshore production from oil and gas fields in relation to reserves and the techniques for producing oil offshore. The intention herein is not to review these studies but rather to argue that the activities of prospecting and producing deep-water oil and gas call for a combination of technology and project management and, above all, of devotion and innovation. Without this sense of commitment motivating men and women in this industry, the human adventure of deep-water production would never have taken place

Behavior of colloids in radionuclide migration in deep geologic formation

International Nuclear Information System (INIS)

Kanno, Takuji

1994-01-01

In case high level waste is isolated in deep strata, it is important to elucidate the behavior of movement that radionuclides take in the strata. Recently, it has been recognized that the participation of colloids is very important, and it has been studied actively. In this study, as to the mechanism of the adsorption of colloids to geological media or buffers, analysis was carried out for a number of systems, and it was clarified in what case they are caught or they move without being caught. Also it is considered what research is necessary hereafter. First, the kinds of colloids are shown. As the properties of colloids that control the movement of colloids in groundwater in deep strata, the surface potential, shape, size and so on of colloids are conceivable. These properties are briefly discussed. As the interaction of colloids and geological media, the interaction by electrostatic attraction, the fast and slow movement of colloids through rock crevices, and the filtration of colloids in buffers and porous media are described. The experimental results on the movement of colloids are reported. (K.I.)

Deep inelastic processes and the parton model

International Nuclear Information System (INIS)

Altarelli, G.

The lecture was intended as an elementary introduction to the physics of deep inelastic phenomena from the point of view of theory. General formulae and facts concerning inclusive deep inelastic processes in the form: l+N→l'+hadrons (electroproduction, neutrino scattering) are first recalled. The deep inelastic annihilation e + e - →hadrons is then envisaged. The light cone approach, the parton model and their relation are mainly emphasized

Life Support for Deep Space and Mars

Science.gov (United States)

Jones, Harry W.; Hodgson, Edward W.; Kliss, Mark H.

2014-01-01

How should life support for deep space be developed? The International Space Station (ISS) life support system is the operational result of many decades of research and development. Long duration deep space missions such as Mars have been expected to use matured and upgraded versions of ISS life support. Deep space life support must use the knowledge base incorporated in ISS but it must also meet much more difficult requirements. The primary new requirement is that life support in deep space must be considerably more reliable than on ISS or anywhere in the Earth-Moon system, where emergency resupply and a quick return are possible. Due to the great distance from Earth and the long duration of deep space missions, if life support systems fail, the traditional approaches for emergency supply of oxygen and water, emergency supply of parts, and crew return to Earth or escape to a safe haven are likely infeasible. The Orbital Replacement Unit (ORU) maintenance approach used by ISS is unsuitable for deep space with ORU's as large and complex as those originally provided in ISS designs because it minimizes opportunities for commonality of spares, requires replacement of many functional parts with each failure, and results in substantial launch mass and volume penalties. It has become impractical even for ISS after the shuttle era, resulting in the need for ad hoc repair activity at lower assembly levels with consequent crew time penalties and extended repair timelines. Less complex, more robust technical approaches may be needed to meet the difficult deep space requirements for reliability, maintainability, and reparability. Developing an entirely new life support system would neglect what has been achieved. The suggested approach is use the ISS life support technologies as a platform to build on and to continue to improve ISS subsystems while also developing new subsystems where needed to meet deep space requirements.

Finding AGN in Deep X-ray Flux States with Swift

OpenAIRE

Grupe, Dirk; Komossa, S.; Bush, Mason; Pruett, Chelsea; Ernst, Sonny; Barber, Taylor; Carter, Jen; Schartel, Norbert; Rodriguez, Pedro; Santos-Lleó, Maria

2015-01-01

We report on our ongoing project of finding Active Galactic Nuclei (AGN) that go into deep X-ray flux states detected by Swift. Swift is performing an extensive study on the flux and spectral variability of AGN using Guest Investigator and team fill-in programs followed by triggering XMM_Newton for deeper follow-up observations. So far this program has been very successful and has led to a number of XMM-Newton follow up observations, including Mkn 335, PG 0844+349, and RX J2340.8-5329. Recent...

Deep postoperative spine infection treated by negative pressure therapy in patients with progressive spinal deformities.

Science.gov (United States)

Canavese, Federico; Marengo, Lorenza; Corradin, Marco; Mansour, Mounira; Samba, Antoine; Andreacchio, Antonio; Rousset, Marie; Dimeglio, Alain

2018-04-01

The aim of the study is to review the outcome of using the VAC system in children and adolescents who have developed postoperative spinal infection after posterior instrumented spinal fusion, and to evaluate whether this technique is also feasible in patients treated with posterior instrumented fusion with polyester sublaminar bands. A total of 11 out of 118 consecutive children and adolescents (5 males) with deep postoperative spinal infection were identified; infections were categorised as early (acute), delayed (subacute) or late (chronic) according to time of onset. Irrespective of the etiology and the onset, all the deep infections were managed with the reported technique. All the patients had regular clinical and radiological follow-up. Eight out of 11 patients developed an early (72.7%), 2 a delayed (18.2%) and 1 a late deep postoperative infection (9.1%); 7 out of 11 (63.6%) showed severe mental compromise. No statistically significant differences were observed for mean number of VAC dressing changes (p = 0.81) and mean length of hospitalisation comparing patients with early infection versus patients with delayed or late infections (p = 0.32). Mean number of VAC dressing changes (p = 0.02) and mean number of hospitalisation days (p = 0.05) were higher in patients with underlying neurological disorders than in those without, while mean length of hospitalisation was longer in neuromuscular patients. The application of the VAC system, as an adjunct to surgical debridement and adequate antibiotic therapy, is a reliable method for the treatment of postoperative infection in children and adolescents undergoing spinal instrumentation and fusion. It can reduce the need for further complex soft-tissue procedure, removal of hardware with consequent loss of correction, and pseudoarthrosis. Finally, the use of VAC therapy is not contraindicated in patients treated with hybrid constructs with sublaminar bands. III.

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.

Towards automatic pulmonary nodule management in lung cancer screening with deep learning.

Science.gov (United States)

Ciompi, Francesco; Chung, Kaman; van Riel, Sarah J; Setio, Arnaud Arindra Adiyoso; Gerke, Paul K; Jacobs, Colin; Scholten, Ernst Th; Schaefer-Prokop, Cornelia; Wille, Mathilde M W; Marchianò, Alfonso; Pastorino, Ugo; Prokop, Mathias; van Ginneken, Bram

2017-04-19

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.

Effects of defibrotide in patients with chronic deep insufficiency. The PROVEDIS study.

Science.gov (United States)

Coccheri, S; Andreozzi, G M; D'Addato, M; Gensini, G F

2004-06-01

In the present study the effect of defibrotide, an antithrombotic and profibrinolytic agent, was investigated in patients with chronic venous insufficiency (CVI) due to deep vein obstruction and/or reflux (chronic deep vein insufficiency, CDVI). The study was a multicenter, randomized, double blind placebo controlled trial in which only patients with CDVI confirmed by ultrasound were enrolled. All patients were treated with adequate elastic compression and randomized to receive either oral defibrotide (800 mg/die) or matching placebo for 1 year. Patients with active or previous leg ulcer were excluded. A total of 288 patients were randomized and 159 completed the study. At baseline ultrasound investigation, obstructive changes were found in 2/3 of all patients thus ascertaining a post-thrombotic syndrome (PTS). The primary endpoint, ankle circumference, was significantly reduced under defibrotide from day 120 throughout 360. Scores for pain and edema were improved. The number of episodes of superficial thrombophlebitis and deep vein thrombosis was significantly lower under defibrotide (n=2) than under placebo (n=10). The majority of these events occurred in the subset of patients with documented PTS. Treatment with defibrotide in addition to elastic compression in patients with objectively assessed CDVI, mostly due to PTS, resulted in clinical benefits and prevented thrombotic complications harmful to the limb conditions.

Feeding in deep-sea demosponges: Influence of abiotic and biotic factors

Science.gov (United States)

Robertson, Leah M.; Hamel, Jean-François; Mercier, Annie

2017-09-01

In shallow benthic communities, sponges are widely recognized for their ability to contribute to food webs by cycling nutrients and mediating carbon fluxes through filter feeding. In comparison, little is known about filter feeding in deep-sea species and how it may be modulated by environmental conditions. Here, a rare opportunity to maintain live healthy deep-sea sponges for an extended period led to a preliminary experimental study of their feeding metrics. This work focused on demosponges collected from the continental slope of eastern Canada at 1000 m depth. Filtration rates (as clearance of phytoplankton cells) at holding temperature (6 °C) were positively correlated with food particle concentration, ranging on average from 18.8 to 160.6 cells ml-1 h-1 at nominal concentrations of 10,000-40,000 cells ml-1. Cell clearance was not significantly affected by decreasing seawater temperature, from 6 °C to 3 °C or 0 °C, although two of the sponges showed decreased filtration rates. Low pH ( 7.5) and the presence of a predatory sea star markedly depressed or inhibited feeding activity in all sponges tested. While performed under laboratory conditions on a limited number of specimens, this work highlights the possible sensitivity of deep-sea demosponges to various types and levels of biotic and abiotic factors, inferring a consequent vulnerability to natural and anthropogenic disturbances.

Automatic Segmentation and Deep Learning of Bird Sounds

NARCIS (Netherlands)