An interactive end-user software application for a deep-sea photographic database

Digital Repository Service at National Institute of Oceanography (India)

Jaisankar, S.; Sharma, R.

. The software is the first of its kind in deep-sea applications and it also attempts to educate the user about deep-sea photography. The application software is developed by modifying established routines and by creating new routines to save the retrieved...

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/.

The dynamics of biogeographic ranges in the deep sea.

Science.gov (United States)

McClain, Craig R; Hardy, Sarah Mincks

2010-12-07

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

Identification and Removal of Contaminant Sequences From Ribosomal Gene Databases: Lessons From the Census of Deep Life.

Science.gov (United States)

Sheik, Cody S; Reese, Brandi Kiel; Twing, Katrina I; Sylvan, Jason B; Grim, Sharon L; Schrenk, Matthew O; Sogin, Mitchell L; Colwell, Frederick S

2018-01-01

Earth's subsurface environment is one of the largest, yet least studied, biomes on Earth, and many questions remain regarding what microorganisms are indigenous to the subsurface. Through the activity of the Census of Deep Life (CoDL) and the Deep Carbon Observatory, an open access 16S ribosomal RNA gene sequence database from diverse subsurface environments has been compiled. However, due to low quantities of biomass in the deep subsurface, the potential for incorporation of contaminants from reagents used during sample collection, processing, and/or sequencing is high. Thus, to understand the ecology of subsurface microorganisms (i.e., the distribution, richness, or survival), it is necessary to minimize, identify, and remove contaminant sequences that will skew the relative abundances of all taxa in the sample. In this meta-analysis, we identify putative contaminants associated with the CoDL dataset, recommend best practices for removing contaminants from samples, and propose a series of best practices for subsurface microbiology sampling. The most abundant putative contaminant genera observed, independent of evenness across samples, were Propionibacterium , Aquabacterium , Ralstonia , and Acinetobacter . While the top five most frequently observed genera were Pseudomonas , Propionibacterium , Acinetobacter , Ralstonia , and Sphingomonas . The majority of the most frequently observed genera (high evenness) were associated with reagent or potential human contamination. Additionally, in DNA extraction blanks, we observed potential archaeal contaminants, including methanogens, which have not been discussed in previous contamination studies. Such contaminants would directly affect the interpretation of subsurface molecular studies, as methanogenesis is an important subsurface biogeochemical process. Utilizing previously identified contaminant genera, we found that ∼27% of the total dataset were identified as contaminant sequences that likely originate from DNA

Upper extremity deep venous thrombosis after port insertion: What are the risk factors?

Science.gov (United States)

Tabatabaie, Omidreza; Kasumova, Gyulnara G; Kent, Tara S; Eskander, Mariam F; Fadayomi, Ayotunde B; Ng, Sing Chau; Critchlow, Jonathan F; Tawa, Nicholas E; Tseng, Jennifer F

2017-08-01

Totally implantable venous access devices (ports) are widely used, especially for cancer chemotherapy. Although their use has been associated with upper extremity deep venous thrombosis, the risk factors of upper extremity deep venous thrombosis in patients with a port are not studied adequately. The Healthcare Cost and Utilization Project's Florida State Ambulatory Surgery and Services Database was queried between 2007 and 2011 for patients who underwent outpatient port insertion, identified by Current Procedural Terminology code. Patients were followed in the State Ambulatory Surgery and Services Database, State Inpatient Database, and State Emergency Department Database for upper extremity deep venous thrombosis occurrence. The cohort was divided into a test cohort and a validation cohort based on the year of port placement. A multivariable logistic regression model was developed to identify risk factors for upper extremity deep venous thrombosis in patients with a port. The model then was tested on the validation cohort. Of the 51,049 patients in the derivation cohort, 926 (1.81%) developed an upper extremity deep venous thrombosis. On multivariate analysis, independently significant predictors of upper extremity deep venous thrombosis included age deep venous thrombosis (odds ratio = 1.77), all-cause 30-day revisit (odds ratio = 2.36), African American race (versus white; odds ratio = 1.86), and other nonwhite races (odds ratio = 1.35). Additionally, compared with genitourinary malignancies, patients with gastrointestinal (odds ratio = 1.55), metastatic (odds ratio = 1.76), and lung cancers (odds ratio = 1.68) had greater risks of developing an upper extremity deep venous thrombosis. This study identified major risk factors of upper extremity deep venous thrombosis. Further studies are needed to evaluate the appropriateness of thromboprophylaxis in patients at greater risk of upper extremity deep venous thrombosis. Copyright © 2017 Elsevier Inc

The International Deep Brain Stimulation Registry and Database for Gilles de la Tourette Syndrome: How Does it Work?

Directory of Open Access Journals (Sweden)

Wissam eDeeb

2016-04-01

Full Text Available Tourette Syndrome (TS is a neuropsychiatric disease characterized by a combination of motor and vocal tics. Deep brain stimulation (DBS, already widely utilized for Parkinson’s disease and other movement disorders, is an emerging therapy for select and severe cases of TS that are resistant to medication and behavioral therapy. Over the last two decades, DBS has been used experimentally to manage severe TS cases. The results of case reports and small case series have been variable but in general positive. The reported interventions have, however, been variable, and there remain non-standardized selection criteria, various brain targets, differences in hardware, as well as variability in the programming parameters utilized. DBS centers perform only a handful of TS DBS cases each year, making large-scale outcomes difficult to study and to interpret. These limitations, coupled with the variable effect of surgery, and the overall small numbers of TS patients with implanted DBS worldwide, have delayed regulatory agency approval (e.g. FDA and equivalent agencies around the world. The Tourette Association of America, in response to the worldwide need for a more organized and collaborative effort, launched an international TS DBS registry and database. The main goal of the project has been to share data, uncover best practices, improve outcomes, and to provide critical information to regulatory agencies. The international registry and database has improved the communication and collaboration among TS DBS centers worldwide. In this paper we will review some of the key operation details for the international TS DBS database and registry.

An Analysis of Database Replication Technologies with Regard to Deep Space Network Application Requirements

Science.gov (United States)

Connell, Andrea M.

2011-01-01

The Deep Space Network (DSN) has three communication facilities which handle telemetry, commands, and other data relating to spacecraft missions. The network requires these three sites to share data with each other and with the Jet Propulsion Laboratory for processing and distribution. Many database management systems have replication capabilities built in, which means that data updates made at one location will be automatically propagated to other locations. This project examines multiple replication solutions, looking for stability, automation, flexibility, performance, and cost. After comparing these features, Oracle Streams is chosen for closer analysis. Two Streams environments are configured - one with a Master/Slave architecture, in which a single server is the source for all data updates, and the second with a Multi-Master architecture, in which updates originating from any of the servers will be propagated to all of the others. These environments are tested for data type support, conflict resolution, performance, changes to the data structure, and behavior during and after network or server outages. Through this experimentation, it is determined which requirements of the DSN can be met by Oracle Streams and which cannot.

DATA EXTRACTION AND LABEL ASSIGNMENT FOR WEB DATABASES

OpenAIRE

T. Rajesh; T. Prathap; S.Naveen Nambi; A.R. Arunachalam

2015-01-01

Deep Web contents are accessed by queries submitted to Web databases and the returned data records are en wrapped in dynamically generated Web pages (they will be called deep Web pages in this paper). The structured data that Extracting from deep Web pages is a challenging problem due to the underlying intricate structures of such pages. Until now, a too many number of techniques have been proposed to address this problem, but all of them have limitations because they are Web-page-programming...

The cost and performance of utility commercial lighting programs. A report from the Database on Energy Efficiency Programs (DEEP) project

Energy Technology Data Exchange (ETDEWEB)

Eto, J.; Vine, E.; Shown, L.; Sonnenblick, R.; Payne, C. [Lawrence Berkeley Lab., CA (United States). Energy and Environment Div.

1994-05-01

The objective of the Database on Energy Efficiency Programs (DEEP) is to document the measured cost and performance of utility-sponsored, energy-efficiency, demand-side management (DSM) programs. Consistent documentation of DSM programs is a challenging goal because of problems with data consistency, evaluation methodologies, and data reporting formats that continue to limit the usefulness and comparability of individual program results. This first DEEP report investigates the results of 20 recent commercial lighting DSM programs. The report, unlike previous reports of its kind, compares the DSM definitions and methodologies that each utility uses to compute costs and energy savings and then makes adjustments to standardize reported program results. All 20 programs were judged cost-effective when compared to avoided costs in their local areas. At an average cost of 3.9{cents}/kWh, however, utility-sponsored energy efficiency programs are not ``too cheap to meter.`` While it is generally agreed upon that utilities must take active measures to minimize the costs and rate impacts of DSM programs, the authors believe that these activities will be facilitated by industry adoption of standard definitions and reporting formats, so that the best program designs can be readily identified and adopted.

Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance

International Nuclear Information System (INIS)

Lee, Sang Hoon; Hong, Tianzhen; Piette, Mary Ann; Sawaya, Geof; Chen, Yixing; Taylor-Lange, Sarah C.

2015-01-01

Small and medium-sized commercial buildings can be retrofitted to significantly reduce their energy use, however it is a huge challenge as owners usually lack of the expertise and resources to conduct detailed on-site energy audit to identify and evaluate cost-effective energy technologies. This study presents a DEEP (database of energy efficiency performance) that provides a direct resource for quick retrofit analysis of commercial buildings. DEEP, compiled from the results of about ten million EnergyPlus simulations, enables an easy screening of ECMs (energy conservation measures) and retrofit analysis. The simulations utilize prototype models representative of small and mid-size offices and retails in California climates. In the formulation of DEEP, large scale EnergyPlus simulations were conducted on high performance computing clusters to evaluate hundreds of individual and packaged ECMs covering envelope, lighting, heating, ventilation, air-conditioning, plug-loads, and service hot water. The architecture and simulation environment to create DEEP is flexible and can expand to cover additional building types, additional climates, and new ECMs. In this study DEEP is integrated into a web-based retrofit toolkit, the Commercial Building Energy Saver, which provides a platform for energy retrofit decision making by querying DEEP and unearthing recommended ECMs, their estimated energy savings and financial payback. - Highlights: • A DEEP (database of energy efficiency performance) supports building retrofit. • DEEP is an SQL database with pre-simulated results from 10 million EnergyPlus runs. • DEEP covers 7 building types, 6 vintages, 16 climates, and 100 energy measures. • DEEP accelerates retrofit of small commercial buildings to save energy use and cost. • DEEP can be expanded and integrated with third-party energy software tools.

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…

Classification of ECG beats using deep belief network and active learning.

Science.gov (United States)

G, Sayantan; T, Kien P; V, Kadambari K

2018-04-12

A new semi-supervised approach based on deep learning and active learning for classification of electrocardiogram signals (ECG) is proposed. The objective of the proposed work is to model a scientific method for classification of cardiac irregularities using electrocardiogram beats. The model follows the Association for the Advancement of medical instrumentation (AAMI) standards and consists of three phases. In phase I, feature representation of ECG is learnt using Gaussian-Bernoulli deep belief network followed by a linear support vector machine (SVM) training in the consecutive phase. It yields three deep models which are based on AAMI-defined classes, namely N, V, S, and F. In the last phase, a query generator is introduced to interact with the expert to label few beats to improve accuracy and sensitivity. The proposed approach depicts significant improvement in accuracy with minimal queries posed to the expert and fast online training as tested on the MIT-BIH Arrhythmia Database and the MIT-BIH Supra-ventricular Arrhythmia Database (SVDB). With 100 queries labeled by the expert in phase III, the method achieves an accuracy of 99.5% in "S" versus all classifications (SVEB) and 99.4% accuracy in "V " versus all classifications (VEB) on MIT-BIH Arrhythmia Database. In a similar manner, it is attributed that an accuracy of 97.5% for SVEB and 98.6% for VEB on SVDB database is achieved respectively. Graphical Abstract Reply- Deep belief network augmented by active learning for efficient prediction of arrhythmia.

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...

Facial expression recognition based on improved deep belief networks

Science.gov (United States)

Wu, Yao; Qiu, Weigen

2017-08-01

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

JAMSTEC E-library of Deep-sea Images (J-EDI) Realizes a Virtual Journey to the Earth's Unexplored Deep Ocean

Science.gov (United States)

Sasaki, T.; Azuma, S.; Matsuda, S.; Nagayama, A.; Ogido, M.; Saito, H.; Hanafusa, Y.

2016-12-01

The Japan Agency for Marine-Earth Science and Technology (JAMSTEC) archives a large amount of deep-sea research videos and photos obtained by JAMSTEC's research submersibles and vehicles with cameras. The web site "JAMSTEC E-library of Deep-sea Images : J-EDI" (http://www.godac.jamstec.go.jp/jedi/e/) has made videos and photos available to the public via the Internet since 2011. Users can search for target videos and photos by keywords, easy-to-understand icons, and dive information at J-EDI because operating staffs classify videos and photos as to contents, e.g. living organism and geological environment, and add comments to them.Dive survey data including videos and photos are not only valiant academically but also helpful for education and outreach activities. With the aim of the improvement of visibility for broader communities, we added new functions of 3-dimensional display synchronized various dive survey data with videos in this year.New Functions Users can search for dive survey data by 3D maps with plotted dive points using the WebGL virtual map engine "Cesium". By selecting a dive point, users can watch deep-sea videos and photos and associated environmental data, e.g. water temperature, salinity, rock and biological sample photos, obtained by the dive survey. Users can browse a dive track visualized in 3D virtual spaces using the WebGL JavaScript library. By synchronizing this virtual dive track with videos, users can watch deep-sea videos recorded at a point on a dive track. Users can play an animation which a submersible-shaped polygon automatically traces a 3D virtual dive track and displays of dive survey data are synchronized with tracing a dive track. Users can directly refer to additional information of other JAMSTEC data sites such as marine biodiversity database, marine biological sample database, rock sample database, and cruise and dive information database, on each page which a 3D virtual dive track is displayed. A 3D visualization of a dive

Design of multi-tiered database application based on CORBA component in SDUV-FEL system

International Nuclear Information System (INIS)

Sun Xiaoying; Shen Liren; Dai Zhimin

2004-01-01

The drawback of usual two-tiered database architecture was analyzed and the Shanghai Deep Ultraviolet-Free Electron Laser database system under development was discussed. A project for realizing the multi-tiered database architecture based on common object request broker architecture (CORBA) component and middleware model constructed by C++ was presented. A magnet database was given to exhibit the design of the CORBA component. (authors)

Deep Sea Coral voucher sequence dataset - Identification of deep-sea corals collected during the 2009 - 2014 West Coast Groundfish Bottom Trawl Survey

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Data for this project resides in the West Coast Groundfish Bottom Trawl Survey Database. Deep-sea corals are often components of trawling bycatch, though their...

Factors governing the deep ventilation of the Red Sea

KAUST Repository

Papadopoulos, Vassilis P.; Zhan, Peng; Sofianos, Sarantis S.; Raitsos, Dionysios E.; Qurban, Mohammed; Abualnaja, Yasser; Bower, Amy; Kontoyiannis, Harilaos; Pavlidou, Alexandra; Asharaf T.T., Mohamed; Zarokanellos, Nikolaos; Hoteit, Ibrahim

2015-01-01

A variety of data based on hydrographic measurements, satellite observations, reanalysis databases, and meteorological observations are used to explore the interannual variability and factors governing the deep water formation in the northern Red

Fund Finder: A case study of database-to-ontology mapping

OpenAIRE

Barrasa Rodríguez, Jesús; Corcho, Oscar; Gómez-Pérez, A.

2003-01-01

The mapping between databases and ontologies is a basic problem when trying to "upgrade" deep web content to the semantic web. Our approach suggests the declarative definition of mappings as a way to achieve domain independency and reusability. A specific language (expressive enough to cover some real world mapping situations like lightly structured databases or not 1st normal form ones) is defined for this purpose. Along with this mapping description language, the ODEMapster processor is in ...

Multiscale deep features learning for land-use scene recognition

Science.gov (United States)

Yuan, Baohua; Li, Shijin; Li, Ning

2018-01-01

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

Deep-sea Hexactinellida (Porifera) of the Weddell Sea

Science.gov (United States)

Janussen, Dorte; Tabachnick, Konstantin R.; Tendal, Ole S.

2004-07-01

New Hexactinellida from the deep Weddel Sea are described. This moderately diverse hexactinellid fauna includes 14 species belonging to 12 genera, of which five species and one subgenus are new to science: Periphragella antarctica n. sp., Holascus pseudostellatus n. sp., Caulophacus (Caulophacus) discohexactinus n. sp., C. ( Caulodiscus) brandti n. sp., C. ( Oxydiscus) weddelli n. sp., and C. ( Oxydiscus) n. subgen. So far, 20 hexactinellid species have been reported from the deep Weddell Sea, 15 are known from the northern part and 10 only from here, while 10 came from the southern area, and five of these only from there. However, this apparent high "endemism" of Antarctic hexactinellid sponges is most likely the result of severe undersampling of the deep-sea fauna. We find no reason to believe that a division between an oceanic and a more continental group of species exists. The current poor database indicates that a substantial part of the deep hexactinellid fauna of the Weddell Sea is shared with other deep-sea regions, but it does not indicate a special biogeographic relationship with any other ocean.

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

Science.gov (United States)

Hsu, N. C.; Jeong, M.-J.; Bettenhausen, C.; Sayer, A. M.; Hansell, R.; Seftor, C. S.; Huang, J.; Tsay, S.-C.

2013-01-01

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas.

Deep Learning based Super-Resolution for Improved Action Recognition

DEFF Research Database (Denmark)

Nasrollahi, Kamal; Guerrero, Sergio Escalera; Rasti, Pejman

2015-01-01

with results of a state-of- the-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate...

Multi-level deep supervised networks for retinal vessel segmentation.

Science.gov (United States)

Mo, Juan; Zhang, Lei

2017-12-01

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

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/.

Deep learning of unsteady laminar flow over a cylinder

Science.gov (United States)

Lee, Sangseung; You, Donghyun

2017-11-01

Unsteady flow over a circular cylinder is reconstructed using deep learning with a particular emphasis on elucidating the potential of learning the solution of the Navier-Stokes equations. A deep neural network (DNN) is employed for deep learning, while numerical simulations are conducted to produce training database. Instantaneous and mean flow fields which are reconstructed by deep learning are compared with the simulation results. Fourier transform of flow variables has been conducted to validate the ability of DNN to capture both amplitudes and frequencies of flow motions. Basis decomposition of learned flow is performed to understand the underlying mechanisms of learning flow through DNN. The present study suggests that a deep learning technique can be utilized for reconstruction and, potentially, for prediction of fluid flow instead of solving the Navier-Stokes equations. This work was supported by the National Research Foundation of Korea(NRF) Grant funded by the Korea government(Ministry of Science, ICT and Future Planning) (No. 2014R1A2A1A11049599, No. 2015R1A2A1A15056086, No. 2016R1E1A2A01939553).

The MIND PALACE: A Multi-Spectral Imaging and Spectroscopy Database for Planetary Science

Science.gov (United States)

Eshelman, E.; Doloboff, I.; Hara, E. K.; Uckert, K.; Sapers, H. M.; Abbey, W.; Beegle, L. W.; Bhartia, R.

2017-12-01

The Multi-Instrument Database (MIND) is the web-based home to a well-characterized set of analytical data collected by a suite of deep-UV fluorescence/Raman instruments built at the Jet Propulsion Laboratory (JPL). Samples derive from a growing body of planetary surface analogs, mineral and microbial standards, meteorites, spacecraft materials, and other astrobiologically relevant materials. In addition to deep-UV spectroscopy, datasets stored in MIND are obtained from a variety of analytical techniques obtained over multiple spatial and spectral scales including electron microscopy, optical microscopy, infrared spectroscopy, X-ray fluorescence, and direct fluorescence imaging. Multivariate statistical analysis techniques, primarily Principal Component Analysis (PCA), are used to guide interpretation of these large multi-analytical spectral datasets. Spatial co-referencing of integrated spectral/visual maps is performed using QGIS (geographic information system software). Georeferencing techniques transform individual instrument data maps into a layered co-registered data cube for analysis across spectral and spatial scales. The body of data in MIND is intended to serve as a permanent, reliable, and expanding database of deep-UV spectroscopy datasets generated by this unique suite of JPL-based instruments on samples of broad planetary science interest.

A Dataset of Deep-Sea Fishes Surveyed by Research Vessels in the Waters around Taiwan

Directory of Open Access Journals (Sweden)

Kwang-Tsao Shao

2014-12-01

Full Text Available The study of deep-sea fish fauna is hampered by a lack of data due to the difficulty and high cost incurred in its surveys and collections. Taiwan is situated along the edge of the Eurasia fig, at the junction of three Large Marine Ecosystems or Ecoregions of the East China Sea, South China Sea and the Philippines. As nearly two-thirds of its surrounding marine ecosystems are deep-sea environments, Taiwan is expected to hold a rich diversity of deep-sea fish. However, in the past, no research vessels were employed to collect fish data on site. Only specimens, caught by bottom trawl fishing in the waters hundreds of meters deep and missing precise locality information, were collected from Dasi and Donggang fishing harbors. Began in 2001, with the support of National Science Council, research vessels were made available to take on the task of systematically collecting deep-sea fish specimens and occurrence records in the waters surrounding Taiwan. By the end of 2006, a total of 3,653 specimens, belonging to 26 orders, 88 families, 198 genera and 366 species, were collected in addition to data such as sampling site geographical coordinates and water depth, and fish body length and weight. The information, all accessible from the “Database of Taiwan’s Deep-Sea Fauna and Its Distribution (http://deepsea.biodiv.tw/” as part of the “Fish Database of Taiwan,” can benefit the study of temporal and spatial changes in distribution and abundance of fish fauna in the context of global deep-sea biodiversity.

Speaker emotion recognition: from classical classifiers to deep neural networks

Science.gov (United States)

Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

2018-04-01

Speaker emotion recognition is considered among the most challenging tasks in recent years. In fact, automatic systems for security, medicine or education can be improved when considering the speech affective state. In this paper, a twofold approach for speech emotion classification is proposed. At the first side, a relevant set of features is adopted, and then at the second one, numerous supervised training techniques, involving classic methods as well as deep learning, are experimented. Experimental results indicate that deep architecture can improve classification performance on two affective databases, the Berlin Dataset of Emotional Speech and the SAVEE Dataset Surrey Audio-Visual Expressed Emotion.

The development of a new database of gas emissions: MAGA, a collaborative web environment for collecting data

Science.gov (United States)

Cardellini, C.; Chiodini, G.; Frigeri, A.; Bagnato, E.; Aiuppa, A.; McCormick, B.

2013-12-01

The data on volcanic and non-volcanic gas emissions available online are, as today, incomplete and most importantly, fragmentary. Hence, there is need for common frameworks to aggregate available data, in order to characterize and quantify the phenomena at various spatial and temporal scales. Building on the Googas experience we are now extending its capability, particularly on the user side, by developing a new web environment for collecting and publishing data. We have started to create a new and detailed web database (MAGA: MApping GAs emissions) for the deep carbon degassing in the Mediterranean area. This project is part of the Deep Earth Carbon Degassing (DECADE) research initiative, lunched in 2012 by the Deep Carbon Observatory (DCO) to improve the global budget of endogenous carbon from volcanoes. MAGA database is planned to complement and integrate the work in progress within DECADE in developing CARD (Carbon Degassing) database. MAGA database will allow researchers to insert data interactively and dynamically into a spatially referred relational database management system, as well as to extract data. MAGA kicked-off with the database set up and a complete literature survey on publications on volcanic gas fluxes, by including data on active craters degassing, diffuse soil degassing and fumaroles both from dormant closed-conduit volcanoes (e.g., Vulcano, Phlegrean Fields, Santorini, Nysiros, Teide, etc.) and open-vent volcanoes (e.g., Etna, Stromboli, etc.) in the Mediterranean area and Azores. For each geo-located gas emission site, the database holds images and description of the site and of the emission type (e.g., diffuse emission, plume, fumarole, etc.), gas chemical-isotopic composition (when available), gas temperature and gases fluxes magnitude. Gas sampling, analysis and flux measurement methods are also reported together with references and contacts to researchers expert of the site. Data can be accessed on the network from a web interface or as

MAGA, a new database of gas natural emissions: a collaborative web environment for collecting data.

Science.gov (United States)

Cardellini, Carlo; Chiodini, Giovanni; Frigeri, Alessandro; Bagnato, Emanuela; Frondini, Francesco; Aiuppa, Alessandro

2014-05-01

The data on volcanic and non-volcanic gas emissions available online are, as today, are incomplete and most importantly, fragmentary. Hence, there is need for common frameworks to aggregate available data, in order to characterize and quantify the phenomena at various scales. A new and detailed web database (MAGA: MApping GAs emissions) has been developed, and recently improved, to collect data on carbon degassing form volcanic and non-volcanic environments. MAGA database allows researchers to insert data interactively and dynamically into a spatially referred relational database management system, as well as to extract data. MAGA kicked-off with the database set up and with the ingestion in to the database of the data from: i) a literature survey on publications on volcanic gas fluxes including data on active craters degassing, diffuse soil degassing and fumaroles both from dormant closed-conduit volcanoes (e.g., Vulcano, Phlegrean Fields, Santorini, Nysiros, Teide, etc.) and open-vent volcanoes (e.g., Etna, Stromboli, etc.) in the Mediterranean area and Azores, and ii) the revision and update of Googas database on non-volcanic emission of the Italian territory (Chiodini et al., 2008), in the framework of the Deep Earth Carbon Degassing (DECADE) research initiative of the Deep Carbon Observatory (DCO). For each geo-located gas emission site, the database holds images and description of the site and of the emission type (e.g., diffuse emission, plume, fumarole, etc.), gas chemical-isotopic composition (when available), gas temperature and gases fluxes magnitude. Gas sampling, analysis and flux measurement methods are also reported together with references and contacts to researchers expert of each site. In this phase data can be accessed on the network from a web interface, and data-driven web service, where software clients can request data directly from the database, are planned to be implemented shortly. This way Geographical Information Systems (GIS) and

Part-based deep representation for product tagging and search

Science.gov (United States)

Chen, Keqing

2017-06-01

Despite previous studies, tagging and indexing the product images remain challenging due to the large inner-class variation of the products. In the traditional methods, the quantized hand-crafted features such as SIFTs are extracted as the representation of the product images, which are not discriminative enough to handle the inner-class variation. For discriminative image representation, this paper firstly presents a novel deep convolutional neural networks (DCNNs) architect true pre-trained on a large-scale general image dataset. Compared to the traditional features, our DCNNs representation is of more discriminative power with fewer dimensions. Moreover, we incorporate the part-based model into the framework to overcome the negative effect of bad alignment and cluttered background and hence the descriptive ability of the deep representation is further enhanced. Finally, we collect and contribute a well-labeled shoe image database, i.e., the TBShoes, on which we apply the part-based deep representation for product image tagging and search, respectively. The experimental results highlight the advantages of the proposed part-based deep representation.

Environmental studies for mining of deep-sea polymetallic nodules - Accomplishments and future plans

Digital Repository Service at National Institute of Oceanography (India)

Sharma, R.

on marine ecosystem, the project on ‘EIA studies for nodule mining in CIB’ was initiated in 1996, under the national programme on polymetallic nodules funded by the Dept. of Ocean Development. Mining of the deep-sea minerals [1] is expected to alter... for the future • Development of predictive ecosystem models • Creation of environmental database • Evaluating the biogeochemical coupling of biota with deep-sea ecosystem • Development of environment management plan for nodule mining References...

ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

Energy Technology Data Exchange (ETDEWEB)

Goh, Garrett B.; Siegel, Charles M.; Vishnu, Abhinav; Hodas, Nathan O.

2017-12-08

With access to large datasets, deep neural networks through representation learning have been able to identify patterns from raw data, achieving human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and with a multitude of chemical properties of interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed from the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical tasks that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models, contemporary MLP models that trains on molecular fingerprints, and it matches the performance of the ConvGraph algorithm, the current state-of-the-art. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a universal “plug-and-play” deep neural network, which accelerates the deployment of deep neural networks for the prediction of novel small-molecule chemical properties.

The DEEP-South: Scheduling and Data Reduction Software System

Science.gov (United States)

Yim, Hong-Suh; Kim, Myung-Jin; Bae, Youngho; Moon, Hong-Kyu; Choi, Young-Jun; Roh, Dong-Goo; the DEEP-South Team

2015-08-01

The DEep Ecliptic Patrol of the Southern sky (DEEP-South), started in October 2012, is currently in test runs with the first Korea Microlensing Telescope Network (KMTNet) 1.6 m wide-field telescope located at CTIO in Chile. While the primary objective for the DEEP-South is physical characterization of small bodies in the Solar System, it is expected to discover a large number of such bodies, many of them previously unknown.An automatic observation planning and data reduction software subsystem called "The DEEP-South Scheduling and Data reduction System" (the DEEP-South SDS) is currently being designed and implemented for observation planning, data reduction and analysis of huge amount of data with minimum human interaction. The DEEP-South SDS consists of three software subsystems: the DEEP-South Scheduling System (DSS), the Local Data Reduction System (LDR), and the Main Data Reduction System (MDR). The DSS manages observation targets, makes decision on target priority and observation methods, schedules nightly observations, and archive data using the Database Management System (DBMS). The LDR is designed to detect moving objects from CCD images, while the MDR conducts photometry and reconstructs lightcurves. Based on analysis made at the LDR and the MDR, the DSS schedules follow-up observation to be conducted at other KMTNet stations. In the end of 2015, we expect the DEEP-South SDS to achieve a stable operation. We also have a plan to improve the SDS to accomplish finely tuned observation strategy and more efficient data reduction in 2016.

Focused Crawling of the Deep Web Using Service Class Descriptions

Energy Technology Data Exchange (ETDEWEB)

Rocco, D; Liu, L; Critchlow, T

2004-06-21

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

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...

A novel biomedical image indexing and retrieval system via deep preference learning.

Science.gov (United States)

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state

DATABASES DEVELOPED IN INDIA FOR BIOLOGICAL SCIENCES

Directory of Open Access Journals (Sweden)

Gitanjali Yadav

2017-09-01

databases have also helped in development of novel data mining methods, prediction strategies and data driven application software or web servers. In this article, we give an overview of biological databases developed in India and their impact on data driven research in biology. We also provide some suggestions for planning training programs in biological data science for making transitions to big data revolution in biology by combining advanced techniques like Deep Learning with biological big data.

Drag Reduction of an Airfoil Using Deep Learning

Science.gov (United States)

Jiang, Chiyu; Sun, Anzhu; Marcus, Philip

2017-11-01

We reduced the drag of a 2D airfoil by starting with a NACA-0012 airfoil and used deep learning methods. We created a database which consists of simulations of 2D external flow over randomly generated shapes. We then developed a machine learning framework for external flow field inference given input shapes. Past work which utilized machine learning in Computational Fluid Dynamics focused on estimations of specific flow parameters, but this work is novel in the inference of entire flow fields. We further showed that learned flow patterns are transferable to cases that share certain similarities. This study illustrates the prospects of deeper integration of data-based modeling into current CFD simulation frameworks for faster flow inference and more accurate flow modeling.

Database Description - Trypanosomes Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Trypanosomes Database Database Description General information of database Database name Trypanosomes Database...stitute of Genetics Research Organization of Information and Systems Yata 1111, Mishima, Shizuoka 411-8540, JAPAN E mail: Database...y Name: Trypanosoma Taxonomy ID: 5690 Taxonomy Name: Homo sapiens Taxonomy ID: 9606 Database description The... Article title: Author name(s): Journal: External Links: Original website information Database maintenance s...DB (Protein Data Bank) KEGG PATHWAY Database DrugPort Entry list Available Query search Available Web servic

Evaluating deep learning architectures for Speech Emotion Recognition.

Science.gov (United States)

Fayek, Haytham M; Lech, Margaret; Cavedon, Lawrence

2017-08-01

Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame-based formulation to SER that relies on minimal speech processing and end-to-end deep learning to model intra-utterance dynamics. We use the proposed SER system to empirically explore feed-forward and recurrent neural network architectures and their variants. Experiments conducted illuminate the advantages and limitations of these architectures in paralinguistic speech recognition and emotion recognition in particular. As a result of our exploration, we report state-of-the-art results on the IEMOCAP database for speaker-independent SER and present quantitative and qualitative assessments of the models' performances. Copyright © 2017 Elsevier Ltd. All rights reserved.

Atlantic Canada's energy research and development website and database

International Nuclear Information System (INIS)

2005-01-01

Petroleum Research Atlantic Canada maintains a website devoted to energy research and development in Atlantic Canada. The site can be viewed on the world wide web at www.energyresearch.ca. It includes a searchable database with information about researchers in Nova Scotia, their projects and published materials on issues related to hydrocarbons, alternative energy technologies, energy efficiency, climate change, environmental impacts and policy. The website also includes links to research funding agencies, external related databases and related energy organizations around the world. Nova Scotia-based users are invited to submit their academic, private or public research to the site. Before being uploaded into the database, a site administrator reviews and processes all new information. Users are asked to identify their areas of interest according to the following research categories: alternative or renewable energy technologies; climate change; coal; computer applications; economics; energy efficiency; environmental impacts; geology; geomatics; geophysics; health and safety; human factors; hydrocarbons; meteorology and oceanology (metocean) activities; petroleum operations in deep and shallow waters; policy; and power generation and supply. The database can be searched 5 ways according to topic, researchers, publication, projects or funding agency. refs., tabs., figs

Database Description - SKIP Stemcell Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us SKIP Stemcell Database Database Description General information of database Database name SKIP Stemcell Database...rsity Journal Search: Contact address http://www.skip.med.keio.ac.jp/en/contact/ Database classification Human Genes and Diseases Dat...abase classification Stemcell Article Organism Taxonomy Name: Homo sapiens Taxonomy ID: 9606 Database...ks: Original website information Database maintenance site Center for Medical Genetics, School of medicine, ...lable Web services Not available URL of Web services - Need for user registration Not available About This Database Database

Database Description - Arabidopsis Phenome Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Arabidopsis Phenome Database Database Description General information of database Database n... BioResource Center Hiroshi Masuya Database classification Plant databases - Arabidopsis thaliana Organism T...axonomy Name: Arabidopsis thaliana Taxonomy ID: 3702 Database description The Arabidopsis thaliana phenome i...heir effective application. We developed the new Arabidopsis Phenome Database integrating two novel database...seful materials for their experimental research. The other, the “Database of Curated Plant Phenome” focusing

A Global Survey of Deep Underground Facilities; Examples of Geotechnical and Engineering Capabilities, Achievements, Challenges (Mines, Shafts, Tunnels, Boreholes, Sites and Underground Facilities for Nuclear Waste and Physics R&D): A Guide to Interactive Global Map Layers, Table Database, References and Notes

International Nuclear Information System (INIS)

Tynan, Mark C.; Russell, Glenn P.; Perry, Frank V.; Kelley, Richard E.; Champenois, Sean T.

2017-01-01

These associated tables, references, notes, and report present a synthesis of some notable geotechnical and engineering information used to create four interactive layer maps for selected: 1) deep mines and shafts; 2) existing, considered or planned radioactive waste management deep underground studies or disposal facilities 3) deep large diameter boreholes, and 4) physics underground laboratories and facilities from around the world. These data are intended to facilitate user access to basic information and references regarding “deep underground” facilities, history, activities, and plans. In general, the interactive maps and database provide each facility’s approximate site location, geology, and engineered features (e.g.: access, geometry, depth, diameter, year of operations, groundwater, lithology, host unit name and age, basin; operator, management organization, geographic data, nearby cultural features, other). Although the survey is not comprehensive, it is representative of many of the significant existing and historical underground facilities discussed in the literature addressing radioactive waste management and deep mined geologic disposal safety systems. The global survey is intended to support and to inform: 1) interested parties and decision makers; 2) radioactive waste disposal and siting option evaluations, and 3) safety case development applicable to any mined geologic disposal facility as a demonstration of historical and current engineering and geotechnical capabilities available for use in deep underground facility siting, planning, construction, operations and monitoring.

A Global Survey of Deep Underground Facilities; Examples of Geotechnical and Engineering Capabilities, Achievements, Challenges (Mines, Shafts, Tunnels, Boreholes, Sites and Underground Facilities for Nuclear Waste and Physics R&D): A Guide to Interactive Global Map Layers, Table Database, References and Notes

Energy Technology Data Exchange (ETDEWEB)

Tynan, Mark C. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Russell, Glenn P. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Perry, Frank V. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kelley, Richard E. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Champenois, Sean T. [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2017-06-13

These associated tables, references, notes, and report present a synthesis of some notable geotechnical and engineering information used to create four interactive layer maps for selected: 1) deep mines and shafts; 2) existing, considered or planned radioactive waste management deep underground studies or disposal facilities 3) deep large diameter boreholes, and 4) physics underground laboratories and facilities from around the world. These data are intended to facilitate user access to basic information and references regarding “deep underground” facilities, history, activities, and plans. In general, the interactive maps and database provide each facility’s approximate site location, geology, and engineered features (e.g.: access, geometry, depth, diameter, year of operations, groundwater, lithology, host unit name and age, basin; operator, management organization, geographic data, nearby cultural features, other). Although the survey is not comprehensive, it is representative of many of the significant existing and historical underground facilities discussed in the literature addressing radioactive waste management and deep mined geologic disposal safety systems. The global survey is intended to support and to inform: 1) interested parties and decision makers; 2) radioactive waste disposal and siting option evaluations, and 3) safety case development applicable to any mined geologic disposal facility as a demonstration of historical and current engineering and geotechnical capabilities available for use in deep underground facility siting, planning, construction, operations and monitoring.

A federated information management system for the Deep Space Network. M.S. Thesis - Univ. of Southern California

Science.gov (United States)

Dobinson, E.

1982-01-01

General requirements for an information management system for the deep space network (DSN) are examined. A concise review of available database management system technology is presented. It is recommended that a federation of logically decentralized databases be implemented for the Network Information Management System of the DSN. Overall characteristics of the federation are specified, as well as reasons for adopting this approach.

Ship space to database: emerging infrastructures for studies of the deep subseafloor biosphere

Directory of Open Access Journals (Sweden)

Peter T. Darch

2016-11-01

Full Text Available Background An increasing array of scientific fields face a “data deluge.” However, in many fields data are scarce, with implications for their epistemic status and ability to command funding. Consequently, they often attempt to develop infrastructure for data production, management, curation, and circulation. A component of a knowledge infrastructure may serve one or more scientific domains. Further, a single domain may rely upon multiple infrastructures simultaneously. Studying how domains negotiate building and accessing scarce infrastructural resources that they share with other domains will shed light on how knowledge infrastructures shape science. Methods We conducted an eighteen-month, qualitative study of scientists studying the deep subseafloor biosphere, focusing on the Center for Dark Energy Biosphere Investigations (C-DEBI and the Integrated Ocean Drilling Program (IODP and its successor, the International Ocean Discovery Program (IODP2. Our methods comprised ethnographic observation, including eight months embedded in a laboratory, interviews (n = 49, and document analysis. Results Deep subseafloor biosphere research is an emergent domain. We identified two reasons for the domain’s concern with data scarcity: limited ability to pursue their research objectives, and the epistemic status of their research. Domain researchers adopted complementary strategies to acquire more data. One was to establish C-DEBI as an infrastructure solely for their domain. The second was to use C-DEBI as a means to gain greater access to, and reconfigure, IODP/IODP2 to their advantage. IODP/IODP2 functions as infrastructure for multiple scientific domains, which creates competition for resources. C-DEBI is building its own data management infrastructure, both to acquire more data from IODP and to make better use of data, once acquired. Discussion Two themes emerge. One is data scarcity, which can be understood only in relation to a domain�

Deep water recycling through time.

Science.gov (United States)

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

2014-11-01

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

Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

Science.gov (United States)

Sun, Wenqing; Zheng, Bin; Qian, Wei

2017-10-01

This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

Deep brain stimulation for Tourette syndrome.

Science.gov (United States)

Kim, Won; Pouratian, Nader

2014-01-01

Gilles de la Tourette syndrome is a movement disorder characterized by repetitive stereotyped motor and phonic movements with varying degrees of psychiatric comorbidity. Deep brain stimulation (DBS) has emerged as a novel therapeutic intervention for patients with refractory Tourette syndrome. Since 1999, more than 100 patients have undergone DBS at various targets within the corticostriatothalamocortical network thought to be implicated in the underlying pathophysiology of Tourette syndrome. Future multicenter clinical trials and the use of a centralized online database to compare the results are necessary to determine the efficacy of DBS for Tourette syndrome. Copyright © 2014 Elsevier Inc. All rights reserved.

EXPANDING ACADEMIC VOCABULARY WITH AN INTERACTIVE ON-LINE DATABASE

Directory of Open Access Journals (Sweden)

Marlise Horst

2005-05-01

Full Text Available University students used a set of existing and purpose-built on-line tools for vocabulary learning in an experimental ESL course. The resources included concordance, dictionary, cloze-builder, hypertext, and a database with interactive self-quizzing feature (all freely available at www.lextutor.ca. The vocabulary targeted for learning consisted of (a Coxhead's (2000 Academic Word List, a list of items that occur frequently in university textbooks, and (b unfamiliar words students had met in academic texts and selected for entry into the class database. The suite of tools were designed to foster retention by engaging learners in deep processing, an aspect that is often described as missing in computer exercises for vocabulary learning. Database entries were examined to determine whether context sentences supported word meanings adequately and whether entered words reflected the unavailability of cognates in the various first languages of the participants. Pre- and post-treatment performance on tests of knowledge of words targeted for learning in the course were compared to establish learning gains. Regression analyses investigated connections between use of specific computer tools and gains.

Transport and Environment Database System (TRENDS): Maritime Air Pollutant Emission Modelling

DEFF Research Database (Denmark)

Georgakaki, Aliki; Coffey, R. A.; Lock, G.

2003-01-01

This paper reports the development of the maritime module within the framework of the TRENDS project. A detailed database has been constructed, which includes all stages of the energy consumption and air pollutant emission calculations. The technical assumptions and factors incorporated in the da...... ¿ short sea or deep-sea shipping. Key Words: Air Pollution, Maritime Transport, Air Pollutant Emissions......This paper reports the development of the maritime module within the framework of the TRENDS project. A detailed database has been constructed, which includes all stages of the energy consumption and air pollutant emission calculations. The technical assumptions and factors incorporated...... encountered since the statistical data collection was not undertaken with a view to this purpose are mentioned. Examples of the results obtained by the database are presented. These include detailed air pollutant emission results per port and vessel type, to aggregate results for different types of movements...

Big Data and Total Hip Arthroplasty: How Do Large Databases Compare?

Science.gov (United States)

Bedard, Nicholas A; Pugely, Andrew J; McHugh, Michael A; Lux, Nathan R; Bozic, Kevin J; Callaghan, John J

2018-01-01

Use of large databases for orthopedic research has become extremely popular in recent years. Each database varies in the methods used to capture data and the population it represents. The purpose of this study was to evaluate how these databases differed in reported demographics, comorbidities, and postoperative complications for primary total hip arthroplasty (THA) patients. Primary THA patients were identified within National Surgical Quality Improvement Programs (NSQIP), Nationwide Inpatient Sample (NIS), Medicare Standard Analytic Files (MED), and Humana administrative claims database (HAC). NSQIP definitions for comorbidities and complications were matched to corresponding International Classification of Diseases, 9th Revision/Current Procedural Terminology codes to query the other databases. Demographics, comorbidities, and postoperative complications were compared. The number of patients from each database was 22,644 in HAC, 371,715 in MED, 188,779 in NIS, and 27,818 in NSQIP. Age and gender distribution were clinically similar. Overall, there was variation in prevalence of comorbidities and rates of postoperative complications between databases. As an example, NSQIP had more than twice the obesity than NIS. HAC and MED had more than 2 times the diabetics than NSQIP. Rates of deep infection and stroke 30 days after THA had more than 2-fold difference between all databases. Among databases commonly used in orthopedic research, there is considerable variation in complication rates following THA depending upon the database used for analysis. It is important to consider these differences when critically evaluating database research. Additionally, with the advent of bundled payments, these differences must be considered in risk adjustment models. Copyright © 2017 Elsevier Inc. All rights reserved.

Using Supervised Deep Learning for Human Age Estimation Problem

Science.gov (United States)

Drobnyh, K. A.; Polovinkin, A. N.

2017-05-01

Automatic facial age estimation is a challenging task upcoming in recent years. In this paper, we propose using the supervised deep learning features to improve an accuracy of the existing age estimation algorithms. There are many approaches solving the problem, an active appearance model and the bio-inspired features are two of them which showed the best accuracy. For experiments we chose popular publicly available FG-NET database, which contains 1002 images with a broad variety of light, pose, and expression. LOPO (leave-one-person-out) method was used to estimate the accuracy. Experiments demonstrated that adding supervised deep learning features has improved accuracy for some basic models. For example, adding the features to an active appearance model gave the 4% gain (the error decreased from 4.59 to 4.41).

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.

A deep knowledge architecture for intelligent support of nuclear waste transportation decisions

International Nuclear Information System (INIS)

Batra, D.; Bowen, W.M.; Hill, T.R.; Weeks, K.D.

1988-01-01

The concept of intelligent decision support has been discussed and explored in several recent papers, one of which has suggested the use of a Deep Knowledge Architecture. This paper explores this concept through application to a specific decision environment. The complex problems involved in nuclear waste disposal decisions provide an excellent test case. The resulting architecture uses an integrated, multi-level model base to represent the deep knowledge of the problem. Combined with the surface level knowledge represented by the database, the proposed knowledge base complements that of the decision-maker, allowing analysis at a range of levels of decisions which may also occur at a range of levels

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

Science.gov (United States)

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

2017-11-01

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

High-throughput sequencing and analysis of the gill tissue transcriptome from the deep-sea hydrothermal vent mussel Bathymodiolus azoricus

Directory of Open Access Journals (Sweden)

Gomes Paula

2010-10-01

Full Text Available Abstract Background Bathymodiolus azoricus is a deep-sea hydrothermal vent mussel found in association with large faunal communities living in chemosynthetic environments at the bottom of the sea floor near the Azores Islands. Investigation of the exceptional physiological reactions that vent mussels have adopted in their habitat, including responses to environmental microbes, remains a difficult challenge for deep-sea biologists. In an attempt to reveal genes potentially involved in the deep-sea mussel innate immunity we carried out a high-throughput sequence analysis of freshly collected B. azoricus transcriptome using gills tissues as the primary source of immune transcripts given its strategic role in filtering the surrounding waterborne potentially infectious microorganisms. Additionally, a substantial EST data set was produced and from which a comprehensive collection of genes coding for putative proteins was organized in a dedicated database, "DeepSeaVent" the first deep-sea vent animal transcriptome database based on the 454 pyrosequencing technology. Results A normalized cDNA library from gills tissue was sequenced in a full 454 GS-FLX run, producing 778,996 sequencing reads. Assembly of the high quality reads resulted in 75,407 contigs of which 3,071 were singletons. A total of 39,425 transcripts were conceptually translated into amino-sequences of which 22,023 matched known proteins in the NCBI non-redundant protein database, 15,839 revealed conserved protein domains through InterPro functional classification and 9,584 were assigned with Gene Ontology terms. Queries conducted within the database enabled the identification of genes putatively involved in immune and inflammatory reactions which had not been previously evidenced in the vent mussel. Their physical counterpart was confirmed by semi-quantitative quantitative Reverse-Transcription-Polymerase Chain Reactions (RT-PCR and their RNA transcription level by quantitative PCR (q

Database Description - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Yeast Interacting Proteins Database Database Description General information of database Database... name Yeast Interacting Proteins Database Alternative name - DOI 10.18908/lsdba.nbdc00742-000 Creator C...-ken 277-8561 Tel: +81-4-7136-3989 FAX: +81-4-7136-3979 E-mail : Database classif...s cerevisiae Taxonomy ID: 4932 Database description Information on interactions and related information obta...l Acad Sci U S A. 2001 Apr 10;98(8):4569-74. Epub 2001 Mar 13. External Links: Original website information Database

Update History of This Database - Trypanosomes Database | LSDB Archive [Life Science Database Archive metadata

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Full Text Available List Contact us Trypanosomes Database Update History of This Database Date Update contents 2014/05/07 The co...ntact information is corrected. The features and manner of utilization of the database are corrected. 2014/02/04 Trypanosomes Databas...e English archive site is opened. 2011/04/04 Trypanosomes Database ( http://www.tan...paku.org/tdb/ ) is opened. About This Database Database Description Download Lice...nse Update History of This Database Site Policy | Contact Us Update History of This Database - Trypanosomes Database | LSDB Archive ...

Application of deep learning to the classification of images from colposcopy.

Science.gov (United States)

Sato, Masakazu; Horie, Koji; Hara, Aki; Miyamoto, Yuichiro; Kurihara, Kazuko; Tomio, Kensuke; Yokota, Harushige

2018-03-01

The objective of the present study was to investigate whether deep learning could be applied successfully to the classification of images from colposcopy. For this purpose, a total of 158 patients who underwent conization were enrolled, and medical records and data from the gynecological oncology database were retrospectively reviewed. Deep learning was performed with the Keras neural network and TensorFlow libraries. Using preoperative images from colposcopy as the input data and deep learning technology, the patients were classified into three groups [severe dysplasia, carcinoma in situ (CIS) and invasive cancer (IC)]. A total of 485 images were obtained for the analysis, of which 142 images were of severe dysplasia (2.9 images/patient), 257 were of CIS (3.3 images/patient), and 86 were of IC (4.1 images/patient). Of these, 233 images were captured with a green filter, and the remaining 252 were captured without a green filter. Following the application of L2 regularization, L1 regularization, dropout and data augmentation, the accuracy of the validation dataset was ~50%. Although the present study is preliminary, the results indicated that deep learning may be applied to classify colposcopy images.

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 ...

Premature ventricular contraction detection combining deep neural networks and rules inference.

Science.gov (United States)

Zhou, Fei-Yan; Jin, Lin-Peng; Dong, Jun

2017-06-01

Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpatient ECG rooms. In this paper, we proposed a new approach that combined deep neural networks and rules inference for PVC detection. The detection performance and generalization were studied using publicly available databases: the MIT-BIH arrhythmia database (MIT-BIH-AR) and the Chinese Cardiovascular Disease Database (CCDD). The PVC detection accuracy on the MIT-BIH-AR database was 99.41%, with a sensitivity and specificity of 97.59% and 99.54%, respectively, which were better than the results from other existing methods. To test the generalization capability, the detection performance was also evaluated on the CCDD. The effectiveness of the proposed method was confirmed by the accuracy (98.03%), sensitivity (96.42%) and specificity (98.06%) with the dataset over 140,000 ECG recordings of the CCDD. Copyright © 2017 Elsevier B.V. All rights reserved.

Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network.

Science.gov (United States)

Charron, Odelin; Lallement, Alex; Jarnet, Delphine; Noblet, Vincent; Clavier, Jean-Baptiste; Meyer, Philippe

2018-04-01

Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter convolutional neural network (DeepMedic) to detect and segment brain metastases on MRI. At first, we sought to adapt the network parameters to brain metastases. We then explored the single or combined use of different MRI modalities, by evaluating network performance in terms of detection and segmentation. We also studied the interest of increasing the database with virtual patients or of using an additional database in which the active parts of the metastases are separated from the necrotic parts. Our results indicated that a deep network approach is promising for the detection and the segmentation of brain metastases on multimodal MRI. Copyright © 2018 Elsevier Ltd. All rights reserved.

Toward An Unstructured Mesh Database

Science.gov (United States)

Rezaei Mahdiraji, Alireza; Baumann, Peter Peter

2014-05-01

Unstructured meshes are used in several application domains such as earth sciences (e.g., seismology), medicine, oceanography, cli- mate modeling, GIS as approximate representations of physical objects. Meshes subdivide a domain into smaller geometric elements (called cells) which are glued together by incidence relationships. The subdivision of a domain allows computational manipulation of complicated physical structures. For instance, seismologists model earthquakes using elastic wave propagation solvers on hexahedral meshes. The hexahedral con- tains several hundred millions of grid points and millions of hexahedral cells. Each vertex node in the hexahedrals stores a multitude of data fields. To run simulation on such meshes, one needs to iterate over all the cells, iterate over incident cells to a given cell, retrieve coordinates of cells, assign data values to cells, etc. Although meshes are used in many application domains, to the best of our knowledge there is no database vendor that support unstructured mesh features. Currently, the main tool for querying and manipulating unstructured meshes are mesh libraries, e.g., CGAL and GRAL. Mesh li- braries are dedicated libraries which includes mesh algorithms and can be run on mesh representations. The libraries do not scale with dataset size, do not have declarative query language, and need deep C++ knowledge for query implementations. Furthermore, due to high coupling between the implementations and input file structure, the implementations are less reusable and costly to maintain. A dedicated mesh database offers the following advantages: 1) declarative querying, 2) ease of maintenance, 3) hiding mesh storage structure from applications, and 4) transparent query optimization. To design a mesh database, the first challenge is to define a suitable generic data model for unstructured meshes. We proposed ImG-Complexes data model as a generic topological mesh data model which extends incidence graph model to multi

Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.

Science.gov (United States)

Li, Zhongyu; Butler, Erik; Li, Kang; Lu, Aidong; Ji, Shuiwang; Zhang, Shaoting

2018-02-12

Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.

Using a model of human visual perception to improve deep learning.

Science.gov (United States)

Stettler, Michael; Francis, Gregory

2018-04-17

Deep learning algorithms achieve human-level (or better) performance on many tasks, but there still remain situations where humans learn better or faster. With regard to classification of images, we argue that some of those situations are because the human visual system represents information in a format that promotes good training and classification. To demonstrate this idea, we show how occluding objects can impair performance of a deep learning system that is trained to classify digits in the MNIST database. We describe a human inspired segmentation and interpolation algorithm that attempts to reconstruct occluded parts of an image, and we show that using this reconstruction algorithm to pre-process occluded images promotes training and classification performance. Copyright © 2018 Elsevier Ltd. All rights reserved.

Update History of This Database - Arabidopsis Phenome Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Arabidopsis Phenome Database Update History of This Database Date Update contents 2017/02/27 Arabidopsis Phenome Data...base English archive site is opened. - Arabidopsis Phenome Database (http://jphenom...e.info/?page_id=95) is opened. About This Database Database Description Download License Update History of This Database... Site Policy | Contact Us Update History of This Database - Arabidopsis Phenome Database | LSDB Archive ...

Refactoring databases evolutionary database design

CERN Document Server

Ambler, Scott W

2006-01-01

Refactoring has proven its value in a wide range of development projects–helping software professionals improve system designs, maintainability, extensibility, and performance. Now, for the first time, leading agile methodologist Scott Ambler and renowned consultant Pramodkumar Sadalage introduce powerful refactoring techniques specifically designed for database systems. Ambler and Sadalage demonstrate how small changes to table structures, data, stored procedures, and triggers can significantly enhance virtually any database design–without changing semantics. You’ll learn how to evolve database schemas in step with source code–and become far more effective in projects relying on iterative, agile methodologies. This comprehensive guide and reference helps you overcome the practical obstacles to refactoring real-world databases by covering every fundamental concept underlying database refactoring. Using start-to-finish examples, the authors walk you through refactoring simple standalone databas...

Update History of This Database - SKIP Stemcell Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us SKIP Stemcell Database Update History of This Database Date Update contents 2017/03/13 SKIP Stemcell Database... English archive site is opened. 2013/03/29 SKIP Stemcell Database ( https://www.skip.med.k...eio.ac.jp/SKIPSearch/top?lang=en ) is opened. About This Database Database Description Download License Update History of This Databa...se Site Policy | Contact Us Update History of This Database - SKIP Stemcell Database | LSDB Archive ...

Fungal diversity in deep-sea sediments associated with asphalt seeps at the Sao Paulo Plateau

Science.gov (United States)

Nagano, Yuriko; Miura, Toshiko; Nishi, Shinro; Lima, Andre O.; Nakayama, Cristina; Pellizari, Vivian H.; Fujikura, Katsunori

2017-12-01

We investigated the fungal diversity in a total of 20 deep-sea sediment samples (of which 14 samples were associated with natural asphalt seeps and 6 samples were not associated) collected from two different sites at the Sao Paulo Plateau off Brazil by Ion Torrent PGM targeting ITS region of ribosomal RNA. Our results suggest that diverse fungi (113 operational taxonomic units (OTUs) based on clustering at 97% sequence similarity assigned into 9 classes and 31 genus) are present in deep-sea sediment samples collected at the Sao Paulo Plateau, dominated by Ascomycota (74.3%), followed by Basidiomycota (11.5%), unidentified fungi (7.1%), and sequences with no affiliation to any organisms in the public database (7.1%). However, it was revealed that only three species, namely Penicillium sp., Cadophora malorum and Rhodosporidium diobovatum, were dominant, with the majority of OTUs remaining a minor community. Unexpectedly, there was no significant difference in major fungal community structure between the asphalt seep and non-asphalt seep sites, despite the presence of mass hydrocarbon deposits and the high amount of macro organisms surrounding the asphalt seeps. However, there were some differences in the minor fungal communities, with possible asphalt degrading fungi present specifically in the asphalt seep sites. In contrast, some differences were found between the two different sampling sites. Classification of OTUs revealed that only 47 (41.6%) fungal OTUs exhibited >97% sequence similarity, in comparison with pre-existing ITS sequences in public databases, indicating that a majority of deep-sea inhabiting fungal taxa still remain undescribed. Although our knowledge on fungi and their role in deep-sea environments is still limited and scarce, this study increases our understanding of fungal diversity and community structure in deep-sea environments.

Multispectral embedding-based deep neural network for three-dimensional human pose recovery

Science.gov (United States)

Yu, Jialin; Sun, Jifeng

2018-01-01

Monocular image-based three-dimensional (3-D) human pose recovery aims to retrieve 3-D poses using the corresponding two-dimensional image features. Therefore, the pose recovery performance highly depends on the image representations. We propose a multispectral embedding-based deep neural network (MSEDNN) to automatically obtain the most discriminative features from multiple deep convolutional neural networks and then embed their penultimate fully connected layers into a low-dimensional manifold. This compact manifold can explore not only the optimum output from multiple deep networks but also the complementary properties of them. Furthermore, the distribution of each hierarchy discriminative manifold is sufficiently smooth so that the training process of our MSEDNN can be effectively implemented only using few labeled data. Our proposed network contains a body joint detector and a human pose regressor that are jointly trained. Extensive experiments conducted on four databases show that our proposed MSEDNN can achieve the best recovery performance compared with the state-of-the-art methods.

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.

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...

Database design and database administration for a kindergarten

OpenAIRE

Vítek, Daniel

2009-01-01

The bachelor thesis deals with creation of database design for a standard kindergarten, installation of the designed database into the database system Oracle Database 10g Express Edition and demonstration of the administration tasks in this database system. The verification of the database was proved by a developed access application.

A compilation of structure functions in deep-inelastic scattering

International Nuclear Information System (INIS)

Roberts, R.G.; Whalley, M.R.

1991-01-01

A compilation of data on the structure functions F 2 , xF 3 , and R = σ L /σ T from lepton deep-inelastic scattering off protons and nuclei is presented. The relevant experiments at CERN, Fermilab and SLAC from 1985 are covered. All the data in this review can be found in and retrieved from the Durham-RAL HEP Databases (HEPDATA on the RAL and CERN VM systems and on DURPDG VAX/VMS) together with data on a wide variety of other reactions. (author)

Database Description - Open TG-GATEs Pathological Image Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Open TG-GATEs Pathological Image Database Database Description General information of database Database... name Open TG-GATEs Pathological Image Database Alternative name - DOI 10.18908/lsdba.nbdc00954-0...iomedical Innovation 7-6-8, Saito-asagi, Ibaraki-city, Osaka 567-0085, Japan TEL:81-72-641-9826 Email: Database... classification Toxicogenomics Database Organism Taxonomy Name: Rattus norvegi... Article title: Author name(s): Journal: External Links: Original website information Database

miRBase: annotating high confidence microRNAs using deep sequencing data.

Science.gov (United States)

Kozomara, Ana; Griffiths-Jones, Sam

2014-01-01

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

A novel application of deep learning for single-lead ECG classification.

Science.gov (United States)

Mathews, Sherin M; Kambhamettu, Chandra; Barner, Kenneth E

2018-06-04

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG. The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63%) and of supraventricular ectopic beats (95.57%) at a low sampling rate of 114 Hz. Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies. Copyright © 2018. Published by Elsevier Ltd.

Pedicle screw loosening is correlated to chronic subclinical deep implant infection: a retrospective database analysis.

Science.gov (United States)

Leitner, Lukas; Malaj, Isabella; Sadoghi, Patrick; Amerstorfer, Florian; Glehr, Mathias; Vander, Klaus; Leithner, Andreas; Radl, Roman

2018-04-13

Spinal fusion is used for treatment of spinal deformities, degeneration, infection, malignancy, and trauma. Reduction of motion enables osseous fusion and permanent stabilization of segments, compromised by loosening of the pedicle screws (PS). Deep implant infection, biomechanical, and chemical mechanisms are suspected reasons for loosening of PS. Study objective was to investigate the frequency and impact of deep implant infection on PS loosening. Intraoperative infection screening from wound and explanted material sonication was performed during revision surgeries following dorsal stabilization. Case history events and factors, which might promote implant infections, were included in this retrospective survey. 110 cases of spinal metal explantation were included. In 29.1% of revision cases, infection screening identified a germ, most commonly Staphylococcus (53.1%) and Propionibacterium (40.6%) genus. Patients screened positive had a significant higher number of previous spinal operations and radiologic loosening of screws. Patients revised for adjacent segment failure had a significantly lower rate of positive infection screening than patients revised for directly implant associated reasons. Removal of implants that revealed positive screening effected significant pain relief. Chronic implant infection seems to play a role in PS loosening and ongoing pain, causing revision surgery after spinal fusion. Screw loosening and multiple prior spinal operations should be suspicious for implant infection after spinal fusion when it comes to revision surgery. These slides can be retrieved under Electronic Supplementary Material.

Shielded battery syndrome: a new hardware complication of deep brain stimulation.

Science.gov (United States)

Chelvarajah, Ramesh; Lumsden, Daniel; Kaminska, Margaret; Samuel, Michael; Hulse, Natasha; Selway, Richard P; Lin, Jean-Pierre; Ashkan, Keyoumars

2012-01-01

Deep brain stimulation hardware is constantly advancing. The last few years have seen the introduction of rechargeable cell technology into the implanted pulse generator design, allowing for longer battery life and fewer replacement operations. The Medtronic® system requires an additional pocket adaptor when revising a non-rechargeable battery such as their Kinetra® to their rechargeable Activa® RC. This additional hardware item can, if it migrates superficially, become an impediment to the recharging of the battery and negate the intended technological advance. To report the emergence of the 'shielded battery syndrome', which has not been previously described. We reviewed our deep brain stimulation database to identify cases of recharging difficulties reported by patients with Activa RC implanted pulse generators. Two cases of shielded battery syndrome were identified. The first required surgery to reposition the adaptor to the deep aspect of the subcutaneous pocket. In the second case, it was possible to perform external manual manipulation to restore the adaptor to its original position deep to the battery. We describe strategies to minimise the occurrence of the shielded battery syndrome and advise vigilance in all patients who experience difficulty with recharging after replacement surgery of this type for the implanted pulse generator. Copyright © 2012 S. Karger AG, Basel.

Pulotu: Database of Austronesian Supernatural Beliefs and Practices.

Science.gov (United States)

Watts, Joseph; Sheehan, Oliver; Greenhill, Simon J; Gomes-Ng, Stephanie; Atkinson, Quentin D; Bulbulia, Joseph; Gray, Russell D

2015-01-01

Scholars have debated naturalistic theories of religion for thousands of years, but only recently have scientists begun to test predictions empirically. Existing databases contain few variables on religion, and are subject to Galton's Problem because they do not sufficiently account for the non-independence of cultures or systematically differentiate the traditional states of cultures from their contemporary states. Here we present Pulotu: the first quantitative cross-cultural database purpose-built to test evolutionary hypotheses of supernatural beliefs and practices. The Pulotu database documents the remarkable diversity of the Austronesian family of cultures, which originated in Taiwan, spread west to Madagascar and east to Easter Island-a region covering over half the world's longitude. The focus of Austronesian beliefs range from localised ancestral spirits to powerful creator gods. A wide range of practices also exist, such as headhunting, elaborate tattooing, and the construction of impressive monuments. Pulotu is freely available, currently contains 116 cultures, and has 80 variables describing supernatural beliefs and practices, as well as social and physical environments. One major advantage of Pulotu is that it has separate sections on the traditional states of cultures, the post-contact history of cultures, and the contemporary states of cultures. A second major advantage is that cultures are linked to a language-based family tree, enabling the use phylogenetic methods, which can be used to address Galton's Problem by accounting for common ancestry, to infer deep prehistory, and to model patterns of trait evolution over time. We illustrate the power of phylogenetic methods by performing an ancestral state reconstruction on the Pulotu variable "headhunting", finding evidence that headhunting was practiced in proto-Austronesian culture. Quantitative cross-cultural databases explicitly linking cultures to a phylogeny have the potential to revolutionise the

Pulotu: Database of Austronesian Supernatural Beliefs and Practices.

Directory of Open Access Journals (Sweden)

Joseph Watts

Full Text Available Scholars have debated naturalistic theories of religion for thousands of years, but only recently have scientists begun to test predictions empirically. Existing databases contain few variables on religion, and are subject to Galton's Problem because they do not sufficiently account for the non-independence of cultures or systematically differentiate the traditional states of cultures from their contemporary states. Here we present Pulotu: the first quantitative cross-cultural database purpose-built to test evolutionary hypotheses of supernatural beliefs and practices. The Pulotu database documents the remarkable diversity of the Austronesian family of cultures, which originated in Taiwan, spread west to Madagascar and east to Easter Island-a region covering over half the world's longitude. The focus of Austronesian beliefs range from localised ancestral spirits to powerful creator gods. A wide range of practices also exist, such as headhunting, elaborate tattooing, and the construction of impressive monuments. Pulotu is freely available, currently contains 116 cultures, and has 80 variables describing supernatural beliefs and practices, as well as social and physical environments. One major advantage of Pulotu is that it has separate sections on the traditional states of cultures, the post-contact history of cultures, and the contemporary states of cultures. A second major advantage is that cultures are linked to a language-based family tree, enabling the use phylogenetic methods, which can be used to address Galton's Problem by accounting for common ancestry, to infer deep prehistory, and to model patterns of trait evolution over time. We illustrate the power of phylogenetic methods by performing an ancestral state reconstruction on the Pulotu variable "headhunting", finding evidence that headhunting was practiced in proto-Austronesian culture. Quantitative cross-cultural databases explicitly linking cultures to a phylogeny have the potential

Update History of This Database - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Yeast Interacting Proteins Database Update History of This Database Date Update contents 201...0/03/29 Yeast Interacting Proteins Database English archive site is opened. 2000/12/4 Yeast Interacting Proteins Database...( http://itolab.cb.k.u-tokyo.ac.jp/Y2H/ ) is released. About This Database Database Description... Download License Update History of This Database Site Policy | Contact Us Update History of This Database... - Yeast Interacting Proteins Database | LSDB Archive ...

Deep transfer learning for automatic target classification: MWIR to LWIR

Science.gov (United States)

Ding, Zhengming; Nasrabadi, Nasser; Fu, Yun

2016-05-01

Publisher's Note: This paper, originally published on 5/12/2016, was replaced with a corrected/revised version on 5/18/2016. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. When dealing with sparse or no labeled data in the target domain, transfer learning shows its appealing performance by borrowing the supervised knowledge from external domains. Recently deep structure learning has been exploited in transfer learning due to its attractive power in extracting effective knowledge through multi-layer strategy, so that deep transfer learning is promising to address the cross-domain mismatch. In general, cross-domain disparity can be resulted from the difference between source and target distributions or different modalities, e.g., Midwave IR (MWIR) and Longwave IR (LWIR). In this paper, we propose a Weighted Deep Transfer Learning framework for automatic target classification through a task-driven fashion. Specifically, deep features and classifier parameters are obtained simultaneously for optimal classification performance. In this way, the proposed deep structures can extract more effective features with the guidance of the classifier performance; on the other hand, the classifier performance is further improved since it is optimized on more discriminative features. Furthermore, we build a weighted scheme to couple source and target output by assigning pseudo labels to target data, therefore we can transfer knowledge from source (i.e., MWIR) to target (i.e., LWIR). Experimental results on real databases demonstrate the superiority of the proposed algorithm by comparing with others.

IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD

Directory of Open Access Journals (Sweden)

A. S. Potapov

2014-09-01

Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.

Database Description - RMOS | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available base Description General information of database Database name RMOS Alternative nam...arch Unit Shoshi Kikuchi E-mail : Database classification Plant databases - Rice Microarray Data and other Gene Expression Database...s Organism Taxonomy Name: Oryza sativa Taxonomy ID: 4530 Database description The Ric...19&lang=en Whole data download - Referenced database Rice Expression Database (RED) Rice full-length cDNA Database... (KOME) Rice Genome Integrated Map Database (INE) Rice Mutant Panel Database (Tos17) Rice Genome Annotation Database

KALIMER database development (database configuration and design methodology)

International Nuclear Information System (INIS)

Jeong, Kwan Seong; Kwon, Young Min; Lee, Young Bum; Chang, Won Pyo; Hahn, Do Hee

2001-10-01

KALIMER Database is an advanced database to utilize the integration management for Liquid Metal Reactor Design Technology Development using Web Applicatins. KALIMER Design database consists of Results Database, Inter-Office Communication (IOC), and 3D CAD database, Team Cooperation system, and Reserved Documents, Results Database is a research results database during phase II for Liquid Metal Reactor Design Technology Develpment of mid-term and long-term nuclear R and D. IOC is a linkage control system inter sub project to share and integrate the research results for KALIMER. 3D CAD Database is s schematic design overview for KALIMER. Team Cooperation System is to inform team member of research cooperation and meetings. Finally, KALIMER Reserved Documents is developed to manage collected data and several documents since project accomplishment. This report describes the features of Hardware and Software and the Database Design Methodology for KALIMER

Deep learning of mutation-gene-drug relations from the literature.

Science.gov (United States)

Lee, Kyubum; Kim, Byounggun; Choi, Yonghwa; Kim, Sunkyu; Shin, Wonho; Lee, Sunwon; Park, Sungjoon; Kim, Seongsoon; Tan, Aik Choon; Kang, Jaewoo

2018-01-25

Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature. Here, we present two new computational methods that utilize all the PubMed articles as domain specific background knowledge to assist in the extraction and curation of gene-mutation-drug relations from the literature. The first method uses the Biomedical Entity Search Tool (BEST) scoring results as some of the features to train the machine learning classifiers. The second method uses not only the BEST scoring results, but also word vectors in a deep convolutional neural network model that are constructed from and trained on numerous documents such as PubMed abstracts and Google News articles. Using the features obtained from both the BEST search engine scores and word vectors, we extract mutation-gene and mutation-drug relations from the literature using machine learning classifiers such as random forest and deep convolutional neural networks. Our methods achieved better results compared with the state-of-the-art methods. We used our proposed features in a simple machine learning model, and obtained F1-scores of 0.96 and 0.82 for mutation-gene and mutation-drug relation classification, respectively. We also developed a deep learning classification model using convolutional neural networks, BEST scores, and the word embeddings that are pre-trained on PubMed or Google News data. Using deep learning, the classification accuracy improved, and F1-scores of 0.96 and 0.86 were obtained for the mutation-gene and mutation-drug relations, respectively. We believe that our computational methods described in this research could be

AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images.

Science.gov (United States)

Albarqouni, Shadi; Baur, Christoph; Achilles, Felix; Belagiannis, Vasileios; Demirci, Stefanie; Navab, Nassir

2016-05-01

The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy annotations during training. In this manuscript, we present a new concept for learning from crowds that handle data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet). Besides, we present an experimental study on learning from crowds designed to answer the following questions. 1) Can deep CNN be trained with data collected from crowdsourcing? 2) How to adapt the CNN to train on multiple types of annotation datasets (ground truth and crowd-based)? 3) How does the choice of annotation and aggregation affect the accuracy? Our experimental setup involved Annot8, a self-implemented web-platform based on Crowdflower API realizing image annotation tasks for a publicly available biomedical image database. Our results give valuable insights into the functionality of deep CNN learning from crowd annotations and prove the necessity of data aggregation integration.

Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods

Directory of Open Access Journals (Sweden)

Mahesh Jangid

2018-02-01

Full Text Available Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN which are one of the recent techniques adopted from the deep learning community. We experimented the ISIDCHAR database provided by (Information Sharing Index ISI, Kolkata and V2DMDCHAR database with six different architectures of DCNN to evaluate the performance and also investigate the use of six recently developed adaptive gradient methods. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.

Database Description - SAHG | LSDB Archive [Life Science Database Archive metadata

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Full Text Available base Description General information of database Database name SAHG Alternative nam...h: Contact address Chie Motono Tel : +81-3-3599-8067 E-mail : Database classification Structure Databases - ...e databases - Protein properties Organism Taxonomy Name: Homo sapiens Taxonomy ID: 9606 Database description... Links: Original website information Database maintenance site The Molecular Profiling Research Center for D...stration Not available About This Database Database Description Download License Update History of This Database Site Policy | Contact Us Database Description - SAHG | LSDB Archive ...

BDFGEOTHERM - A Swiss geothermal fluids database; BDFGEOTHERM - Base de donnees des fluides geothermiques de la Suisse - Rapport final

Energy Technology Data Exchange (ETDEWEB)

Sonney, R.; Vuataz, F.-D.

2007-07-01

The motivation to build up the database BDFGeotherm was to put at the disposal of the geothermal community a comprehensive set of data on the deep fluids of Switzerland and of some neighbouring areas. Researchers, engineers and all persons wanting to know the type and properties of geothermal fluids existing in a given area or underground system can find in BDFGeotherm a wealth of information which are generally widely dispersed and often difficult to reach. The BDFGeotherm database has been built under Microsoft ACCESS code and consists of nine tables connected with a primary key: the field 'Code'. A selection of parameters has been chosen from the following fields: general and geographical description, geology, hydrogeology, hydraulics, hydrochemistry and isotopes and finally geothermal parameters. Data implemented in BDFGeotherm are in numerical or in text format. Moreover, in the field 'Lithological log', one can visualize and save bitmap images containing lithological logs of boreholes. A total of 203 thermal springs or deep boreholes from 82 geothermal sites are implemented in BDFGeotherm. Among the 68 Swiss sites, a large majority of them are located in the northern part of the Jura range and in the upper Rhone valley (Wallis). Some sites, in Germany (5), France (3) and Italy (6), were selected for the following reasons: located near Swiss hot springs or deep boreholes, having similar geological features or representing a significant geothermal potential. Many types of queries could be realised, using any fields of the database and the results can be put into tables and printed or exported and saved in other files. (author)

Database Description - PSCDB | LSDB Archive [Life Science Database Archive metadata

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Full Text Available abase Description General information of database Database name PSCDB Alternative n...rial Science and Technology (AIST) Takayuki Amemiya E-mail: Database classification Structure Databases - Protein structure Database...554-D558. External Links: Original website information Database maintenance site Graduate School of Informat...available URL of Web services - Need for user registration Not available About This Database Database Descri...ption Download License Update History of This Database Site Policy | Contact Us Database Description - PSCDB | LSDB Archive ...

Large-scale extraction of gene interactions from full-text literature using DeepDive.

Science.gov (United States)

Mallory, Emily K; Zhang, Ce; Ré, Christopher; Altman, Russ B

2016-01-01

A complete repository of gene-gene interactions is key for understanding cellular processes, human disease and drug response. These gene-gene interactions include both protein-protein interactions and transcription factor interactions. The majority of known interactions are found in the biomedical literature. Interaction databases, such as BioGRID and ChEA, annotate these gene-gene interactions; however, curation becomes difficult as the literature grows exponentially. DeepDive is a trained system for extracting information from a variety of sources, including text. In this work, we used DeepDive to extract both protein-protein and transcription factor interactions from over 100,000 full-text PLOS articles. We built an extractor for gene-gene interactions that identified candidate gene-gene relations within an input sentence. For each candidate relation, DeepDive computed a probability that the relation was a correct interaction. We evaluated this system against the Database of Interacting Proteins and against randomly curated extractions. Our system achieved 76% precision and 49% recall in extracting direct and indirect interactions involving gene symbols co-occurring in a sentence. For randomly curated extractions, the system achieved between 62% and 83% precision based on direct or indirect interactions, as well as sentence-level and document-level precision. Overall, our system extracted 3356 unique gene pairs using 724 features from over 100,000 full-text articles. Application source code is publicly available at https://github.com/edoughty/deepdive_genegene_app russ.altman@stanford.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Database Description - ASTRA | LSDB Archive [Life Science Database Archive metadata

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Full Text Available abase Description General information of database Database name ASTRA Alternative n...tics Journal Search: Contact address Database classification Nucleotide Sequence Databases - Gene structure,...3702 Taxonomy Name: Oryza sativa Taxonomy ID: 4530 Database description The database represents classified p...(10):1211-6. External Links: Original website information Database maintenance site National Institute of Ad... for user registration Not available About This Database Database Description Dow

Database Description - RPD | LSDB Archive [Life Science Database Archive metadata

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Full Text Available ase Description General information of database Database name RPD Alternative name Rice Proteome Database...titute of Crop Science, National Agriculture and Food Research Organization Setsuko Komatsu E-mail: Database... classification Proteomics Resources Plant databases - Rice Organism Taxonomy Name: Oryza sativa Taxonomy ID: 4530 Database... description Rice Proteome Database contains information on protei...and entered in the Rice Proteome Database. The database is searchable by keyword,

Database Description - PLACE | LSDB Archive [Life Science Database Archive metadata

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Full Text Available abase Description General information of database Database name PLACE Alternative name A Database...Kannondai, Tsukuba, Ibaraki 305-8602, Japan National Institute of Agrobiological Sciences E-mail : Databas...e classification Plant databases Organism Taxonomy Name: Tracheophyta Taxonomy ID: 58023 Database...99, Vol.27, No.1 :297-300 External Links: Original website information Database maintenance site National In...- Need for user registration Not available About This Database Database Descripti

Deep data science to prevent and treat growth faltering in Maya children

OpenAIRE

Varela-Silva, M I; Bogin, B; Sobral, J A G; Dickinson, F; Monserrat-Revillo, S

2016-01-01

The Maya people are descended from the indigenous inhabitants of southern Mexico, Guatemala and adjacent regions of Central America. In Guatemala, 50% of infants and children are stunted (very low height-for-age), and some rural Maya regions have >70% children stunted. A large, longitudinal, intergenerational database was created to (1) provide deep data to prevent and treat somatic growth faltering and impaired neurocognitive development, (2) detect key dependencies and predictive relations ...

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

Directory of Open Access Journals (Sweden)

Srdjan Sladojevic

2016-01-01

Full Text Available The latest generation of convolutional neural networks (CNNs has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Database Description - JSNP | LSDB Archive [Life Science Database Archive metadata

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Full Text Available base Description General information of database Database name JSNP Alternative nam...n Science and Technology Agency Creator Affiliation: Contact address E-mail : Database...sapiens Taxonomy ID: 9606 Database description A database of about 197,000 polymorphisms in Japanese populat...1):605-610 External Links: Original website information Database maintenance site Institute of Medical Scien...er registration Not available About This Database Database Description Download License Update History of This Database

Database Description - RED | LSDB Archive [Life Science Database Archive metadata

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Full Text Available ase Description General information of database Database name RED Alternative name Rice Expression Database...enome Research Unit Shoshi Kikuchi E-mail : Database classification Plant databases - Rice Database classifi...cation Microarray, Gene Expression Organism Taxonomy Name: Oryza sativa Taxonomy ID: 4530 Database descripti... Article title: Rice Expression Database: the gateway to rice functional genomics...nt Science (2002) Dec 7 (12):563-564 External Links: Original website information Database maintenance site

Database Description - ConfC | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available abase Description General information of database Database name ConfC Alternative name Database...amotsu Noguchi Tel: 042-495-8736 E-mail: Database classification Structure Database...s - Protein structure Structure Databases - Small molecules Structure Databases - Nucleic acid structure Database... services - Need for user registration - About This Database Database Description Download License Update History of This Database... Site Policy | Contact Us Database Description - ConfC | LSDB Archive ...

Database management systems understanding and applying database technology

CERN Document Server

Gorman, Michael M

1991-01-01

Database Management Systems: Understanding and Applying Database Technology focuses on the processes, methodologies, techniques, and approaches involved in database management systems (DBMSs).The book first takes a look at ANSI database standards and DBMS applications and components. Discussion focus on application components and DBMS components, implementing the dynamic relationship application, problems and benefits of dynamic relationship DBMSs, nature of a dynamic relationship application, ANSI/NDL, and DBMS standards. The manuscript then ponders on logical database, interrogation, and phy

Database Description - RMG | LSDB Archive [Life Science Database Archive metadata

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Full Text Available ase Description General information of database Database name RMG Alternative name ...raki 305-8602, Japan National Institute of Agrobiological Sciences E-mail : Database... classification Nucleotide Sequence Databases Organism Taxonomy Name: Oryza sativa Japonica Group Taxonomy ID: 39947 Database...rnal: Mol Genet Genomics (2002) 268: 434–445 External Links: Original website information Database...available URL of Web services - Need for user registration Not available About This Database Database Descri

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.

Relational databases

CERN Document Server

Bell, D A

1986-01-01

Relational Databases explores the major advances in relational databases and provides a balanced analysis of the state of the art in relational databases. Topics covered include capture and analysis of data placement requirements; distributed relational database systems; data dependency manipulation in database schemata; and relational database support for computer graphics and computer aided design. This book is divided into three sections and begins with an overview of the theory and practice of distributed systems, using the example of INGRES from Relational Technology as illustration. The

Database Description - DGBY | LSDB Archive [Life Science Database Archive metadata

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Full Text Available base Description General information of database Database name DGBY Alternative name Database...EL: +81-29-838-8066 E-mail: Database classification Microarray Data and other Gene Expression Databases Orga...nism Taxonomy Name: Saccharomyces cerevisiae Taxonomy ID: 4932 Database descripti...-called phenomics). We uploaded these data on this website which is designated DGBY(Database for Gene expres...ma J, Ando A, Takagi H. Journal: Yeast. 2008 Mar;25(3):179-90. External Links: Original website information Database

Database Description - KOME | LSDB Archive [Life Science Database Archive metadata

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Full Text Available base Description General information of database Database name KOME Alternative nam... Sciences Plant Genome Research Unit Shoshi Kikuchi E-mail : Database classification Plant databases - Rice ...Organism Taxonomy Name: Oryza sativa Taxonomy ID: 4530 Database description Information about approximately ...Hayashizaki Y, Kikuchi S. Journal: PLoS One. 2007 Nov 28; 2(11):e1235. External Links: Original website information Database...OS) Rice mutant panel database (Tos17) A Database of Plant Cis-acting Regulatory

Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

Science.gov (United States)

Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

Random Deep Belief Networks for Recognizing Emotions from Speech Signals

Directory of Open Access Journals (Sweden)

Guihua Wen

2017-01-01

Full Text Available Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities

DEFF Research Database (Denmark)

Haque, Mohammad Ahsanul; Nasrollahi, Kamal; Moeslund, Thomas B.

2018-01-01

, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data......PAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance...... recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate....

Databases

Directory of Open Access Journals (Sweden)

Nick Ryan

2004-01-01

Full Text Available Databases are deeply embedded in archaeology, underpinning and supporting many aspects of the subject. However, as well as providing a means for storing, retrieving and modifying data, databases themselves must be a result of a detailed analysis and design process. This article looks at this process, and shows how the characteristics of data models affect the process of database design and implementation. The impact of the Internet on the development of databases is examined, and the article concludes with a discussion of a range of issues associated with the recording and management of archaeological data.

Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition

Science.gov (United States)

Kim, Sungho

2017-06-01

Automatic target recognition (ATR) is a traditionally challenging problem in military applications because of the wide range of infrared (IR) image variations and the limited number of training images. IR variations are caused by various three-dimensional target poses, noncooperative weather conditions (fog and rain), and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches for RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of RGB-CNN to the IR ATR problem fails to work because of the IR database problems (limited database size and IR image variations). An IR variation-reduced deep CNN (IVR-CNN) to cope with the problems is presented. The problem of limited IR database size is solved by a commercial thermal simulator (OKTAL-SE). The second problem of IR variations is mitigated by the proposed shifted ramp function-based intensity transformation. This can suppress the background and enhance the target contrast simultaneously. The experimental results on the synthesized IR images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVR-CNN for military ATR applications.

Development of the sorption and diffusion database system for safety assessment of geological disposal

International Nuclear Information System (INIS)

Tachi, Yukio; Tochigi, Yoshikatsu; Suyama, Tadahiro; Saito, Yoshihiko; Yui, Mikazu; Ochs, Michael

2009-02-01

Japan Atomic Energy Agency (JAEA) has been developing databases of sorption and diffusion parameters in buffer material (bentonite) and rock, which are key parameters for safety assessment of the geological disposal. These sorption and diffusion databases (SDB/DDB) have been firstly developed as an important basis for the H12 performance assessment (PA) of high-level radioactive waste disposal in Japan, and have been provided through the Web. JAEA has been and is continuing to improve and update the SDB/DDB in view of potential future data needs, focusing on assuring the desired quality level and testing the usefulness of the existing databases for possible applications to parameter-setting for the deep geological environment. The new web-based sorption and diffusion database system (JAEA-SDB/DDB) has been developed to utilize quality assuring procedure and to allow effective application for parameter setting, by adding the following functions to the existing database; - consistency and linkage between sorption and diffusion database - effective utilization of quality assuring (QA) guideline and categolized QA data - additional function for estimating of parameters and graphing of relation between parameters - counting and summarizing function for effective access to respective data for parameter setting. In the present report, practical examples were illustrated regarding the applicability of the database system to the parameter setting by using additional functions such as QA information and data estimation. This database system is expected to make it possible to obtain quick overview of the available data from the database, and to have suitable access to the respective data for parameter-setting for performance assessment and parameter-deriving for mechanistic modeling in traceable and transparent manner. (author)

Database Description - SSBD | LSDB Archive [Life Science Database Archive metadata

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Full Text Available base Description General information of database Database name SSBD Alternative nam...ss 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan, RIKEN Quantitative Biology Center Shuichi Onami E-mail: Database... classification Other Molecular Biology Databases Database classification Dynamic databa...elegans Taxonomy ID: 6239 Taxonomy Name: Escherichia coli Taxonomy ID: 562 Database description Systems Scie...i Onami Journal: Bioinformatics/April, 2015/Volume 31, Issue 7 External Links: Original website information Database

Database Description - GETDB | LSDB Archive [Life Science Database Archive metadata

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Full Text Available abase Description General information of database Database name GETDB Alternative n...ame Gal4 Enhancer Trap Insertion Database DOI 10.18908/lsdba.nbdc00236-000 Creator Creator Name: Shigeo Haya... Chuo-ku, Kobe 650-0047 Tel: +81-78-306-3185 FAX: +81-78-306-3183 E-mail: Database classification Expression... Invertebrate genome database Organism Taxonomy Name: Drosophila melanogaster Taxonomy ID: 7227 Database des...riginal website information Database maintenance site Drosophila Genetic Resource

JICST Factual DatabaseJICST Chemical Substance Safety Regulation Database

Science.gov (United States)

Abe, Atsushi; Sohma, Tohru

JICST Chemical Substance Safety Regulation Database is based on the Database of Safety Laws for Chemical Compounds constructed by Japan Chemical Industry Ecology-Toxicology & Information Center (JETOC) sponsored by the Sience and Technology Agency in 1987. JICST has modified JETOC database system, added data and started the online service through JOlS-F (JICST Online Information Service-Factual database) in January 1990. JICST database comprises eighty-three laws and fourteen hundred compounds. The authors outline the database, data items, files and search commands. An example of online session is presented.

Deep data science to prevent and treat growth faltering in Maya children.

Science.gov (United States)

Varela-Silva, M I; Bogin, B; Sobral, J A G; Dickinson, F; Monserrat-Revillo, S

2016-06-01

The Maya people are descended from the indigenous inhabitants of southern Mexico, Guatemala and adjacent regions of Central America. In Guatemala, 50% of infants and children are stunted (very low height-for-age), and some rural Maya regions have >70% children stunted. A large, longitudinal, intergenerational database was created to (1) provide deep data to prevent and treat somatic growth faltering and impaired neurocognitive development, (2) detect key dependencies and predictive relations between highly complex, time-varying, and interacting biological and cultural variables and (3) identify targeted multifactorial intervention strategies for field testing and validation. Contributions to this database included data from the Universidad del Valle de Guatemala Longitudinal Study of Child and Adolescent Development, child growth and intergenerational studies among the Maya in Mexico and studies about Maya migrants in the United States.

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...

Pulotu: Database of Austronesian Supernatural Beliefs and Practices

Science.gov (United States)

Watts, Joseph; Sheehan, Oliver; Greenhill, Simon J.; Gomes-Ng, Stephanie; Atkinson, Quentin D.; Bulbulia, Joseph; Gray, Russell D.

2015-01-01

Scholars have debated naturalistic theories of religion for thousands of years, but only recently have scientists begun to test predictions empirically. Existing databases contain few variables on religion, and are subject to Galton’s Problem because they do not sufficiently account for the non-independence of cultures or systematically differentiate the traditional states of cultures from their contemporary states. Here we present Pulotu: the first quantitative cross-cultural database purpose-built to test evolutionary hypotheses of supernatural beliefs and practices. The Pulotu database documents the remarkable diversity of the Austronesian family of cultures, which originated in Taiwan, spread west to Madagascar and east to Easter Island–a region covering over half the world’s longitude. The focus of Austronesian beliefs range from localised ancestral spirits to powerful creator gods. A wide range of practices also exist, such as headhunting, elaborate tattooing, and the construction of impressive monuments. Pulotu is freely available, currently contains 116 cultures, and has 80 variables describing supernatural beliefs and practices, as well as social and physical environments. One major advantage of Pulotu is that it has separate sections on the traditional states of cultures, the post-contact history of cultures, and the contemporary states of cultures. A second major advantage is that cultures are linked to a language-based family tree, enabling the use phylogenetic methods, which can be used to address Galton’s Problem by accounting for common ancestry, to infer deep prehistory, and to model patterns of trait evolution over time. We illustrate the power of phylogenetic methods by performing an ancestral state reconstruction on the Pulotu variable “headhunting", finding evidence that headhunting was practiced in proto-Austronesian culture. Quantitative cross-cultural databases explicitly linking cultures to a phylogeny have the potential to

Integration of deep transcriptome and proteome analyses reveals the components of alkaloid metabolism in opium poppy cell cultures.

Science.gov (United States)

Desgagné-Penix, Isabel; Khan, Morgan F; Schriemer, David C; Cram, Dustin; Nowak, Jacek; Facchini, Peter J

2010-11-18

Papaver somniferum (opium poppy) is the source for several pharmaceutical benzylisoquinoline alkaloids including morphine, the codeine and sanguinarine. In response to treatment with a fungal elicitor, the biosynthesis and accumulation of sanguinarine is induced along with other plant defense responses in opium poppy cell cultures. The transcriptional induction of alkaloid metabolism in cultured cells provides an opportunity to identify components of this process via the integration of deep transcriptome and proteome databases generated using next-generation technologies. A cDNA library was prepared for opium poppy cell cultures treated with a fungal elicitor for 10 h. Using 454 GS-FLX Titanium pyrosequencing, 427,369 expressed sequence tags (ESTs) with an average length of 462 bp were generated. Assembly of these sequences yielded 93,723 unigenes, of which 23,753 were assigned Gene Ontology annotations. Transcripts encoding all known sanguinarine biosynthetic enzymes were identified in the EST database, 5 of which were represented among the 50 most abundant transcripts. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) of total protein extracts from cell cultures treated with a fungal elicitor for 50 h facilitated the identification of 1,004 proteins. Proteins were fractionated by one-dimensional SDS-PAGE and digested with trypsin prior to LC-MS/MS analysis. Query of an opium poppy-specific EST database substantially enhanced peptide identification. Eight out of 10 known sanguinarine biosynthetic enzymes and many relevant primary metabolic enzymes were represented in the peptide database. The integration of deep transcriptome and proteome analyses provides an effective platform to catalogue the components of secondary metabolism, and to identify genes encoding uncharacterized enzymes. The establishment of corresponding transcript and protein databases generated by next-generation technologies in a system with a well-defined metabolite profile facilitates

Database Description - KAIKOcDNA | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us KAIKOcDNA Database Description General information of database Database name KAIKOcDNA Alter...National Institute of Agrobiological Sciences Akiya Jouraku E-mail : Database cla...ssification Nucleotide Sequence Databases Organism Taxonomy Name: Bombyx mori Taxonomy ID: 7091 Database des...rnal: G3 (Bethesda) / 2013, Sep / vol.9 External Links: Original website information Database maintenance si...available URL of Web services - Need for user registration Not available About This Database Database

Download - Trypanosomes Database | LSDB Archive [Life Science Database Archive metadata

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Full Text Available List Contact us Trypanosomes Database Download First of all, please read the license of this database. Data ...1.4 KB) Simple search and download Downlaod via FTP FTP server is sometimes jammed. If it is, access [here]. About This Database Data...base Description Download License Update History of This Database Site Policy | Contact Us Download - Trypanosomes Database | LSDB Archive ...

License - Arabidopsis Phenome Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Arabidopsis Phenome Database License License to Use This Database Last updated : 2017/02/27 You may use this database...cense specifies the license terms regarding the use of this database and the requirements you must follow in using this database.... The license for this database is specified in the Creative ...Commons Attribution-Share Alike 4.0 International . If you use data from this database, please be sure attribute this database...ative Commons Attribution-Share Alike 4.0 International is found here . With regard to this database, you ar

Duplex imaging of residual venous obstruction to guide duration of therapy for lower extremity deep venous thrombosis.

Science.gov (United States)

Stephenson, Elliot J P; Liem, Timothy K

2015-07-01

Clinical trials have shown that the presence of ultrasound-identified residual venous obstruction (RVO) on follow-up scanning may be associated with an elevated risk for recurrence, thus providing a potential tool to help determine the optimal duration of anticoagulant therapy. We performed a systematic review to evaluate the clinical utility of post-treatment duplex imaging in predicting venous thromboembolism (VTE) recurrence and in adjusting duration of anticoagulation. The Ovid MEDLINE Database, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Database of Abstracts of Reviews of Effects were queried for the terms residual thrombus or obstruction, duration of therapy, deep vein thrombosis, deep venous thrombosis, DVT, venous thromboembolism, VTE, antithrombotic therapy, and anticoagulation, and 228 studies were selected for review. Six studies determined the rate of VTE recurrence on the basis of the presence or absence of RVO. Findings on venous ultrasound scans frequently remained abnormal in 38% to 80% of patients, despite at least 3 months of therapeutic anticoagulation. In evaluating for VTE recurrence, the definition of RVO varied widely in the literature. Some studies have shown an association between RVO and VTE recurrence, whereas other studies have not. Overall, the presence of RVO is a mild risk factor for recurrence (odds ratio, 1.3-2.0), but only when surveillance imaging is performed soon after the index deep venous thrombosis (3 months). RVO is a mild risk factor for VTE recurrence. The presence or absence of ultrasound-identified RVO has a limited role in guiding the duration of therapeutic anticoagulation. Further research is needed to evaluate its utility relative to other known risk factors for VTE recurrence. Copyright © 2015 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Database Description - AcEST | LSDB Archive [Life Science Database Archive metadata

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Full Text Available abase Description General information of database Database name AcEST Alternative n...hi, Tokyo-to 192-0397 Tel: +81-42-677-1111(ext.3654) E-mail: Database classificat...eneris Taxonomy ID: 13818 Database description This is a database of EST sequences of Adiantum capillus-vene...(3): 223-227. External Links: Original website information Database maintenance site Plant Environmental Res...base Database Description Download License Update History of This Database Site Policy | Contact Us Database Description - AcEST | LSDB Archive ...

License - SKIP Stemcell Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us SKIP Stemcell Database License License to Use This Database Last updated : 2017/03/13 You may use this database...specifies the license terms regarding the use of this database and the requirements you must follow in using this database.... The license for this database is specified in the Creative Common...s Attribution-Share Alike 4.0 International . If you use data from this database, please be sure attribute this database...al ... . The summary of the Creative Commons Attribution-Share Alike 4.0 International is found here . With regard to this database

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

Science.gov (United States)

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

Recognition of emotions using multimodal physiological signals and an ensemble deep learning model.

Science.gov (United States)

Yin, Zhong; Zhao, Mengyuan; Wang, Yongxiong; Yang, Jingdong; Zhang, Jianhua

2017-03-01

Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions. In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed for recognizing emotions, in which the deep structure is identified based on a physiological-data-driven approach. Each SAE consists of three hidden layers to filter the unwanted noise in the physiological features and derives the stable feature representations. An additional deep model is used to achieve the SAE ensembles. The physiological features are split into several subsets according to different feature extraction approaches with each subset separately encoded by a SAE. The derived SAE abstractions are combined according to the physiological modality to create six sets of encodings, which are then fed to a three-layer, adjacent-graph-based network for feature fusion. The fused features are used to recognize binary arousal or valence states. DEAP multimodal database was employed to validate the performance of the MESAE. By comparing with the best existing emotion classifier, the mean of classification rate and F-score improves by 5.26%. The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

KALIMER database development

Energy Technology Data Exchange (ETDEWEB)

Jeong, Kwan Seong; Lee, Yong Bum; Jeong, Hae Yong; Ha, Kwi Seok

2003-03-01

KALIMER database is an advanced database to utilize the integration management for liquid metal reactor design technology development using Web applications. KALIMER design database is composed of results database, Inter-Office Communication (IOC), 3D CAD database, and reserved documents database. Results database is a research results database during all phase for liquid metal reactor design technology development of mid-term and long-term nuclear R and D. IOC is a linkage control system inter sub project to share and integrate the research results for KALIMER. 3D CAD database is a schematic overview for KALIMER design structure. And reserved documents database is developed to manage several documents and reports since project accomplishment.

KALIMER database development

International Nuclear Information System (INIS)

Jeong, Kwan Seong; Lee, Yong Bum; Jeong, Hae Yong; Ha, Kwi Seok

2003-03-01

KALIMER database is an advanced database to utilize the integration management for liquid metal reactor design technology development using Web applications. KALIMER design database is composed of results database, Inter-Office Communication (IOC), 3D CAD database, and reserved documents database. Results database is a research results database during all phase for liquid metal reactor design technology development of mid-term and long-term nuclear R and D. IOC is a linkage control system inter sub project to share and integrate the research results for KALIMER. 3D CAD database is a schematic overview for KALIMER design structure. And reserved documents database is developed to manage several documents and reports since project accomplishment

Database Description - RPSD | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available base Description General information of database Database name RPSD Alternative nam...e Rice Protein Structure Database DOI 10.18908/lsdba.nbdc00749-000 Creator Creator Name: Toshimasa Yamazaki ... Ibaraki 305-8602, Japan National Institute of Agrobiological Sciences Toshimasa Yamazaki E-mail : Databas...e classification Structure Databases - Protein structure Organism Taxonomy Name: Or...or name(s): Journal: External Links: Original website information Database maintenance site National Institu

Database Description - FANTOM5 | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us FANTOM5 Database Description General information of database Database name FANTOM5 Alternati...me: Rattus norvegicus Taxonomy ID: 10116 Taxonomy Name: Macaca mulatta Taxonomy ID: 9544 Database descriptio...l Links: Original website information Database maintenance site RIKEN Center for Life Science Technologies, ...ilable Web services Not available URL of Web services - Need for user registration Not available About This Database Database... Description Download License Update History of This Database Site Policy | Contact Us Database Description - FANTOM5 | LSDB Archive ...

NoSQL databases

OpenAIRE

Mrozek, Jakub

2012-01-01

This thesis deals with database systems referred to as NoSQL databases. In the second chapter, I explain basic terms and the theory of database systems. A short explanation is dedicated to database systems based on the relational data model and the SQL standardized query language. Chapter Three explains the concept and history of the NoSQL databases, and also presents database models, major features and the use of NoSQL databases in comparison with traditional database systems. In the fourth ...

A Magnetic Petrology Database for Satellite Magnetic Anomaly Interpretations

Science.gov (United States)

Nazarova, K.; Wasilewski, P.; Didenko, A.; Genshaft, Y.; Pashkevich, I.

2002-05-01

A Magnetic Petrology Database (MPDB) is now being compiled at NASA/Goddard Space Flight Center in cooperation with Russian and Ukrainian Institutions. The purpose of this database is to provide the geomagnetic community with a comprehensive and user-friendly method of accessing magnetic petrology data via Internet for more realistic interpretation of satellite magnetic anomalies. Magnetic Petrology Data had been accumulated in NASA/Goddard Space Flight Center, United Institute of Physics of the Earth (Russia) and Institute of Geophysics (Ukraine) over several decades and now consists of many thousands of records of data in our archives. The MPDB was, and continues to be in big demand especially since recent launching in near Earth orbit of the mini-constellation of three satellites - Oersted (in 1999), Champ (in 2000), and SAC-C (in 2000) which will provide lithospheric magnetic maps with better spatial and amplitude resolution (about 1 nT). The MPDB is focused on lower crustal and upper mantle rocks and will include data on mantle xenoliths, serpentinized ultramafic rocks, granulites, iron quartzites and rocks from Archean-Proterozoic metamorphic sequences from all around the world. A substantial amount of data is coming from the area of unique Kursk Magnetic Anomaly and Kola Deep Borehole (which recovered 12 km of continental crust). A prototype MPDB can be found on the Geodynamics Branch web server of Goddard Space Flight Center at http://core2.gsfc.nasa.gov/terr_mag/magnpetr.html. The MPDB employs a searchable relational design and consists of 7 interrelated tables. The schema of database is shown at http://core2.gsfc.nasa.gov/terr_mag/doc.html. MySQL database server was utilized to implement MPDB. The SQL (Structured Query Language) is used to query the database. To present the results of queries on WEB and for WEB programming we utilized PHP scripting language and CGI scripts. The prototype MPDB is designed to search database by major satellite magnetic

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...

Database Description - DMPD | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available base Description General information of database Database name DMPD Alternative nam...e Dynamic Macrophage Pathway CSML Database DOI 10.18908/lsdba.nbdc00558-000 Creator Creator Name: Masao Naga...ty of Tokyo 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639 Tel: +81-3-5449-5615 FAX: +83-3-5449-5442 E-mail: Database...606 Taxonomy Name: Mammalia Taxonomy ID: 40674 Database description DMPD collects...e(s) Article title: Author name(s): Journal: External Links: Original website information Database maintenan

Database Dump - fRNAdb | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us fRNAdb Database Dump Data detail Data name Database Dump DOI 10.18908/lsdba.nbdc00452-002 De... data (tab separeted text) Data file File name: Database_Dump File URL: ftp://ftp....biosciencedbc.jp/archive/frnadb/LATEST/Database_Dump File size: 673 MB Simple search URL - Data acquisition...s. Data analysis method - Number of data entries 4 files - About This Database Database Description Download... License Update History of This Database Site Policy | Contact Us Database Dump - fRNAdb | LSDB Archive ...

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...

Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles.

Science.gov (United States)

Zhang, Duona; Ding, Wenrui; Zhang, Baochang; Xie, Chunyu; Li, Hongguang; Liu, Chunhui; Han, Jungong

2018-03-20

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network.

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...

DOT Online Database

Science.gov (United States)

Page Home Table of Contents Contents Search Database Search Login Login Databases Advisory Circulars accessed by clicking below: Full-Text WebSearch Databases Database Records Date Advisory Circulars 2092 5 data collection and distribution policies. Document Database Website provided by MicroSearch

SFCOMPO: A new database of isotopic compositions of spent nuclear fuel

International Nuclear Information System (INIS)

Michel-Sendis, Franco; Gauld, Ian

2014-01-01

The numerous applications of nuclear fuel depletion simulations impact all areas related to nuclear safety. They are at the basis of, inter alia, spent fuel criticality safety analyses, reactor physics calculations, burn-up credit methodologies, decay heat thermal analyses, radiation shielding, reprocessing, waste management, deep geological repository safety studies and safeguards. Experimentally determined nuclide compositions of well-characterised spent nuclear fuel (SNF) samples are used to validate the accuracy of depletion code predictions for a given burn-up. At the same time, the measured nuclide composition of the sample is used to determine the burn-up of the fuel. It is therefore essential to have a reliable and well-qualified database of measured nuclide concentrations and relevant reactor operational data that can be used as experimental benchmark data for depletion codes and associated nuclear data. The Spent Fuel Isotopic Composition Database (SFCOMPO) has been hosted by the NEA since 2001. In 2012, a collaborative effort led by the NEA Data Bank and Oak Ridge National Laboratory (ORNL) in the United States, under the guidance of the NEA Expert Group on Assay Data of Spent Nuclear Fuel (EGADSNF) of the Working Party on Nuclear Criticality Safety (WPNCS), has resulted in the creation of an enhanced relational database structure and a significant expansion of the SFCOMPO database, which now contains experimental assay data for a wider selection of international reactor designs. The new database was released online in 2014. This new SFCOMPO database aims to provide access to open experimental SNF assay data to ensure their preservation and to facilitate their qualification as evaluated assay data suitable for the validation of methodologies used to predict the composition of irradiated nuclear fuel. Having a centralised, internationally reviewed database that makes these data openly available for a large selection of international reactor designs is of

Databases

Digital Repository Service at National Institute of Oceanography (India)

Kunte, P.D.

Information on bibliographic as well as numeric/textual databases relevant to coastal geomorphology has been included in a tabular form. Databases cover a broad spectrum of related subjects like coastal environment and population aspects, coastline...

Towards the renewal of the NEA Thermochemical Database

International Nuclear Information System (INIS)

Ragoussi, Maria-Eleni; Costa, Davide; Bossant, Manuel

2015-01-01

The Thermochemical Database (TDB) Project was created three decades ago as a joint undertaking of the NEA Radioactive Waste Management Committee and the NEA Data Bank. The project involves the collection of high-quality and traceable thermochemical data for a set of elements (mainly minor actinides and fission products) relevant to geophysical modelling of deep geological repositories. Funding comes from 15 participating organisations, primarily national nuclear waste authorities and research institutions. The quantities that are stored in the TDB database are: the standard molar Gibbs energy and enthalpy of formation, the standard molar entropy and, when available, the heat capacity at constant pressure, together with their uncertainty intervals. Reaction data are also provided: equilibrium constant of reaction, molar Gibbs energy of reaction, molar enthalpy of reaction and molar entropy of reaction. Data assessment is carried out by teams of expert reviewers through an in-depth analysis of the available scientific literature, following strict guidelines defined by the NEA to ensure the accuracy and self-consistency of the adopted datasets. Thermochemical data that has been evaluated and selected over the years have been published in the 13 volumes of the Chemical Thermodynamics series. They are also stored in a database that is updated each time the study of a new element is completed. The TDB selected data are made available to external third parties through the NEA web site where data extracted from the database can be displayed and downloaded as plain text files. Following recent recommendations of the Task Force on the Future Programme of the NEA Data Bank to enhance scientific expertise and user services, a renewal of the software managing the TDB database is being undertaken. The software currently used was designed 20 years ago and is becoming obsolete. Redesigning the application will provide an opportunity to correct current shortcomings and to develop

Database Description - eSOL | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available base Description General information of database Database name eSOL Alternative nam...eator Affiliation: The Research and Development of Biological Databases Project, National Institute of Genet...nology 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501 Japan Email: Tel.: +81-45-924-5785 Database... classification Protein sequence databases - Protein properties Organism Taxonomy Name: Escherichia coli Taxonomy ID: 562 Database...i U S A. 2009 Mar 17;106(11):4201-6. External Links: Original website information Database maintenance site

"Mr. Database" : Jim Gray and the History of Database Technologies.

Science.gov (United States)

Hanwahr, Nils C

2017-12-01

Although the widespread use of the term "Big Data" is comparatively recent, it invokes a phenomenon in the developments of database technology with distinct historical contexts. The database engineer Jim Gray, known as "Mr. Database" in Silicon Valley before his disappearance at sea in 2007, was involved in many of the crucial developments since the 1970s that constitute the foundation of exceedingly large and distributed databases. Jim Gray was involved in the development of relational database systems based on the concepts of Edgar F. Codd at IBM in the 1970s before he went on to develop principles of Transaction Processing that enable the parallel and highly distributed performance of databases today. He was also involved in creating forums for discourse between academia and industry, which influenced industry performance standards as well as database research agendas. As a co-founder of the San Francisco branch of Microsoft Research, Gray increasingly turned toward scientific applications of database technologies, e. g. leading the TerraServer project, an online database of satellite images. Inspired by Vannevar Bush's idea of the memex, Gray laid out his vision of a Personal Memex as well as a World Memex, eventually postulating a new era of data-based scientific discovery termed "Fourth Paradigm Science". This article gives an overview of Gray's contributions to the development of database technology as well as his research agendas and shows that central notions of Big Data have been occupying database engineers for much longer than the actual term has been in use.

Mathematics for Databases

NARCIS (Netherlands)

ir. Sander van Laar

2007-01-01

A formal description of a database consists of the description of the relations (tables) of the database together with the constraints that must hold on the database. Furthermore the contents of a database can be retrieved using queries. These constraints and queries for databases can very well be

Clinical and bacteriological influence of diabetes mellitus on deep neck infection: Systematic review and meta-analysis.

Science.gov (United States)

Hidaka, Hiroshi; Yamaguchi, Takuhiro; Hasegawa, Jun; Yano, Hisakazu; Kakuta, Risako; Ozawa, Daiki; Nomura, Kazuhiro; Katori, Yukio

2015-10-01

Diabetes mellitus has been recognized as the most common systemic disease associated with deep neck infection. We report the first systematic review and meta-analysis of the influence of diabetes on clinical and bacteriological characteristics of deep neck infection. Articles were retrieved from PubMed, EMBASE, and the Japan Medical Abstracts Society database. A critical review of 227 studies identified 20 studies eligible for quantitative synthesis. Diabetes was associated with higher prevalences of multispace spread of infection, complications, and failure to identify pathogenesis, with risk ratios (RRs) of 1.96, 2.42, and 1.29, respectively. Bacteriologically, patients with diabetes showed a higher prevalence of culture identification of Klebsiella pneumoniae (RR, 3.28), and lower prevalences of Streptococcus spp. (RR, 0.57) and anaerobes (RR, 0.54). Deep neck infection with diabetes differs from that without in several clinical aspects. Again, bacteriological differences imply that diabetic infections might be populated by different bacterial flora. © 2014 Wiley Periodicals, Inc.

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.

A deep learning approach for fetal QRS complex detection.

Science.gov (United States)

Zhong, Wei; Liao, Lijuan; Guo, Xuemei; Wang, Guoli

2018-04-20

Non-invasive foetal electrocardiography (NI-FECG) has the potential to provide more additional clinical information for detecting and diagnosing fetal diseases. We propose and demonstrate a deep learning approach for fetal QRS complex detection from raw NI-FECG signals by using a convolutional neural network (CNN) model. The main objective is to investigate whether reliable fetal QRS complex detection performance can still be obtained from features of single-channel NI-FECG signals, without canceling maternal ECG (MECG) signals. A deep learning method is proposed for recognizing fetal QRS complexes. Firstly, we collect data from set-a of the PhysioNet/computing in Cardiology Challenge database. The sample entropy method is used for signal quality assessment. Part of the bad quality signals is excluded in the further analysis. Secondly, in the proposed method, the features of raw NI-FECG signals are normalized before they are fed to a CNN classifier to perform fetal QRS complex detection. We use precision, recall, F-measure and accuracy as the evaluation metrics to assess the performance of fetal QRS complex detection. The proposed deep learning method can achieve relatively high precision (75.33%), recall (80.54%), and F-measure scores (77.85%) compared with three other well-known pattern classification methods, namely KNN, naive Bayes and SVM. the proposed deep learning method can attain reliable fetal QRS complex detection performance from the raw NI-FECG signals without canceling MECG signals. In addition, the influence of different activation functions and signal quality assessment on classification performance are evaluated, and results show that Relu outperforms the Sigmoid and Tanh on this particular task, and better classification performance is obtained with the signal quality assessment step in this study.

Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

Science.gov (United States)

Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

2017-07-01

Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

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.

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

Science.gov (United States)

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

2015-09-01

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

Database development and management

CERN Document Server

Chao, Lee

2006-01-01

Introduction to Database Systems Functions of a DatabaseDatabase Management SystemDatabase ComponentsDatabase Development ProcessConceptual Design and Data Modeling Introduction to Database Design Process Understanding Business ProcessEntity-Relationship Data Model Representing Business Process with Entity-RelationshipModelTable Structure and NormalizationIntroduction to TablesTable NormalizationTransforming Data Models to Relational Databases .DBMS Selection Transforming Data Models to Relational DatabasesEnforcing ConstraintsCreating Database for Business ProcessPhysical Design and Database

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...

Database reliability engineering designing and operating resilient database systems

CERN Document Server

Campbell, Laine

2018-01-01

The infrastructure-as-code revolution in IT is also affecting database administration. With this practical book, developers, system administrators, and junior to mid-level DBAs will learn how the modern practice of site reliability engineering applies to the craft of database architecture and operations. Authors Laine Campbell and Charity Majors provide a framework for professionals looking to join the ranks of today’s database reliability engineers (DBRE). You’ll begin by exploring core operational concepts that DBREs need to master. Then you’ll examine a wide range of database persistence options, including how to implement key technologies to provide resilient, scalable, and performant data storage and retrieval. With a firm foundation in database reliability engineering, you’ll be ready to dive into the architecture and operations of any modern database. This book covers: Service-level requirements and risk management Building and evolving an architecture for operational visibility ...

Solving Relational Database Problems with ORDBMS in an Advanced Database Course

Science.gov (United States)

Wang, Ming

2011-01-01

This paper introduces how to use the object-relational database management system (ORDBMS) to solve relational database (RDB) problems in an advanced database course. The purpose of the paper is to provide a guideline for database instructors who desire to incorporate the ORDB technology in their traditional database courses. The paper presents…

Generalized Database Management System Support for Numeric Database Environments.

Science.gov (United States)

Dominick, Wayne D.; Weathers, Peggy G.

1982-01-01

This overview of potential for utilizing database management systems (DBMS) within numeric database environments highlights: (1) major features, functions, and characteristics of DBMS; (2) applicability to numeric database environment needs and user needs; (3) current applications of DBMS technology; and (4) research-oriented and…

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 ...

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.

Directory of Open Access Journals (Sweden)

Ehsaneddin Asgari

Full Text Available We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec to refer to biological sequences in general with protein-vectors (ProtVec for proteins (amino-acid sequences and gene-vectors (GeneVec for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups. Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be

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.

License - Trypanosomes Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data List Contact us Trypanoso... Attribution-Share Alike 2.1 Japan . If you use data from this database, please be sure attribute this database as follows: Trypanoso...nse Update History of This Database Site Policy | Contact Us License - Trypanosomes Database | LSDB Archive ...

Integration of deep transcriptome and proteome analyses reveals the components of alkaloid metabolism in opium poppy cell cultures

Directory of Open Access Journals (Sweden)

Schriemer David C

2010-11-01

Full Text Available Abstract Background Papaver somniferum (opium poppy is the source for several pharmaceutical benzylisoquinoline alkaloids including morphine, the codeine and sanguinarine. In response to treatment with a fungal elicitor, the biosynthesis and accumulation of sanguinarine is induced along with other plant defense responses in opium poppy cell cultures. The transcriptional induction of alkaloid metabolism in cultured cells provides an opportunity to identify components of this process via the integration of deep transcriptome and proteome databases generated using next-generation technologies. Results A cDNA library was prepared for opium poppy cell cultures treated with a fungal elicitor for 10 h. Using 454 GS-FLX Titanium pyrosequencing, 427,369 expressed sequence tags (ESTs with an average length of 462 bp were generated. Assembly of these sequences yielded 93,723 unigenes, of which 23,753 were assigned Gene Ontology annotations. Transcripts encoding all known sanguinarine biosynthetic enzymes were identified in the EST database, 5 of which were represented among the 50 most abundant transcripts. Liquid chromatography-tandem mass spectrometry (LC-MS/MS of total protein extracts from cell cultures treated with a fungal elicitor for 50 h facilitated the identification of 1,004 proteins. Proteins were fractionated by one-dimensional SDS-PAGE and digested with trypsin prior to LC-MS/MS analysis. Query of an opium poppy-specific EST database substantially enhanced peptide identification. Eight out of 10 known sanguinarine biosynthetic enzymes and many relevant primary metabolic enzymes were represented in the peptide database. Conclusions The integration of deep transcriptome and proteome analyses provides an effective platform to catalogue the components of secondary metabolism, and to identify genes encoding uncharacterized enzymes. The establishment of corresponding transcript and protein databases generated by next-generation technologies in a

Federal databases

International Nuclear Information System (INIS)

Welch, M.J.; Welles, B.W.

1988-01-01

Accident statistics on all modes of transportation are available as risk assessment analytical tools through several federal agencies. This paper reports on the examination of the accident databases by personal contact with the federal staff responsible for administration of the database programs. This activity, sponsored by the Department of Energy through Sandia National Laboratories, is an overview of the national accident data on highway, rail, air, and marine shipping. For each mode, the definition or reporting requirements of an accident are determined and the method of entering the accident data into the database is established. Availability of the database to others, ease of access, costs, and who to contact were prime questions to each of the database program managers. Additionally, how the agency uses the accident data was of major interest

The YH database: the first Asian diploid genome database

DEFF Research Database (Denmark)

Li, Guoqing; Ma, Lijia; Song, Chao

2009-01-01

genome consensus. The YH database is currently one of the three personal genome database, organizing the original data and analysis results in a user-friendly interface, which is an endeavor to achieve fundamental goals for establishing personal medicine. The database is available at http://yh.genomics.org.cn....

Database Description - tRNADB-CE | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data List Contact us tRNAD...B-CE Database Description General information of database Database name tRNADB-CE Alter...CC BY-SA Detail Background and funding Name: MEXT Integrated Database Project Reference(s) Article title: tRNAD... 2009 Jan;37(Database issue):D163-8. External Links: Article title: tRNADB-CE 2011: tRNA gene database curat...n Download License Update History of This Database Site Policy | Contact Us Database Description - tRNADB-CE | LSDB Archive ...

Deep generative learning for automated EHR diagnosis of traditional Chinese medicine.

Science.gov (United States)

Liang, Zhaohui; Liu, Jun; Ou, Aihua; Zhang, Honglai; Li, Ziping; Huang, Jimmy Xiangji

2018-05-04

information retrieval over the conventional shallow models. It is able to capture the features of both plain text and the highly-structured database of EMR data. The performance of the deep model is superior to the conventional shallow learning models such as SVM and DT. It is an appropriate knowledge-learning model for information retrieval of EMR system. Therefore, deep learning provides a good solution to improve the performance of CAMDM systems. Copyright © 2018. Published by Elsevier B.V.

INFRASTRUCTURE IN RAIL TRANSPORT IN BRAZIL

Directory of Open Access Journals (Sweden)

Debora Brito dos Santos

2018-03-01

Full Text Available Rail transport is the second mode most used in the country, due to the low cost of transporting large volumes over long distances. Although there was no investment in the expansion for a long time, after privatization there was a resumption of maintenance and expansion of the railways, but it is far from achieving international quality and performance indices. The objective of this work was a study on the rail transport in the country, to raise the structure of the Brazilian railway network. For the development of this article, the method used was bibliographic research in books, articles and websites. Although the Brazilian railway network is just over 12% of the railroad network in the United States, after the privatization of the sector, growth has resumed. The Bioceanic, a railroad that will connect the Atlantic Ocean to the Pacific Ocean, will improve the flow of agricultural production to overseas

A Situation Awareness Assistant for Human Deep Space Exploration

Science.gov (United States)

Boy, Guy A.; Platt, Donald

2013-01-01

This paper presents the development and testing of a Virtual Camera (VC) system to improve astronaut and mission operations situation awareness while exploring other planetary bodies. In this embodiment, the VC is implemented using a tablet-based computer system to navigate through inter active database application. It is claimed that the advanced interaction media capability of the VC can improve situation awareness as the distribution of hu man space exploration roles change in deep space exploration. The VC is being developed and tested for usability and capability to improve situation awareness. Work completed thus far as well as what is needed to complete the project will be described. Planned testing will also be described.

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 ...

Database Administrator

Science.gov (United States)

Moore, Pam

2010-01-01

The Internet and electronic commerce (e-commerce) generate lots of data. Data must be stored, organized, and managed. Database administrators, or DBAs, work with database software to find ways to do this. They identify user needs, set up computer databases, and test systems. They ensure that systems perform as they should and add people to the…

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 ...

Database Description - TMFunction | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available sidue (or mutant) in a protein. The experimental data are collected from the literature both by searching th...the sequence database, UniProt, structural database, PDB, and literature database

Designing for Peta-Scale in the LSST Database

Science.gov (United States)

Kantor, J.; Axelrod, T.; Becla, J.; Cook, K.; Nikolaev, S.; Gray, J.; Plante, R.; Nieto-Santisteban, M.; Szalay, A.; Thakar, A.

2007-10-01

The Large Synoptic Survey Telescope (LSST), a proposed ground-based 8.4 m telescope with a 10 deg^2 field of view, will generate 15 TB of raw images every observing night. When calibration and processed data are added, the image archive, catalogs, and meta-data will grow 15 PB yr^{-1} on average. The LSST Data Management System (DMS) must capture, process, store, index, replicate, and provide open access to this data. Alerts must be triggered within 30 s of data acquisition. To do this in real-time at these data volumes will require advances in data management, database, and file system techniques. This paper describes the design of the LSST DMS and emphasizes features for peta-scale data. The LSST DMS will employ a combination of distributed database and file systems, with schema, partitioning, and indexing oriented for parallel operations. Image files are stored in a distributed file system with references to, and meta-data from, each file stored in the databases. The schema design supports pipeline processing, rapid ingest, and efficient query. Vertical partitioning reduces disk input/output requirements, horizontal partitioning allows parallel data access using arrays of servers and disks. Indexing is extensive, utilizing both conventional RAM-resident indexes and column-narrow, row-deep tag tables/covering indices that are extracted from tables that contain many more attributes. The DMS Data Access Framework is encapsulated in a middleware framework to provide a uniform service interface to all framework capabilities. This framework will provide the automated work-flow, replication, and data analysis capabilities necessary to make data processing and data quality analysis feasible at this scale.

PrimateLit Database

Science.gov (United States)

Primate Info Net Related Databases NCRR PrimateLit: A bibliographic database for primatology Top of any problems with this service. We welcome your feedback. The PrimateLit database is no longer being Resources, National Institutes of Health. The database is a collaborative project of the Wisconsin Primate

ChimerDB 3.0: an enhanced database for fusion genes from cancer transcriptome and literature data mining.

Science.gov (United States)

Lee, Myunggyo; Lee, Kyubum; Yu, Namhee; Jang, Insu; Choi, Ikjung; Kim, Pora; Jang, Ye Eun; Kim, Byounggun; Kim, Sunkyu; Lee, Byungwook; Kang, Jaewoo; Lee, Sanghyuk

2017-01-04

Fusion gene is an important class of therapeutic targets and prognostic markers in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data and manual curations. In this update, the database coverage was enhanced considerably by adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining. ChimerDB 3.0 is composed of three modules of ChimerKB, ChimerPub and ChimerSeq. ChimerKB represents a knowledgebase including 1066 fusion genes with manual curation that were compiled from public resources of fusion genes with experimental evidences. ChimerPub includes 2767 fusion genes obtained from text mining of PubMed abstracts. ChimerSeq module is designed to archive the fusion candidates from deep sequencing data. Importantly, we have analyzed RNA-Seq data of the TCGA project covering 4569 patients in 23 cancer types using two reliable programs of FusionScan and TopHat-Fusion. The new user interface supports diverse search options and graphic representation of fusion gene structure. ChimerDB 3.0 is available at http://ercsb.ewha.ac.kr/fusiongene/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach.

Science.gov (United States)

Han, Hu; K Jain, Anil; Shan, Shiguang; Chen, Xilin

2017-08-10

Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.

License - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Yeast Interacting Proteins Database License to Use This Database Last updated : 2010/02/15 You may use this database...nal License described below. The Standard License specifies the license terms regarding the use of this database... and the requirements you must follow in using this database. The Additional ...the Standard License. Standard License The Standard License for this database is the license specified in th...e Creative Commons Attribution-Share Alike 2.1 Japan . If you use data from this database

NoSQL database scaling

OpenAIRE

Žardin, Norbert

2017-01-01

NoSQL database scaling is a decision, where system resources or financial expenses are traded for database performance or other benefits. By scaling a database, database performance and resource usage might increase or decrease, such changes might have a negative impact on an application that uses the database. In this work it is analyzed how database scaling affect database resource usage and performance. As a results, calculations are acquired, using which database scaling types and differe...

Factors governing the deep ventilation of the Red Sea

KAUST Repository

Papadopoulos, Vassilis P.

2015-11-19

A variety of data based on hydrographic measurements, satellite observations, reanalysis databases, and meteorological observations are used to explore the interannual variability and factors governing the deep water formation in the northern Red Sea. Historical and recent hydrographic data consistently indicate that the ventilation of the near-bottom layer in the Red Sea is a robust feature of the thermohaline circulation. Dense water capable to reach the bottom layers of the Red Sea can be regularly produced mostly inside the Gulfs of Aqaba and Suez. Occasionally, during colder than usual winters, deep water formation may also take place over coastal areas in the northernmost end of the open Red Sea just outside the Gulfs of Aqaba and Suez. However, the origin as well as the amount of deep waters exhibit considerable interannual variability depending not only on atmospheric forcing but also on the water circulation over the northern Red Sea. Analysis of several recent winters shows that the strength of the cyclonic gyre prevailing in the northernmost part of the basin can effectively influence the sea surface temperature (SST) and intensify or moderate the winter surface cooling. Upwelling associated with periods of persistent gyre circulation lowers the SST over the northernmost part of the Red Sea and can produce colder than normal winter SST even without extreme heat loss by the sea surface. In addition, the occasional persistence of the cyclonic gyre feeds the surface layers of the northern Red Sea with nutrients, considerably increasing the phytoplankton biomass.

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

Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles

Directory of Open Access Journals (Sweden)

Duona Zhang

2018-03-01

Full Text Available Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC. It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF method to solve the problem in a unified framework. The contributions include the following: (1 a convolutional neural network (CNN and long short-term memory (LSTM are combined by two different ways without prior knowledge involved; (2 a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs based on a real geographical environment; and (3 experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network.

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.

Assessment and Methods for Supply-Following Loads in Modern Electricity Grids with Deep Renewables Penetration

Science.gov (United States)

2013-12-18

today. Following the course of this trend, grids with deep renewables penetration present a CHAPTER 1. INTRODUCTION 2 family of new challenges and...maps the demand curve. Coal is operated as an intermediate resource, turned on for the course of the day, but turned off at night; this represents a...Internet access and a data storage entity, recording data samples into a MySQL database. Having all of the sensors on a network was important for

DataBase on Demand

International Nuclear Information System (INIS)

Aparicio, R Gaspar; Gomez, D; Wojcik, D; Coz, I Coterillo

2012-01-01

At CERN a number of key database applications are running on user-managed MySQL database services. The database on demand project was born out of an idea to provide the CERN user community with an environment to develop and run database services outside of the actual centralised Oracle based database services. The Database on Demand (DBoD) empowers the user to perform certain actions that had been traditionally done by database administrators, DBA's, providing an enterprise platform for database applications. It also allows the CERN user community to run different database engines, e.g. presently open community version of MySQL and single instance Oracle database server. This article describes a technology approach to face this challenge, a service level agreement, the SLA that the project provides, and an evolution of possible scenarios.

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.

Full Data of Yeast Interacting Proteins Database (Original Version) - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Yeast Interacting Proteins Database Full Data of Yeast Interacting Proteins Database (Origin...al Version) Data detail Data name Full Data of Yeast Interacting Proteins Database (Original Version) DOI 10....18908/lsdba.nbdc00742-004 Description of data contents The entire data in the Yeast Interacting Proteins Database...eir interactions are required. Several sources including YPD (Yeast Proteome Database, Costanzo, M. C., Hoga...ematic name in the SGD (Saccharomyces Genome Database; http://www.yeastgenome.org /). Bait gene name The gen

Energy Consumption Database

Science.gov (United States)

Consumption Database The California Energy Commission has created this on-line database for informal reporting ) classifications. The database also provides easy downloading of energy consumption data into Microsoft Excel (XLSX

Using a Semi-Realistic Database to Support a Database Course

Science.gov (United States)

Yue, Kwok-Bun

2013-01-01

A common problem for university relational database courses is to construct effective databases for instructions and assignments. Highly simplified "toy" databases are easily available for teaching, learning, and practicing. However, they do not reflect the complexity and practical considerations that students encounter in real-world…

DSAP: deep-sequencing small RNA analysis pipeline.

Science.gov (United States)

Huang, Po-Jung; Liu, Yi-Chung; Lee, Chi-Ching; Lin, Wei-Chen; Gan, Richie Ruei-Chi; Lyu, Ping-Chiang; Tang, Petrus

2010-07-01

DSAP is an automated multiple-task web service designed to provide a total solution to analyzing deep-sequencing small RNA datasets generated by next-generation sequencing technology. DSAP uses a tab-delimited file as an input format, which holds the unique sequence reads (tags) and their corresponding number of copies generated by the Solexa sequencing platform. The input data will go through four analysis steps in DSAP: (i) cleanup: removal of adaptors and poly-A/T/C/G/N nucleotides; (ii) clustering: grouping of cleaned sequence tags into unique sequence clusters; (iii) non-coding RNA (ncRNA) matching: sequence homology mapping against a transcribed sequence library from the ncRNA database Rfam (http://rfam.sanger.ac.uk/); and (iv) known miRNA matching: detection of known miRNAs in miRBase (http://www.mirbase.org/) based on sequence homology. The expression levels corresponding to matched ncRNAs and miRNAs are summarized in multi-color clickable bar charts linked to external databases. DSAP is also capable of displaying miRNA expression levels from different jobs using a log(2)-scaled color matrix. Furthermore, a cross-species comparative function is also provided to show the distribution of identified miRNAs in different species as deposited in miRBase. DSAP is available at http://dsap.cgu.edu.tw.

Fire test database

International Nuclear Information System (INIS)

Lee, J.A.

1989-01-01

This paper describes a project recently completed for EPRI by Impell. The purpose of the project was to develop a reference database of fire tests performed on non-typical fire rated assemblies. The database is designed for use by utility fire protection engineers to locate test reports for power plant fire rated assemblies. As utilities prepare to respond to Information Notice 88-04, the database will identify utilities, vendors or manufacturers who have specific fire test data. The database contains fire test report summaries for 729 tested configurations. For each summary, a contact is identified from whom a copy of the complete fire test report can be obtained. Five types of configurations are included: doors, dampers, seals, wraps and walls. The database is computerized. One version for IBM; one for Mac. Each database is accessed through user-friendly software which allows adding, deleting, browsing, etc. through the database. There are five major database files. One each for the five types of tested configurations. The contents of each provides significant information regarding the test method and the physical attributes of the tested configuration. 3 figs

Artificial Radionuclides Database in the Pacific Ocean: HAM Database

Directory of Open Access Journals (Sweden)

Michio Aoyama

2004-01-01

Full Text Available The database “Historical Artificial Radionuclides in the Pacific Ocean and its Marginal Seas”, or HAM database, has been created. The database includes 90Sr, 137Cs, and 239,240Pu concentration data from the seawater of the Pacific Ocean and its marginal seas with some measurements from the sea surface to the bottom. The data in the HAM database were collected from about 90 literature citations, which include published papers; annual reports by the Hydrographic Department, Maritime Safety Agency, Japan; and unpublished data provided by individuals. The data of concentrations of 90Sr, 137Cs, and 239,240Pu have been accumulating since 1957–1998. The present HAM database includes 7737 records for 137Cs concentration data, 3972 records for 90Sr concentration data, and 2666 records for 239,240Pu concentration data. The spatial variation of sampling stations in the HAM database is heterogeneous, namely, more than 80% of the data for each radionuclide is from the Pacific Ocean and the Sea of Japan, while a relatively small portion of data is from the South Pacific. This HAM database will allow us to use these radionuclides as significant chemical tracers for oceanographic study as well as the assessment of environmental affects of anthropogenic radionuclides for these 5 decades. Furthermore, these radionuclides can be used to verify the oceanic general circulation models in the time scale of several decades.

Databases and their application

NARCIS (Netherlands)

Grimm, E.C.; Bradshaw, R.H.W; Brewer, S.; Flantua, S.; Giesecke, T.; Lézine, A.M.; Takahara, H.; Williams, J.W.,Jr; Elias, S.A.; Mock, C.J.

2013-01-01

During the past 20 years, several pollen database cooperatives have been established. These databases are now constituent databases of the Neotoma Paleoecology Database, a public domain, multiproxy, relational database designed for Quaternary-Pliocene fossil data and modern surface samples. The

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.

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

Science.gov (United States)

Fang, Shih-Hau; Tsao, Yu; Hsiao, Min-Jing; Chen, Ji-Ying; Lai, Ying-Hui; Lin, Feng-Chuan; Wang, Chi-Te

2018-03-19

Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learning-based approach to detect pathological voice and examines its performance and utility compared with other automatic classification algorithms. This study retrospectively collected 60 normal voice samples and 402 pathological voice samples of 8 common clinical voice disorders in a voice clinic of a tertiary teaching hospital. We extracted Mel frequency cepstral coefficients from 3-second samples of a sustained vowel. The performances of three machine learning algorithms, namely, deep neural network (DNN), support vector machine, and Gaussian mixture model, were evaluated based on a fivefold cross-validation. Collective cases from the voice disorder database of MEEI (Massachusetts Eye and Ear Infirmary) were used to verify the performance of the classification mechanisms. The experimental results demonstrated that DNN outperforms Gaussian mixture model and support vector machine. Its accuracy in detecting voice pathologies reached 94.26% and 90.52% in male and female subjects, based on three representative Mel frequency cepstral coefficient features. When applied to the MEEI database for validation, the DNN also achieved a higher accuracy (99.32%) than the other two classification algorithms. By stacking several layers of neurons with optimized weights, the proposed DNN algorithm can fully utilize the acoustic features and efficiently differentiate between normal and pathological voice samples. Based on this pilot study, future research may proceed to explore more application of DNN from laboratory and clinical perspectives. Copyright © 2018 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Mimvec: a deep learning approach for analyzing the human phenome.

Science.gov (United States)

Gan, Mingxin; Li, Wenran; Zeng, Wanwen; Wang, Xiaojian; Jiang, Rui

2017-09-21

The human phenome has been widely used with a variety of genomic data sources in the inference of disease genes. However, most existing methods thus far derive phenotype similarity based on the analysis of biomedical databases by using the traditional term frequency-inverse document frequency (TF-IDF) formulation. This framework, though intuitive, not only ignores semantic relationships between words but also tends to produce high-dimensional vectors, and hence lacks the ability to precisely capture intrinsic semantic characteristics of biomedical documents. To overcome these limitations, we propose a framework called mimvec to analyze the human phenome by making use of the state-of-the-art deep learning technique in natural language processing. We converted 24,061 records in the Online Mendelian Inheritance in Man (OMIM) database to low-dimensional vectors using our method. We demonstrated that the vector presentation not only effectively enabled classification of phenotype records against gene ones, but also succeeded in discriminating diseases of different inheritance styles and different mechanisms. We further derived pairwise phenotype similarities between 7988 human inherited diseases using their vector presentations. With a joint analysis of this phenome with multiple genomic data, we showed that phenotype overlap indeed implied genotype overlap. We finally used the derived phenotype similarities with genomic data to prioritize candidate genes and demonstrated advantages of this method over existing ones. Our method is capable of not only capturing semantic relationships between words in biomedical records but also alleviating the dimensional disaster accompanying the traditional TF-IDF framework. With the approaching of precision medicine, there will be abundant electronic records of medicine and health awaiting for deep analysis, and we expect to see a wide spectrum of applications borrowing the idea of our method in the near future.

Database Optimizing Services

Directory of Open Access Journals (Sweden)

Adrian GHENCEA

2010-12-01

Full Text Available Almost every organization has at its centre a database. The database provides support for conducting different activities, whether it is production, sales and marketing or internal operations. Every day, a database is accessed for help in strategic decisions. The satisfaction therefore of such needs is entailed with a high quality security and availability. Those needs can be realised using a DBMS (Database Management System which is, in fact, software for a database. Technically speaking, it is software which uses a standard method of cataloguing, recovery, and running different data queries. DBMS manages the input data, organizes it, and provides ways of modifying or extracting the data by its users or other programs. Managing the database is an operation that requires periodical updates, optimizing and monitoring.

Update History of This Database - RED | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us RED Update History of This Database Date Update contents 2015/12/21 Rice Expression Database English archi...s Database Database Description Download License Update History of This Database Site Policy | Contact Us Update History of This Database - RED | LSDB Archive ... ...ve site is opened. 2000/10/1 Rice Expression Database ( http://red.dna.affrc.go.jp/RED/ ) is opened. About Thi

GRIP Database original data - GRIPDB | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data List Contact us GRI...PDB GRIP Database original data Data detail Data name GRIP Database original data DOI 10....18908/lsdba.nbdc01665-006 Description of data contents GRIP Database original data It consists of data table...s and sequences. Data file File name: gripdb_original_data.zip File URL: ftp://ftp.biosciencedbc.jp/archive/gripdb/LATEST/gri...e Database Description Download License Update History of This Database Site Policy | Contact Us GRIP Database original data - GRIPDB | LSDB Archive ...

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....

Update History of This Database - RPD | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us RPD Update History of This Database Date Update contents 2016/02/02 Rice Proteome Database English archi...s Database Database Description Download License Update History of This Database Site Policy | Contact Us Update History of This Database - RPD | LSDB Archive ... ...ve site is opened. 2003/01/07 Rice Proteome Database ( http://gene64.dna.affrc.go.jp/RPD/ ) is opened. About Thi

Brasilia’s Database Administrators

Directory of Open Access Journals (Sweden)

Jane Adriana

2016-06-01

Full Text Available Database administration has gained an essential role in the management of new database technologies. Different data models are being created for supporting the enormous data volume, from the traditional relational database. These new models are called NoSQL (Not only SQL databases. The adoption of best practices and procedures, has become essential for the operation of database management systems. Thus, this paper investigates some of the techniques and tools used by database administrators. The study highlights features and particularities in databases within the area of Brasilia, the Capital of Brazil. The results point to which new technologies regarding database management are currently the most relevant, as well as the central issues in this area.

Large-Scale Image Analytics Using Deep Learning

Science.gov (United States)

Ganguly, S.; Nemani, R. R.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Votava, P.

2014-12-01

High resolution land cover classification maps are needed to increase the accuracy of current Land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) land cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agricultural Imaging Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~100 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. In order to perform image analytics in such a granular system, it is mandatory to devise an intelligent archiving and query system for image retrieval, file structuring, metadata processing and filtering of all available image scenes. Using the Open NASA Earth Exchange (NEX) initiative, which is a partnership with Amazon Web Services (AWS), we have developed an end-to-end architecture for designing the database and the deep belief network (following the distbelief computing model) to solve a grand challenge of scaling this process across quarter million NAIP tiles that cover the entire Continental United States. The

Deep venous thrombus characterization: ultrasonography, elastography and scattering operator

Directory of Open Access Journals (Sweden)

Thibaud Berthomier

2017-04-01

Full Text Available A thrombus or a blood clot is the result of blood coagulation which is a natural process to prevent bleeding. An inappropriate formation of a thrombus in a deep vein is known as Deep Venous Thrombosis (DVT. The main complication of a DVT is a Pulmonary Embolism (PE which occurs when a thrombus breaks loose and travels to the lungs. DVT, PE, or both are also known as Venous thromboembolism (VTE. It affects an estimated 300,000–600,000 Individuals just in the United States per year and can cause considerable morbidity and mortality. This multifactorial disease related to advanced age, immobility, surgery or obesity is an important public health issue. Our project is looking to link the VTE epidemiology (risk factors, patient history, PE to the thrombus structure. To reach our goals, we are collecting ultrasonography (echogenicity and elastography (stiffness of human thrombus. This manuscript describes our approach to create and preprocess a database using Toshiba Aplio 500. Our approaches to characterize the thrombus structure with ultrasound images are also described. The feature extraction is made with the scattering operator. Obtained features are then reduced using Principal Component Analysis and are analyzed to evaluate our approach.

Applications of the observational method in deep foundations

Directory of Open Access Journals (Sweden)

Fathi M. Abdrabbo

2012-12-01

Full Text Available The observational method was introduced into geotechnical engineering to improve both theories and construction techniques. This method was developed to avoid highly conservative assumptions about soil properties in geotechnical design when faced with unavoidable uncertainties of natural ground conditions. The assumptions involved in soil mechanics theories usually differ to a certain extent from reality. These assumptions can be improved by employing an observational database. Thus, theories of geotechnical engineering can be developed by observations during the construction stage. Moreover, precise management of construction work by close observations is essential to avoid risk and to alter the design if needed to match the real conditions. This paper sheds some light on the importance of the observational concept in deep foundations through three case studies. The first one demonstrates the effect of working hypotheses on design outputs of an open caisson of 22 m internal diameter. The other two case studies present the difference between theory and reality during construction stage of auger cast-in-place piles (ACIP in difficult subsoil conditions. Importance of merging the documented theories with the available observations is discussed. The study shows that working hypotheses and engineering models affect the cost and the time required for construction of deep foundations. Field observations are essential during installation of ACIP at a site. Some precautions should be considered when drilling ACIP through sandstone of inclined top surface. These precautions are mainly dependent upon field observations during construction. ACIP can be used effectively in soil formations that have galleries and caves using a cement–bentonite mixture to fill the holes of the unsuccessful piles. Finally, the paper shares a series of practical guidelines with engineering community all over the world that may assist in design and construction of deep

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.

Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network

Directory of Open Access Journals (Sweden)

Buzhong Zhang

2018-05-01

Full Text Available Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson’s correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset.

Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.

Science.gov (United States)

Zhang, Buzhong; Li, Linqing; Lü, Qiang

2018-05-25

Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson's correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset.

The global distribution of deep-water Antipatharia habitat

Science.gov (United States)

Yesson, Chris; Bedford, Faye; Rogers, Alex D.; Taylor, Michelle L.

2017-11-01

Antipatharia are a diverse group of corals with many species found in deep water. Many Antipatharia are habitat for associates, have extreme longevity and some species can occur beyond 8500 m depth. As they are major constituents of'coral gardens', which are Vulnerable Marine Ecosystems (VMEs), knowledge of their distribution and environmental requirements is an important pre-requisite for informed conservation planning particularly where the expense and difficulty of deep-sea sampling prohibits comprehensive surveys. This study uses a global database of Antipatharia distribution data to perform habitat suitability modelling using the Maxent methodology to estimate the global extent of black coral habitat suitability. The model of habitat suitability is driven by temperature but there is notable influence from other variables of topography, surface productivity and oxygen levels. This model can be used to predict areas of suitable habitat, which can be useful for conservation planning. The global distribution of Antipatharia habitat suitability shows a marked contrast with the distribution of specimen observations, indicating that many potentially suitable areas have not been sampled, and that sampling effort has been disproportionate to shallow, accessible areas inside marine protected areas (MPAs). Although 25% of Antipatharia observations are located in MPAs, only 7-8% of predicted suitable habitat is protected, which is short of the Convention on Biological Diversity target to protect 10% of ocean habitats by 2020.

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.

NREL: U.S. Life Cycle Inventory Database - About the LCI Database Project

Science.gov (United States)

About the LCI Database Project The U.S. Life Cycle Inventory (LCI) Database is a publicly available database that allows users to objectively review and compare analysis results that are based on similar source of critically reviewed LCI data through its LCI Database Project. NREL's High-Performance

Biofuel Database

Science.gov (United States)

Biofuel Database (Web, free access)   This database brings together structural, biological, and thermodynamic data for enzymes that are either in current use or are being considered for use in the production of biofuels.

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

NIRS database of the original research database

International Nuclear Information System (INIS)

Morita, Kyoko

1991-01-01

Recently, library staffs arranged and compiled the original research papers that have been written by researchers for 33 years since National Institute of Radiological Sciences (NIRS) established. This papers describes how the internal database of original research papers has been created. This is a small sample of hand-made database. This has been cumulating by staffs who have any knowledge about computer machine or computer programming. (author)

Teaching Case: Adapting the Access Northwind Database to Support a Database Course

Science.gov (United States)

Dyer, John N.; Rogers, Camille

2015-01-01

A common problem encountered when teaching database courses is that few large illustrative databases exist to support teaching and learning. Most database textbooks have small "toy" databases that are chapter objective specific, and thus do not support application over the complete domain of design, implementation and management concepts…

Community Database

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This excel spreadsheet is the result of merging at the port level of several of the in-house fisheries databases in combination with other demographic databases such...

Applications of GIS and database technologies to manage a Karst Feature Database

Science.gov (United States)

Gao, Y.; Tipping, R.G.; Alexander, E.C.

2006-01-01

This paper describes the management of a Karst Feature Database (KFD) in Minnesota. Two sets of applications in both GIS and Database Management System (DBMS) have been developed for the KFD of Minnesota. These applications were used to manage and to enhance the usability of the KFD. Structured Query Language (SQL) was used to manipulate transactions of the database and to facilitate the functionality of the user interfaces. The Database Administrator (DBA) authorized users with different access permissions to enhance the security of the database. Database consistency and recovery are accomplished by creating data logs and maintaining backups on a regular basis. The working database provides guidelines and management tools for future studies of karst features in Minnesota. The methodology of designing this DBMS is applicable to develop GIS-based databases to analyze and manage geomorphic and hydrologic datasets at both regional and local scales. The short-term goal of this research is to develop a regional KFD for the Upper Mississippi Valley Karst and the long-term goal is to expand this database to manage and study karst features at national and global scales.

Distance correlation methods for discovering associations in large astrophysical databases

International Nuclear Information System (INIS)

Martínez-Gómez, Elizabeth; Richards, Mercedes T.; Richards, Donald St. P.

2014-01-01

High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between different astrophysical variables; hence an ability to detect and verify associations or correlations between variables is important in astrophysical research. In this paper, we apply a recently defined statistical measure called the distance correlation coefficient, which can be used to identify new associations and correlations between astrophysical variables. The distance correlation coefficient applies to variables of any dimension, can be used to determine smaller sets of variables that provide equivalent astrophysical information, is zero only when variables are independent, and is capable of detecting nonlinear associations that are undetectable by the classical Pearson correlation coefficient. Hence, the distance correlation coefficient provides more information than the Pearson coefficient. We analyze numerous pairs of variables in the COMBO-17 database with the distance correlation method and with the maximal information coefficient. We show that the Pearson coefficient can be estimated with higher accuracy from the corresponding distance correlation coefficient than from the maximal information coefficient. For given values of the Pearson coefficient, the distance correlation method has a greater ability than the maximal information coefficient to resolve astrophysical data into highly concentrated horseshoe- or V-shapes, which enhances classification and pattern identification. These results are observed over a range of redshifts beyond the local universe and for galaxies from elliptical to spiral.

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.

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.

East-China Geochemistry Database (ECGD):A New Networking Database for North China Craton

Science.gov (United States)

Wang, X.; Ma, W.

2010-12-01

North China Craton is one of the best natural laboratories that research some Earth Dynamic questions[1]. Scientists made much progress in research on this area, and got vast geochemistry data, which are essential for answering many fundamental questions about the age, composition, structure, and evolution of the East China area. But the geochemical data have long been accessible only through the scientific literature and theses where they have been widely dispersed, making it difficult for the broad Geosciences community to find, access and efficiently use the full range of available data[2]. How to effectively store, manage, share and reuse the existing geochemical data in the North China Craton area? East-China Geochemistry Database(ECGD) is a networking geochemical scientific database system that has been designed based on WebGIS and relational database for the structured storage and retrieval of geochemical data and geological map information. It is integrated the functions of data retrieval, spatial visualization and online analysis. ECGD focus on three areas: 1.Storage and retrieval of geochemical data and geological map information. Research on the characters of geochemical data, including its composing and connecting of each other, we designed a relational database, which based on geochemical relational data model, to store a variety of geological sample information such as sampling locality, age, sample characteristics, reference, major elements, rare earth elements, trace elements and isotope system et al. And a web-based user-friendly interface is provided for constructing queries. 2.Data view. ECGD is committed to online data visualization by different ways, especially to view data in digital map with dynamic way. Because ECGD was integrated WebGIS technology, the query results can be mapped on digital map, which can be zoomed, translation and dot selection. Besides of view and output query results data by html, txt or xls formats, researchers also can

Open Geoscience Database

Science.gov (United States)

Bashev, A.

2012-04-01

Currently there is an enormous amount of various geoscience databases. Unfortunately the only users of the majority of the databases are their elaborators. There are several reasons for that: incompaitability, specificity of tasks and objects and so on. However the main obstacles for wide usage of geoscience databases are complexity for elaborators and complication for users. The complexity of architecture leads to high costs that block the public access. The complication prevents users from understanding when and how to use the database. Only databases, associated with GoogleMaps don't have these drawbacks, but they could be hardly named "geoscience" Nevertheless, open and simple geoscience database is necessary at least for educational purposes (see our abstract for ESSI20/EOS12). We developed a database and web interface to work with them and now it is accessible at maps.sch192.ru. In this database a result is a value of a parameter (no matter which) in a station with a certain position, associated with metadata: the date when the result was obtained; the type of a station (lake, soil etc); the contributor that sent the result. Each contributor has its own profile, that allows to estimate the reliability of the data. The results can be represented on GoogleMaps space image as a point in a certain position, coloured according to the value of the parameter. There are default colour scales and each registered user can create the own scale. The results can be also extracted in *.csv file. For both types of representation one could select the data by date, object type, parameter type, area and contributor. The data are uploaded in *.csv format: Name of the station; Lattitude(dd.dddddd); Longitude(ddd.dddddd); Station type; Parameter type; Parameter value; Date(yyyy-mm-dd). The contributor is recognised while entering. This is the minimal set of features that is required to connect a value of a parameter with a position and see the results. All the complicated data

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...

Inleiding database-systemen

NARCIS (Netherlands)

Pels, H.J.; Lans, van der R.F.; Pels, H.J.; Meersman, R.A.

1993-01-01

Dit artikel introduceert de voornaamste begrippen die een rol spelen rond databases en het geeft een overzicht van de doelstellingen, de functies en de componenten van database-systemen. Hoewel de functie van een database intuitief vrij duidelijk is, is het toch een in technologisch opzicht complex

Integration of Biodiversity Databases in Taiwan and Linkage to Global Databases

Directory of Open Access Journals (Sweden)

Kwang-Tsao Shao

2007-03-01

Full Text Available The biodiversity databases in Taiwan were dispersed to various institutions and colleges with limited amount of data by 2001. The Natural Resources and Ecology GIS Database sponsored by the Council of Agriculture, which is part of the National Geographic Information System planned by the Ministry of Interior, was the most well established biodiversity database in Taiwan. But thisThis database was, however, mainly collectingcollected the distribution data of terrestrial animals and plants within the Taiwan area. In 2001, GBIF was formed, and Taiwan joined as one of the an Associate Participant and started, starting the establishment and integration of animal and plant species databases; therefore, TaiBIF was able to co-operate with GBIF. The information of Catalog of Life, specimens, and alien species were integrated by the Darwin core. The standard. These metadata standards allowed the biodiversity information of Taiwan to connect with global databases.

Database Replication

CERN Document Server

Kemme, Bettina

2010-01-01

Database replication is widely used for fault-tolerance, scalability and performance. The failure of one database replica does not stop the system from working as available replicas can take over the tasks of the failed replica. Scalability can be achieved by distributing the load across all replicas, and adding new replicas should the load increase. Finally, database replication can provide fast local access, even if clients are geographically distributed clients, if data copies are located close to clients. Despite its advantages, replication is not a straightforward technique to apply, and

The Public Health Impact of Pediatric Deep Neck Space Infections.

Science.gov (United States)

Adil, Eelam; Tarshish, Yael; Roberson, David; Jang, Jisun; Licameli, Greg; Kenna, Margaret

2015-12-01

There is little consensus about the best management of pediatric deep neck space infections (DNSIs) and limited information about the national disease burden. The purpose of this study is to examine the health care burden, management, and complications of DNSIs from a national perspective. Retrospective administrative data set review. National pediatric admission database. Pediatric patients diagnosed with a parapharyngeal space and/or retropharyngeal abscess were identified from the 2009 KIDS' Inpatient Database. Patient demographic, hospital, and clinical characteristics were compared between patients who received surgical and nonsurgical management. All results for the analyses were weighted, clustered, and stratified appropriately according to the sampling design of the KIDS' Inpatient Database. The prevalence of DNSIs was 3444 in 2009, and the estimated incidence was 4.6 per 100,000 children. The total hospital charges were >$75 million. The patients who were drained surgically had a 22% longer length of stay (mean = 4.19 days) than that of those who were managed without surgery (mean = 3.44 days). Mean hospital charges for patients who were drained surgically were almost twice those of patients who were managed medically ($28,969 vs $17,022); 165 patients (4.8%) had a complication. There are >3400 admissions for pediatric DNSIs annually, and they account for a significant number of inpatient days and hospital charges. A randomized controlled trial of management may be indicated from a public health perspective. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2015.

Update History of This Database - PLACE | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us PLACE Update History of This Database Date Update contents 2016/08/22 The contact address is...s Database Database Description Download License Update History of Thi...s Database Site Policy | Contact Us Update History of This Database - PLACE | LSDB Archive ... ... changed. 2014/10/20 The URLs of the database maintenance site and the portal site are changed. 2014/07/17 PLACE English archi

Database Publication Practices

DEFF Research Database (Denmark)

Bernstein, P.A.; DeWitt, D.; Heuer, A.

2005-01-01

There has been a growing interest in improving the publication processes for database research papers. This panel reports on recent changes in those processes and presents an initial cut at historical data for the VLDB Journal and ACM Transactions on Database Systems.......There has been a growing interest in improving the publication processes for database research papers. This panel reports on recent changes in those processes and presents an initial cut at historical data for the VLDB Journal and ACM Transactions on Database Systems....

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

KAUST Repository

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

2018-01-01

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

Deep learning for steganalysis via convolutional neural networks

Science.gov (United States)

Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu

2015-03-01

Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.

Stimulation Technologies for Deep Well Completions

Energy Technology Data Exchange (ETDEWEB)

None

2003-09-30

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

The MAR databases: development and implementation of databases specific for marine metagenomics.

Science.gov (United States)

Klemetsen, Terje; Raknes, Inge A; Fu, Juan; Agafonov, Alexander; Balasundaram, Sudhagar V; Tartari, Giacomo; Robertsen, Espen; Willassen, Nils P

2018-01-04

We introduce the marine databases; MarRef, MarDB and MarCat (https://mmp.sfb.uit.no/databases/), which are publicly available resources that promote marine research and innovation. These data resources, which have been implemented in the Marine Metagenomics Portal (MMP) (https://mmp.sfb.uit.no/), are collections of richly annotated and manually curated contextual (metadata) and sequence databases representing three tiers of accuracy. While MarRef is a database for completely sequenced marine prokaryotic genomes, which represent a marine prokaryote reference genome database, MarDB includes all incomplete sequenced prokaryotic genomes regardless level of completeness. The last database, MarCat, represents a gene (protein) catalog of uncultivable (and cultivable) marine genes and proteins derived from marine metagenomics samples. The first versions of MarRef and MarDB contain 612 and 3726 records, respectively. Each record is built up of 106 metadata fields including attributes for sampling, sequencing, assembly and annotation in addition to the organism and taxonomic information. Currently, MarCat contains 1227 records with 55 metadata fields. Ontologies and controlled vocabularies are used in the contextual databases to enhance consistency. The user-friendly web interface lets the visitors browse, filter and search in the contextual databases and perform BLAST searches against the corresponding sequence databases. All contextual and sequence databases are freely accessible and downloadable from https://s1.sfb.uit.no/public/mar/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Update History of This Database - DMPD | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us DMPD Update History of This Database Date Update contents 2010/03/29 DMPD English archive si....jp/macrophage/ ) is released. About This Database Database Description Download License Update History of Thi...s Database Site Policy | Contact Us Update History of This Database - DMPD | LSDB Archive ...

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/ .

Fundamentals of the NEA Thermochemical Database and its influence over national nuclear programs on the performance assessment of deep geological repositories.

Science.gov (United States)

Ragoussi, Maria-Eleni; Costa, Davide

2017-03-14

For the last 30 years, the NEA Thermochemical Database (TDB) Project (www.oecd-nea.org/dbtdb/) has been developing a chemical thermodynamic database for elements relevant to the safety of radioactive waste repositories, providing data that are vital to support the geochemical modeling of such systems. The recommended data are selected on the basis of strict review procedures and are characterized by their consistency. The results of these efforts are freely available, and have become an international point of reference in the field. As a result, a number of important national initiatives with regard to waste management programs have used the NEA TDB as their basis, both in terms of recommended data and guidelines. In this article we describe the fundamentals and achievements of the project together with the characteristics of some databases developed in national nuclear waste disposal programs that have been influenced by the NEA TDB. We also give some insights on how this work could be seen as an approach to be used in broader areas of environmental interest. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Development of a personalized training system using the Lung Image Database Consortium and Image Database resource Initiative Database.

Science.gov (United States)

Lin, Hongli; Wang, Weisheng; Luo, Jiawei; Yang, Xuedong

2014-12-01

The aim of this study was to develop a personalized training system using the Lung Image Database Consortium (LIDC) and Image Database resource Initiative (IDRI) Database, because collecting, annotating, and marking a large number of appropriate computed tomography (CT) scans, and providing the capability of dynamically selecting suitable training cases based on the performance levels of trainees and the characteristics of cases are critical for developing a efficient training system. A novel approach is proposed to develop a personalized radiology training system for the interpretation of lung nodules in CT scans using the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which provides a Content-Boosted Collaborative Filtering (CBCF) algorithm for predicting the difficulty level of each case of each trainee when selecting suitable cases to meet individual needs, and a diagnostic simulation tool to enable trainees to analyze and diagnose lung nodules with the help of an image processing tool and a nodule retrieval tool. Preliminary evaluation of the system shows that developing a personalized training system for interpretation of lung nodules is needed and useful to enhance the professional skills of trainees. The approach of developing personalized training systems using the LIDC/IDRL database is a feasible solution to the challenges of constructing specific training program in terms of cost and training efficiency. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

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

Science.gov (United States)

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

2016-09-01

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

Update History of This Database - KOME | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us KOME Update History of This Database Date Update contents 2014/10/22 The URL of the whole da...site is opened. 2003/07/18 KOME ( http://cdna01.dna.affrc.go.jp/cDNA/ ) is opened. About This Database Dat...abase Description Download License Update History of This Database Site Policy | Contact Us Update History of This Database - KOME | LSDB Archive ...

Update History of This Database - PSCDB | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us PSCDB Update History of This Database Date Update contents 2016/11/30 PSCDB English archive ...site is opened. 2011/11/13 PSCDB ( http://idp1.force.cs.is.nagoya-u.ac.jp/pscdb/ ) is opened. About This Database Database... Description Download License Update History of This Database Site Policy | Contact Us Update History of This Database - PSCDB | LSDB Archive ...

Mycobacteriophage genome database.

Science.gov (United States)

Joseph, Jerrine; Rajendran, Vasanthi; Hassan, Sameer; Kumar, Vanaja

2011-01-01

Mycobacteriophage genome database (MGDB) is an exclusive repository of the 64 completely sequenced mycobacteriophages with annotated information. It is a comprehensive compilation of the various gene parameters captured from several databases pooled together to empower mycobacteriophage researchers. The MGDB (Version No.1.0) comprises of 6086 genes from 64 mycobacteriophages classified into 72 families based on ACLAME database. Manual curation was aided by information available from public databases which was enriched further by analysis. Its web interface allows browsing as well as querying the classification. The main objective is to collect and organize the complexity inherent to mycobacteriophage protein classification in a rational way. The other objective is to browse the existing and new genomes and describe their functional annotation. The database is available for free at http://mpgdb.ibioinformatics.org/mpgdb.php.

Logical database design principles

CERN Document Server

Garmany, John; Clark, Terry

2005-01-01

INTRODUCTION TO LOGICAL DATABASE DESIGNUnderstanding a Database Database Architectures Relational Databases Creating the Database System Development Life Cycle (SDLC)Systems Planning: Assessment and Feasibility System Analysis: RequirementsSystem Analysis: Requirements Checklist Models Tracking and Schedules Design Modeling Functional Decomposition DiagramData Flow Diagrams Data Dictionary Logical Structures and Decision Trees System Design: LogicalSYSTEM DESIGN AND IMPLEMENTATION The ER ApproachEntities and Entity Types Attribute Domains AttributesSet-Valued AttributesWeak Entities Constraint

Specialist Bibliographic Databases.

Science.gov (United States)

Gasparyan, Armen Yuri; Yessirkepov, Marlen; Voronov, Alexander A; Trukhachev, Vladimir I; Kostyukova, Elena I; Gerasimov, Alexey N; Kitas, George D

2016-05-01

Specialist bibliographic databases offer essential online tools for researchers and authors who work on specific subjects and perform comprehensive and systematic syntheses of evidence. This article presents examples of the established specialist databases, which may be of interest to those engaged in multidisciplinary science communication. Access to most specialist databases is through subscription schemes and membership in professional associations. Several aggregators of information and database vendors, such as EBSCOhost and ProQuest, facilitate advanced searches supported by specialist keyword thesauri. Searches of items through specialist databases are complementary to those through multidisciplinary research platforms, such as PubMed, Web of Science, and Google Scholar. Familiarizing with the functional characteristics of biomedical and nonbiomedical bibliographic search tools is mandatory for researchers, authors, editors, and publishers. The database users are offered updates of the indexed journal lists, abstracts, author profiles, and links to other metadata. Editors and publishers may find particularly useful source selection criteria and apply for coverage of their peer-reviewed journals and grey literature sources. These criteria are aimed at accepting relevant sources with established editorial policies and quality controls.

Specialist Bibliographic Databases

Science.gov (United States)

2016-01-01

Specialist bibliographic databases offer essential online tools for researchers and authors who work on specific subjects and perform comprehensive and systematic syntheses of evidence. This article presents examples of the established specialist databases, which may be of interest to those engaged in multidisciplinary science communication. Access to most specialist databases is through subscription schemes and membership in professional associations. Several aggregators of information and database vendors, such as EBSCOhost and ProQuest, facilitate advanced searches supported by specialist keyword thesauri. Searches of items through specialist databases are complementary to those through multidisciplinary research platforms, such as PubMed, Web of Science, and Google Scholar. Familiarizing with the functional characteristics of biomedical and nonbiomedical bibliographic search tools is mandatory for researchers, authors, editors, and publishers. The database users are offered updates of the indexed journal lists, abstracts, author profiles, and links to other metadata. Editors and publishers may find particularly useful source selection criteria and apply for coverage of their peer-reviewed journals and grey literature sources. These criteria are aimed at accepting relevant sources with established editorial policies and quality controls. PMID:27134485

Directory of IAEA databases

International Nuclear Information System (INIS)

1992-12-01

This second edition of the Directory of IAEA Databases has been prepared within the Division of Scientific and Technical Information (NESI). Its main objective is to describe the computerized information sources available to staff members. This directory contains all databases produced at the IAEA, including databases stored on the mainframe, LAN's and PC's. All IAEA Division Directors have been requested to register the existence of their databases with NESI. For the second edition database owners were requested to review the existing entries for their databases and answer four additional questions. The four additional questions concerned the type of database (e.g. Bibliographic, Text, Statistical etc.), the category of database (e.g. Administrative, Nuclear Data etc.), the available documentation and the type of media used for distribution. In the individual entries on the following pages the answers to the first two questions (type and category) is always listed, but the answers to the second two questions (documentation and media) is only listed when information has been made available

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

Update History of This Database - SAHG | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us SAHG Update History of This Database Date Update contents 2016/05/09 SAHG English archive si...te is opened. 2009/10 SAHG ( http://bird.cbrc.jp/sahg ) is opened. About This Database Database Description ...Download License Update History of This Database Site Policy | Contact Us Update History of This Database - SAHG | LSDB Archive ...

Update History of This Database - RMOS | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us RMOS Update History of This Database Date Update contents 2015/10/27 RMOS English archive si...12 RMOS (http://cdna01.dna.affrc.go.jp/RMOS/) is opened. About This Database Database Description Download License Update Hi...story of This Database Site Policy | Contact Us Update History of This Database - RMOS | LSDB Archive ...

The World Bacterial Biogeography and Biodiversity through Databases: A Case Study of NCBI Nucleotide Database and GBIF Database

Directory of Open Access Journals (Sweden)

Okba Selama

2013-01-01

Full Text Available Databases are an essential tool and resource within the field of bioinformatics. The primary aim of this study was to generate an overview of global bacterial biodiversity and biogeography using available data from the two largest public online databases, NCBI Nucleotide and GBIF. The secondary aim was to highlight the contribution each geographic area has to each database. The basis for data analysis of this study was the metadata provided by both databases, mainly, the taxonomy and the geographical area origin of isolation of the microorganism (record. These were directly obtained from GBIF through the online interface, while E-utilities and Python were used in combination with a programmatic web service access to obtain data from the NCBI Nucleotide Database. Results indicate that the American continent, and more specifically the USA, is the top contributor, while Africa and Antarctica are less well represented. This highlights the imbalance of exploration within these areas rather than any reduction in biodiversity. This study describes a novel approach to generating global scale patterns of bacterial biodiversity and biogeography and indicates that the Proteobacteria are the most abundant and widely distributed phylum within both databases.

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.

An Interoperable Cartographic Database

OpenAIRE

Slobodanka Ključanin; Zdravko Galić

2007-01-01

The concept of producing a prototype of interoperable cartographic database is explored in this paper, including the possibilities of integration of different geospatial data into the database management system and their visualization on the Internet. The implementation includes vectorization of the concept of a single map page, creation of the cartographic database in an object-relation database, spatial analysis, definition and visualization of the database content in the form of a map on t...

Software listing: CHEMTOX database

International Nuclear Information System (INIS)

Moskowitz, P.D.

1993-01-01

Initially launched in 1983, the CHEMTOX Database was among the first microcomputer databases containing hazardous chemical information. The database is used in many industries and government agencies in more than 17 countries. Updated quarterly, the CHEMTOX Database provides detailed environmental and safety information on 7500-plus hazardous substances covered by dozens of regulatory and advisory sources. This brief listing describes the method of accessing data and provides ordering information for those wishing to obtain the CHEMTOX Database

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.

Linking white matter and deep gray matter alterations in premanifest Huntington disease

Directory of Open Access Journals (Sweden)

Andreia V. Faria

2016-01-01

Full Text Available Huntington disease (HD is a fatal progressive neurodegenerative disorder for which only symptomatic treatment is available. A better understanding of the pathology, and identification of biomarkers will facilitate the development of disease-modifying treatments. HD is potentially a good model of a neurodegenerative disease for development of biomarkers because it is an autosomal-dominant disease with complete penetrance, caused by a single gene mutation, in which the neurodegenerative process can be assessed many years before onset of signs and symptoms of manifest disease. Previous MRI studies have detected abnormalities in gray and white matter starting in premanifest stages. However, the understanding of how these abnormalities are related, both in time and space, is still incomplete. In this study, we combined deep gray matter shape diffeomorphometry and white matter DTI analysis in order to provide a better mapping of pathology in the deep gray matter and subcortical white matter in premanifest HD. We used 296 MRI scans from the PREDICT-HD database. Atrophy in the deep gray matter, thalamus, hippocampus, and nucleus accumbens was analyzed by surface based morphometry, and while white matter abnormalities were analyzed in (i regions of interest surrounding these structures, using (ii tractography-based analysis, and using (iii whole brain atlas-based analysis. We detected atrophy in the deep gray matter, particularly in putamen, from early premanifest stages. The atrophy was greater both in extent and effect size in cases with longer exposure to the effects of the CAG expansion mutation (as assessed by greater CAP-scores, and preceded detectible abnormalities in the white matter. Near the predicted onset of manifest HD, the MD increase was widespread, with highest indices in the deep and posterior white matter. This type of in-vivo macroscopic mapping of HD brain abnormalities can potentially indicate when and where therapeutics could be

Database Dictionary for Ethiopian National Ground-Water DAtabase (ENGDA) Data Fields

Science.gov (United States)

Kuniansky, Eve L.; Litke, David W.; Tucci, Patrick

2007-01-01

Introduction This document describes the data fields that are used for both field forms and the Ethiopian National Ground-water Database (ENGDA) tables associated with information stored about production wells, springs, test holes, test wells, and water level or water-quality observation wells. Several different words are used in this database dictionary and in the ENGDA database to describe a narrow shaft constructed in the ground. The most general term is borehole, which is applicable to any type of hole. A well is a borehole specifically constructed to extract water from the ground; however, for this data dictionary and for the ENGDA database, the words well and borehole are used interchangeably. A production well is defined as any well used for water supply and includes hand-dug wells, small-diameter bored wells equipped with hand pumps, or large-diameter bored wells equipped with large-capacity motorized pumps. Test holes are borings made to collect information about the subsurface with continuous core or non-continuous core and/or where geophysical logs are collected. Test holes are not converted into wells. A test well is a well constructed for hydraulic testing of an aquifer in order to plan a larger ground-water production system. A water-level or water-quality observation well is a well that is used to collect information about an aquifer and not used for water supply. A spring is any naturally flowing, local, ground-water discharge site. The database dictionary is designed to help define all fields on both field data collection forms (provided in attachment 2 of this report) and for the ENGDA software screen entry forms (described in Litke, 2007). The data entered into each screen entry field are stored in relational database tables within the computer database. The organization of the database dictionary is designed based on field data collection and the field forms, because this is what the majority of people will use. After each field, however, the

Update History of This Database - SSBD | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us SSBD Update History of This Database Date Update contents 2016/07/25 SSBD English archive si...tion Download License Update History of This Database Site Policy | Contact Us Update History of This Database - SSBD | LSDB Archive ... ...te is opened. 2013/09/03 SSBD ( http://ssbd.qbic.riken.jp/ ) is opened. About This Database Database Descrip

A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network.

Science.gov (United States)

Zhao, Jianwei; Lv, Yongbiao; Zhou, Zhenghua; Cao, Feilong

2017-10-01

There have been a lot of methods to address the recognition of complete face images. However, in real applications, the images to be recognized are usually incomplete, and it is more difficult to realize such a recognition. In this paper, a novel convolution neural network frame, named a low-rank-recovery network (LRRNet), is proposed to conquer the difficulty effectively inspired by matrix completion and deep learning techniques. The proposed LRRNet first recovers the incomplete face images via an approach of matrix completion with the truncated nuclear norm regularization solution, and then extracts some low-rank parts of the recovered images as the filters. With these filters, some important features are obtained by means of the binaryzation and histogram algorithms. Finally, these features are classified with the classical support vector machines (SVMs). The proposed LRRNet method has high face recognition rate for the heavily corrupted images, especially for the images in the large databases. The proposed LRRNet performs well and efficiently for the images with heavily corrupted, especially in the case of large databases. Extensive experiments on several benchmark databases demonstrate that the proposed LRRNet performs better than some other excellent robust face recognition methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Interoperable Cartographic Database

Directory of Open Access Journals (Sweden)

Slobodanka Ključanin

2007-05-01

Full Text Available The concept of producing a prototype of interoperable cartographic database is explored in this paper, including the possibilities of integration of different geospatial data into the database management system and their visualization on the Internet. The implementation includes vectorization of the concept of a single map page, creation of the cartographic database in an object-relation database, spatial analysis, definition and visualization of the database content in the form of a map on the Internet. 

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.

End-to-End Multimodal Emotion Recognition Using Deep Neural Networks

Science.gov (United States)

Tzirakis, Panagiotis; Trigeorgis, George; Nicolaou, Mihalis A.; Schuller, Bjorn W.; Zafeiriou, Stefanos

2017-12-01

Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using auditory and visual modalities. To capture the emotional content for various styles of speaking, robust features need to be extracted. To this purpose, we utilize a Convolutional Neural Network (CNN) to extract features from the speech, while for the visual modality a deep residual network (ResNet) of 50 layers. In addition to the importance of feature extraction, a machine learning algorithm needs also to be insensitive to outliers while being able to model the context. To tackle this problem, Long Short-Term Memory (LSTM) networks are utilized. The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.

Extending Database Integration Technology

National Research Council Canada - National Science Library

Buneman, Peter

1999-01-01

Formal approaches to the semantics of databases and database languages can have immediate and practical consequences in extending database integration technologies to include a vastly greater range...

MaGa, a web-based collaborative database for gas emissions: a tool to improve the knowledge on Earth degassing

Science.gov (United States)

Frigeri, A.; Cardellini, C.; Chiodini, G.; Frondini, F.; Bagnato, E.; Aiuppa, A.; Fischer, T. P.; Lehnert, K. A.

2014-12-01

The study of the main pathways of carbon flux from the deep Earth requires the analysis of a large quantity and variety of data on volcanic and non-volcanic gas emissions. Hence, there is need for common frameworks to aggregate available data and insert new observations. Since 2010 we have been developing the Mapping Gas emissions (MaGa) web-based database to collect data on carbon degassing form volcanic and non-volcanic environments. MaGa uses an Object-relational model, translating the experience of field surveyors into the database schema. The current web interface of MaGa allows users to browse the data in tabular format or by browsing an interactive web-map. Enabled users can insert information as measurement methods, instrument details as well as the actual values collected in the field. Measurements found in the literature can be inserted as well as direct field observations made by human-operated instruments. Currently the database includes fluxes and gas compositions from active craters degassing, diffuse soil degassing and fumaroles both from dormant volcanoes and open-vent volcanoes from literature survey and data about non-volcanic emission of the Italian territory. Currently, MaGa holds more than 1000 volcanic plume degassing fluxes, data from 30 sites of diffuse soil degassing from italian volcanoes, and about 60 measurements from fumarolic and non volcanic emission sites. For each gas emission site, the MaGa holds data, pictures, descriptions on gas sampling, analysis and measurement methods, together with bibliographic references and contacts to researchers having experience on each site. From 2012, MaGa developments started to be focused towards the framework of the Deep Earth Carbon Degassing research initiative of the Deep Carbon Observatory. Whithin the DECADE initiative, there are others data systems, as EarthChem and the Smithsonian Institution's Global Volcanism Program. An interoperable interaction between the DECADE data systems is being

Bothered by abstractness or engaged by cohesion? Experts' explanations enhance novices' deep-learning.

Science.gov (United States)

Lachner, Andreas; Nückles, Matthias

2015-03-01

Experts' explanations have been shown to better enhance novices' transfer as compared with advanced students' explanations. Based on research on expertise and text comprehension, we investigated whether the abstractness or the cohesion of experts' and intermediates' explanations accounted for novices' learning. In Study 1, we showed that the superior cohesion of experts' explanations accounted for most of novices' transfer, whereas the degree of abstractness did not impact novices' transfer performance. In Study 2, we investigated novices' processing while learning with experts' and intermediates' explanations. We found that novices studying experts' explanations actively self-regulated their processing of the explanations, as they showed mainly deep-processing activities, whereas novices learning with intermediates' explanations were mainly engaged in shallow-processing activities by paraphrasing the explanations. Thus, we concluded that subject-matter expertise is a crucial prerequisite for instructors. Despite the abstract character of experts' explanations, their subject-matter expertise enables them to generate highly cohesive explanations that serve as a valuable scaffold for students' construction of flexible knowledge by engaging them in deep-level processing. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Database specification for the Worldwide Port System (WPS) Regional Integrated Cargo Database (ICDB)

Energy Technology Data Exchange (ETDEWEB)

Faby, E.Z.; Fluker, J.; Hancock, B.R.; Grubb, J.W.; Russell, D.L. [Univ. of Tennessee, Knoxville, TN (United States); Loftis, J.P.; Shipe, P.C.; Truett, L.F. [Oak Ridge National Lab., TN (United States)

1994-03-01

This Database Specification for the Worldwide Port System (WPS) Regional Integrated Cargo Database (ICDB) describes the database organization and storage allocation, provides the detailed data model of the logical and physical designs, and provides information for the construction of parts of the database such as tables, data elements, and associated dictionaries and diagrams.

Specialist Bibliographic Databases

OpenAIRE

Gasparyan, Armen Yuri; Yessirkepov, Marlen; Voronov, Alexander A.; Trukhachev, Vladimir I.; Kostyukova, Elena I.; Gerasimov, Alexey N.; Kitas, George D.

2016-01-01

Specialist bibliographic databases offer essential online tools for researchers and authors who work on specific subjects and perform comprehensive and systematic syntheses of evidence. This article presents examples of the established specialist databases, which may be of interest to those engaged in multidisciplinary science communication. Access to most specialist databases is through subscription schemes and membership in professional associations. Several aggregators of information and d...

Towards PLDA-RBM based speaker recognition in mobile environment: Designing stacked/deep PLDA-RBM systems

DEFF Research Database (Denmark)

Nautsch, Andreas; Hao, Hong; Stafylakis, Themos

2016-01-01

recognition: two deep architectures are presented and examined, which aim at suppressing channel effects and recovering speaker-discriminative information on back-ends trained on a small dataset. Experiments are carried out on the MOBIO SRE'13 database, which is a challenging and publicly available dataset...... for mobile speaker recognition with limited amounts of training data. The experiments show that the proposed system outperforms the baseline i-vector/PLDA approach by relative gains of 31% on female and 9% on male speakers in terms of half total error rate....

Improving face image extraction by using deep learning technique

Science.gov (United States)

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

2016-03-01

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

Nuclear power economic database

International Nuclear Information System (INIS)

Ding Xiaoming; Li Lin; Zhao Shiping

1996-01-01

Nuclear power economic database (NPEDB), based on ORACLE V6.0, consists of three parts, i.e., economic data base of nuclear power station, economic data base of nuclear fuel cycle and economic database of nuclear power planning and nuclear environment. Economic database of nuclear power station includes data of general economics, technique, capital cost and benefit, etc. Economic database of nuclear fuel cycle includes data of technique and nuclear fuel price. Economic database of nuclear power planning and nuclear environment includes data of energy history, forecast, energy balance, electric power and energy facilities

Keyword Search in Databases

CERN Document Server

Yu, Jeffrey Xu; Chang, Lijun

2009-01-01

It has become highly desirable to provide users with flexible ways to query/search information over databases as simple as keyword search like Google search. This book surveys the recent developments on keyword search over databases, and focuses on finding structural information among objects in a database using a set of keywords. Such structural information to be returned can be either trees or subgraphs representing how the objects, that contain the required keywords, are interconnected in a relational database or in an XML database. The structural keyword search is completely different from

Update History of This Database - AT Atlas | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us AT Atlas Update History of This Database Date Update contents 2013/12/16 The email address i... ( http://www.tanpaku.org/atatlas/ ) is opened. About This Database Database Description Download License Update History of This Data...base Site Policy | Contact Us Update History of This Database - AT Atlas | LSDB Archive ...

600 MW nuclear power database

International Nuclear Information System (INIS)

Cao Ruiding; Chen Guorong; Chen Xianfeng; Zhang Yishu

1996-01-01

600 MW Nuclear power database, based on ORACLE 6.0, consists of three parts, i.e. nuclear power plant database, nuclear power position database and nuclear power equipment database. In the database, there are a great deal of technique data and picture of nuclear power, provided by engineering designing units and individual. The database can give help to the designers of nuclear power

A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms

Science.gov (United States)

Werner, Gijsbert D. A.; Cornwell, William K.; Sprent, Janet I.; Kattge, Jens; Kiers, E. Toby

2014-01-01

Symbiotic associations occur in every habitat on earth, but we know very little about their evolutionary histories. Current models of trait evolution cannot adequately reconstruct the deep history of symbiotic innovation, because they assume homogenous evolutionary processes across millions of years. Here we use a recently developed, heterogeneous and quantitative phylogenetic framework to study the origin of the symbiosis between angiosperms and nitrogen-fixing (N2) bacterial symbionts housed in nodules. We compile the largest database of global nodulating plant species and reconstruct the symbiosis’ evolution. We identify a single, cryptic evolutionary innovation driving symbiotic N2-fixation evolution, followed by multiple gains and losses of the symbiosis, and the subsequent emergence of ‘stable fixers’ (clades extremely unlikely to lose the symbiosis). Originating over 100 MYA, this innovation suggests deep homology in symbiotic N2-fixation. Identifying cryptic innovations on the tree of life is key to understanding the evolution of complex traits, including symbiotic partnerships. PMID:24912610

Report on fiscal 1999 survey for geothermal exploration technology verification. Survey of deep-seated geothermal resources; 1999 nendo chinetsu tansa gijutsu nado kensho chosa hokokusho. Shinbu chinetsu shigen chosa

Energy Technology Data Exchange (ETDEWEB)

NONE

2001-03-01

To promote the development of deep-seated geothermal resources in a rationalized way, studies were conducted about deep-seated geothermal resource assessment techniques, development guidelines, and the like. For the development of techniques for estimating deep-seated geothermal reservoir parameters, the Uenotai district, Akita Prefecture, and the Hatchobaru district, Oita Prefecture, were designated as model fields, and a geothermal system conceptual model was fabricated. Data of the two districts were registered in a database. Using these data, verification was performed of the validity of stochastic estimation techniques, large area flow simulation, rock/water equilibrium reaction simulation, and the like. As for the technique of deep-seated resource amount estimation, a simplified reservoir model was experimentally constructed based on parameters determined by the stochastic estimation of deep-seated reservoirs and on the conceptual model, and a method was studied for TOUGH2-based production prediction. Studies were also made about deep-seated geothermal resource development guidelines, such as exploration guidelines, exploration well boring guidelines, and geothermal fluid production guidelines. (NEDO)

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.

Hazard Analysis Database Report

Energy Technology Data Exchange (ETDEWEB)

GAULT, G.W.

1999-10-13

The Hazard Analysis Database was developed in conjunction with the hazard analysis activities conducted in accordance with DOE-STD-3009-94, Preparation Guide for US Department of Energy Nonreactor Nuclear Facility Safety Analysis Reports, for the Tank Waste Remediation System (TWRS) Final Safety Analysis Report (FSAR). The FSAR is part of the approved TWRS Authorization Basis (AB). This document describes, identifies, and defines the contents and structure of the TWRS FSAR Hazard Analysis Database and documents the configuration control changes made to the database. The TWRS Hazard Analysis Database contains the collection of information generated during the initial hazard evaluations and the subsequent hazard and accident analysis activities. The database supports the preparation of Chapters 3,4, and 5 of the TWRS FSAR and the USQ process and consists of two major, interrelated data sets: (1) Hazard Evaluation Database--Data from the results of the hazard evaluations; and (2) Hazard Topography Database--Data from the system familiarization and hazard identification.

Seismic stability of the survey areas of potential sites for the deep geological repository of the spent nuclear fuel

Science.gov (United States)

Kaláb, Zdeněk; Šílený, Jan; Lednická, Markéta

2017-07-01

This paper deals with the seismic stability of the survey areas of potential sites for the deep geological repository of the spent nuclear fuel in the Czech Republic. The basic source of data for historical earthquakes up to 1990 was the seismic website [1-]. The most intense earthquake described occurred on September 15, 1590 in the Niederroesterreich region (Austria) in the historical period; its reported intensity is Io = 8-9. The source of the contemporary seismic data for the period since 1991 to the end of 2014 was the website [11]. It may be stated based on the databases and literature review that in the period from 1900, no earthquake exceeding magnitude 5.1 originated in the territory of the Czech Republic. In order to evaluate seismicity and to assess the impact of seismic effects at depths of hypothetical deep geological repository for the next time period, the neo-deterministic method was selected as an extension of the probabilistic method. Each one out of the seven survey areas were assessed by the neo-deterministic evaluation of the seismic wave-field excited by selected individual events and determining the maximum loading. Results of seismological databases studies and neo-deterministic analysis of Čihadlo locality are presented.

Collecting Taxes Database

Data.gov (United States)

US Agency for International Development — The Collecting Taxes Database contains performance and structural indicators about national tax systems. The database contains quantitative revenue performance...

Accessing and using chemical databases

DEFF Research Database (Denmark)

Nikolov, Nikolai Georgiev; Pavlov, Todor; Niemelä, Jay Russell

2013-01-01

Computer-based representation of chemicals makes it possible to organize data in chemical databases-collections of chemical structures and associated properties. Databases are widely used wherever efficient processing of chemical information is needed, including search, storage, retrieval......, and dissemination. Structure and functionality of chemical databases are considered. The typical kinds of information found in a chemical database are considered-identification, structural, and associated data. Functionality of chemical databases is presented, with examples of search and access types. More details...... are included about the OASIS database and platform and the Danish (Q)SAR Database online. Various types of chemical database resources are discussed, together with a list of examples....

Column-oriented database management systems

OpenAIRE

Možina, David

2013-01-01

In the following thesis I will present column-oriented database. Among other things, I will answer on a question why there is a need for a column-oriented database. In recent years there have been a lot of attention regarding a column-oriented database, even if the existence of a columnar database management systems dates back in the early seventies of the last century. I will compare both systems for a database management – a colum-oriented database system and a row-oriented database system ...

Deep learning for computational chemistry

Energy Technology Data Exchange (ETDEWEB)

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

2017-03-08

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

Deep learning for computational chemistry.

Science.gov (United States)

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

2017-06-15

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

Database Description - Society Catalog | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available ion of the academic societies in Japan (organization name, website URL, contact a...sing a category tree or a society website's thumbnail. This database is useful especially when the users are... External Links: Original website information Database maintenance site National Bioscience Database Center *The original web...site was terminated. URL of the original website - Operation start date 2008/06 Last update

Update History of This Database - RMG | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us RMG Update History of This Database Date Update contents 2016/08/22 The contact address is c...dna.affrc.go.jp/ ) is opened. About This Database Database Description Download License Update Hi...story of This Database Site Policy | Contact Us Update History of This Database - RMG | LSDB Archive ... ... URL of the portal site is changed. 2013/08/07 RMG archive site is opened. 2002/09/25 RMG ( http://rmg.rice.

Update History of This Database - DGBY | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us DGBY Update History of This Database Date Update contents 2014/10/20 The URL of the portal s...aro.affrc.go.jp/yakudachi/yeast/index.html ) is opened. About This Database Database Description Download License Update Hi...story of This Database Site Policy | Contact Us Update History of This Database - DGBY | LSDB Archive ... ... Expression of attribution in License is updated. 2012/03/08 DGBY English archive site is opened. 2006/10/02

Update History of This Database - KAIKOcDNA | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us KAIKOcDNA Update History of This Database Date Update contents 2014/10/20 The URL of the dat... database ( http://sgp.dna.affrc.go.jp/EST/ ) is opened. About This Database Database Description Download License Update Hi...story of This Database Site Policy | Contact Us Update History of This Database - KAIKOcDNA | LSDB Archive ... ...abase maintenance site is changed. 2014/10/08 KAIKOcDNA English archive site is opened. 2004/04/12 KAIKOcDNA

Dietary Supplement Ingredient Database

Science.gov (United States)

... and US Department of Agriculture Dietary Supplement Ingredient Database Toggle navigation Menu Home About DSID Mission Current ... values can be saved to build a small database or add to an existing database for national, ...

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...

Database and Expert Systems Applications

DEFF Research Database (Denmark)

Viborg Andersen, Kim; Debenham, John; Wagner, Roland

schemata, query evaluation, semantic processing, information retrieval, temporal and spatial databases, querying XML, organisational aspects of databases, natural language processing, ontologies, Web data extraction, semantic Web, data stream management, data extraction, distributed database systems......This book constitutes the refereed proceedings of the 16th International Conference on Database and Expert Systems Applications, DEXA 2005, held in Copenhagen, Denmark, in August 2005.The 92 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 390...... submissions. The papers are organized in topical sections on workflow automation, database queries, data classification and recommendation systems, information retrieval in multimedia databases, Web applications, implementational aspects of databases, multimedia databases, XML processing, security, XML...

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)

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.

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.

Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture.

Science.gov (United States)

Chen, C L Philip; Liu, Zhulin

2018-01-01

Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as "mapped features" in feature nodes and the structure is expanded in wide sense in the "enhancement nodes." The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.

National Database of Geriatrics

DEFF Research Database (Denmark)

Kannegaard, Pia Nimann; Vinding, Kirsten L; Hare-Bruun, Helle

2016-01-01

AIM OF DATABASE: The aim of the National Database of Geriatrics is to monitor the quality of interdisciplinary diagnostics and treatment of patients admitted to a geriatric hospital unit. STUDY POPULATION: The database population consists of patients who were admitted to a geriatric hospital unit....... Geriatric patients cannot be defined by specific diagnoses. A geriatric patient is typically a frail multimorbid elderly patient with decreasing functional ability and social challenges. The database includes 14-15,000 admissions per year, and the database completeness has been stable at 90% during the past......, percentage of discharges with a rehabilitation plan, and the part of cases where an interdisciplinary conference has taken place. Data are recorded by doctors, nurses, and therapists in a database and linked to the Danish National Patient Register. DESCRIPTIVE DATA: Descriptive patient-related data include...

A new relational database structure and online interface for the HITRAN database

International Nuclear Information System (INIS)

Hill, Christian; Gordon, Iouli E.; Rothman, Laurence S.; Tennyson, Jonathan

2013-01-01

A new format for the HITRAN database is proposed. By storing the line-transition data in a number of linked tables described by a relational database schema, it is possible to overcome the limitations of the existing format, which have become increasingly apparent over the last few years as new and more varied data are being used by radiative-transfer models. Although the database in the new format can be searched using the well-established Structured Query Language (SQL), a web service, HITRANonline, has been deployed to allow users to make most common queries of the database using a graphical user interface in a web page. The advantages of the relational form of the database to ensuring data integrity and consistency are explored, and the compatibility of the online interface with the emerging standards of the Virtual Atomic and Molecular Data Centre (VAMDC) project is discussed. In particular, the ability to access HITRAN data using a standard query language from other websites, command line tools and from within computer programs is described. -- Highlights: • A new, interactive version of the HITRAN database is presented. • The data is stored in a structured fashion in a relational database. • The new HITRANonline interface offers increased functionality and easier error correction

Update History of This Database - fRNAdb | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us fRNAdb Update History of This Database Date Update contents 2016/03/29 fRNAdb English archiv...on Download License Update History of This Database Site Policy | Contact Us Update History of This Database - fRNAdb | LSDB Archive ... ...e site is opened. 2006/12 fRNAdb ( http://www.ncrna.org/ ) is opened. About This Database Database Descripti

Multilevel security for relational databases

CERN Document Server

Faragallah, Osama S; El-Samie, Fathi E Abd

2014-01-01

Concepts of Database Security Database Concepts Relational Database Security Concepts Access Control in Relational Databases      Discretionary Access Control      Mandatory Access Control      Role-Based Access Control Work Objectives Book Organization Basic Concept of Multilevel Database Security IntroductionMultilevel Database Relations Polyinstantiation      Invisible Polyinstantiation      Visible Polyinstantiation      Types of Polyinstantiation      Architectural Consideration

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.

A Case for Database Filesystems

Energy Technology Data Exchange (ETDEWEB)

Adams, P A; Hax, J C

2009-05-13

Data intensive science is offering new challenges and opportunities for Information Technology and traditional relational databases in particular. Database filesystems offer the potential to store Level Zero data and analyze Level 1 and Level 3 data within the same database system [2]. Scientific data is typically composed of both unstructured files and scalar data. Oracle SecureFiles is a new database filesystem feature in Oracle Database 11g that is specifically engineered to deliver high performance and scalability for storing unstructured or file data inside the Oracle database. SecureFiles presents the best of both the filesystem and the database worlds for unstructured content. Data stored inside SecureFiles can be queried or written at performance levels comparable to that of traditional filesystems while retaining the advantages of the Oracle database.

Update History of This Database - TogoTV | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us TogoTV Update History of This Database Date Update contents 2017/05/12 TogoTV English archiv...ription Download License Update History of This Database Site Policy | Contact Us Update History of This Database - TogoTV | LSDB Archive ... ...e site is opened. 2007/07/20 TogoTV ( http://togotv.dbcls.jp/ ) is opened. About This Database Database Desc

Update History of This Database - ConfC | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us ConfC Update History of This Database Date Update contents 2016/09/20 ConfC English archive ...tion Download License Update History of This Database Site Policy | Contact Us Update History of This Database - ConfC | LSDB Archive ... ...site is opened. 2005/05/01 ConfC (http://mbs.cbrc.jp/ConfC/) is opened. About This Database Database Descrip

Developments in diffraction databases

International Nuclear Information System (INIS)

Jenkins, R.

1999-01-01

Full text: There are a number of databases available to the diffraction community. Two of the more important of these are the Powder Diffraction File (PDF) maintained by the International Centre for Diffraction Data (ICDD), and the Inorganic Crystal Structure Database (ICSD) maintained by Fachsinformationzentrum (FIZ, Karlsruhe). In application, the PDF has been used as an indispensable tool in phase identification and identification of unknowns. The ICSD database has extensive and explicit reference to the structures of compounds: atomic coordinates, space group and even thermal vibration parameters. A similar database, but for organic compounds, is maintained by the Cambridge Crystallographic Data Centre. These databases are often used as independent sources of information. However, little thought has been given on how to exploit the combined properties of structural database tools. A recently completed agreement between ICDD and FIZ, plus ICDD and Cambridge, provides a first step in complementary use of the PDF and the ICSD databases. The focus of this paper (as indicated below) is to examine ways of exploiting the combined properties of both databases. In 1996, there were approximately 76,000 entries in the PDF and approximately 43,000 entries in the ICSD database. The ICSD database has now been used to calculate entries in the PDF. Thus, to derive d-spacing and peak intensity data requires the synthesis of full diffraction patterns, i.e., we use the structural data in the ICSD database and then add instrumental resolution information. The combined data from PDF and ICSD can be effectively used in many ways. For example, we can calculate PDF data for an ideally random crystal distribution and also in the absence of preferred orientation. Again, we can use systematic studies of intermediate members in solid solutions series to help produce reliable quantitative phase analyses. In some cases, we can study how solid solution properties vary with composition and

Aviation Safety Issues Database

Science.gov (United States)

Morello, Samuel A.; Ricks, Wendell R.

2009-01-01

The aviation safety issues database was instrumental in the refinement and substantiation of the National Aviation Safety Strategic Plan (NASSP). The issues database is a comprehensive set of issues from an extremely broad base of aviation functions, personnel, and vehicle categories, both nationally and internationally. Several aviation safety stakeholders such as the Commercial Aviation Safety Team (CAST) have already used the database. This broader interest was the genesis to making the database publically accessible and writing this report.

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...

BDVC (Bimodal Database of Violent Content): A database of violent audio and video

Science.gov (United States)

Rivera Martínez, Jose Luis; Mijes Cruz, Mario Humberto; Rodríguez Vázqu, Manuel Antonio; Rodríguez Espejo, Luis; Montoya Obeso, Abraham; García Vázquez, Mireya Saraí; Ramírez Acosta, Alejandro Álvaro

2017-09-01

Nowadays there is a trend towards the use of unimodal databases for multimedia content description, organization and retrieval applications of a single type of content like text, voice and images, instead bimodal databases allow to associate semantically two different types of content like audio-video, image-text, among others. The generation of a bimodal database of audio-video implies the creation of a connection between the multimedia content through the semantic relation that associates the actions of both types of information. This paper describes in detail the used characteristics and methodology for the creation of the bimodal database of violent content; the semantic relationship is stablished by the proposed concepts that describe the audiovisual information. The use of bimodal databases in applications related to the audiovisual content processing allows an increase in the semantic performance only and only if these applications process both type of content. This bimodal database counts with 580 audiovisual annotated segments, with a duration of 28 minutes, divided in 41 classes. Bimodal databases are a tool in the generation of applications for the semantic web.

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.

Experiment Databases

Science.gov (United States)

Vanschoren, Joaquin; Blockeel, Hendrik

Next to running machine learning algorithms based on inductive queries, much can be learned by immediately querying the combined results of many prior studies. Indeed, all around the globe, thousands of machine learning experiments are being executed on a daily basis, generating a constant stream of empirical information on machine learning techniques. While the information contained in these experiments might have many uses beyond their original intent, results are typically described very concisely in papers and discarded afterwards. If we properly store and organize these results in central databases, they can be immediately reused for further analysis, thus boosting future research. In this chapter, we propose the use of experiment databases: databases designed to collect all the necessary details of these experiments, and to intelligently organize them in online repositories to enable fast and thorough analysis of a myriad of collected results. They constitute an additional, queriable source of empirical meta-data based on principled descriptions of algorithm executions, without reimplementing the algorithms in an inductive database. As such, they engender a very dynamic, collaborative approach to experimentation, in which experiments can be freely shared, linked together, and immediately reused by researchers all over the world. They can be set up for personal use, to share results within a lab or to create open, community-wide repositories. Here, we provide a high-level overview of their design, and use an existing experiment database to answer various interesting research questions about machine learning algorithms and to verify a number of recent studies.

An XCT image database system

International Nuclear Information System (INIS)

Komori, Masaru; Minato, Kotaro; Koide, Harutoshi; Hirakawa, Akina; Nakano, Yoshihisa; Itoh, Harumi; Torizuka, Kanji; Yamasaki, Tetsuo; Kuwahara, Michiyoshi.

1984-01-01

In this paper, an expansion of X-ray CT (XCT) examination history database to XCT image database is discussed. The XCT examination history database has been constructed and used for daily examination and investigation in our hospital. This database consists of alpha-numeric information (locations, diagnosis and so on) of more than 15,000 cases, and for some of them, we add tree structured image data which has a flexibility for various types of image data. This database system is written by MUMPS database manipulation language. (author)

The Danish fetal medicine database

DEFF Research Database (Denmark)

Ekelund, Charlotte Kvist; Kopp, Tine Iskov; Tabor, Ann

2016-01-01

trimester ultrasound scan performed at all public hospitals in Denmark are registered in the database. Main variables/descriptive data: Data on maternal characteristics, ultrasonic, and biochemical variables are continuously sent from the fetal medicine units’Astraia databases to the central database via...... analyses are sent to the database. Conclusion: It has been possible to establish a fetal medicine database, which monitors first-trimester screening for chromosomal abnormalities and second-trimester screening for major fetal malformations with the input from already collected data. The database...

National database

DEFF Research Database (Denmark)

Kristensen, Helen Grundtvig; Stjernø, Henrik

1995-01-01

Artikel om national database for sygeplejeforskning oprettet på Dansk Institut for Sundheds- og Sygeplejeforskning. Det er målet med databasen at samle viden om forsknings- og udviklingsaktiviteter inden for sygeplejen.......Artikel om national database for sygeplejeforskning oprettet på Dansk Institut for Sundheds- og Sygeplejeforskning. Det er målet med databasen at samle viden om forsknings- og udviklingsaktiviteter inden for sygeplejen....

The CAPEC Database

DEFF Research Database (Denmark)

Nielsen, Thomas Lund; Abildskov, Jens; Harper, Peter Mathias

2001-01-01

in the compound. This classification makes the CAPEC database a very useful tool, for example, in the development of new property models, since properties of chemically similar compounds are easily obtained. A program with efficient search and retrieval functions of properties has been developed.......The Computer-Aided Process Engineering Center (CAPEC) database of measured data was established with the aim to promote greater data exchange in the chemical engineering community. The target properties are pure component properties, mixture properties, and special drug solubility data....... The database divides pure component properties into primary, secondary, and functional properties. Mixture properties are categorized in terms of the number of components in the mixture and the number of phases present. The compounds in the database have been classified on the basis of the functional groups...

Update History of This Database - AcEST | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us AcEST Update History of This Database Date Update contents 2013/01/10 Errors found on AcEST ...s Database Database Description Download License Update History of This Data...base Site Policy | Contact Us Update History of This Database - AcEST | LSDB Archive ... ...Conting data have been correceted. For details, please refer to the following page. Data correction 2010/03/29 AcEST English archi

The Danish Testicular Cancer database.

Science.gov (United States)

Daugaard, Gedske; Kier, Maria Gry Gundgaard; Bandak, Mikkel; Mortensen, Mette Saksø; Larsson, Heidi; Søgaard, Mette; Toft, Birgitte Groenkaer; Engvad, Birte; Agerbæk, Mads; Holm, Niels Vilstrup; Lauritsen, Jakob

2016-01-01

The nationwide Danish Testicular Cancer database consists of a retrospective research database (DaTeCa database) and a prospective clinical database (Danish Multidisciplinary Cancer Group [DMCG] DaTeCa database). The aim is to improve the quality of care for patients with testicular cancer (TC) in Denmark, that is, by identifying risk factors for relapse, toxicity related to treatment, and focusing on late effects. All Danish male patients with a histologically verified germ cell cancer diagnosis in the Danish Pathology Registry are included in the DaTeCa databases. Data collection has been performed from 1984 to 2007 and from 2013 onward, respectively. The retrospective DaTeCa database contains detailed information with more than 300 variables related to histology, stage, treatment, relapses, pathology, tumor markers, kidney function, lung function, etc. A questionnaire related to late effects has been conducted, which includes questions regarding social relationships, life situation, general health status, family background, diseases, symptoms, use of medication, marital status, psychosocial issues, fertility, and sexuality. TC survivors alive on October 2014 were invited to fill in this questionnaire including 160 validated questions. Collection of questionnaires is still ongoing. A biobank including blood/sputum samples for future genetic analyses has been established. Both samples related to DaTeCa and DMCG DaTeCa database are included. The prospective DMCG DaTeCa database includes variables regarding histology, stage, prognostic group, and treatment. The DMCG DaTeCa database has existed since 2013 and is a young clinical database. It is necessary to extend the data collection in the prospective database in order to answer quality-related questions. Data from the retrospective database will be added to the prospective data. This will result in a large and very comprehensive database for future studies on TC patients.

Update History of This Database - D-HaploDB | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us D-HaploDB Update History of This Database Date Update contents 2016/12/13 Description of the.../orca.gen.kyushu-u.ac.jp/) is released. About This Database Database Description Download License Update History of This Database... Site Policy | Contact Us Update History of This Database - D-HaploDB | LSDB Archive ...

Scopus database: a review.

Science.gov (United States)

Burnham, Judy F

2006-03-08

The Scopus database provides access to STM journal articles and the references included in those articles, allowing the searcher to search both forward and backward in time. The database can be used for collection development as well as for research. This review provides information on the key points of the database and compares it to Web of Science. Neither database is inclusive, but complements each other. If a library can only afford one, choice must be based in institutional needs.

Database principles programming performance

CERN Document Server

O'Neil, Patrick

2014-01-01

Database: Principles Programming Performance provides an introduction to the fundamental principles of database systems. This book focuses on database programming and the relationships between principles, programming, and performance.Organized into 10 chapters, this book begins with an overview of database design principles and presents a comprehensive introduction to the concepts used by a DBA. This text then provides grounding in many abstract concepts of the relational model. Other chapters introduce SQL, describing its capabilities and covering the statements and functions of the programmi

Post-processing of Deep Web Information Extraction Based on Domain Ontology

Directory of Open Access Journals (Sweden)

PENG, T.

2013-11-01

Full Text Available Many methods are utilized to extract and process query results in deep Web, which rely on the different structures of Web pages and various designing modes of databases. However, some semantic meanings and relations are ignored. So, in this paper, we present an approach for post-processing deep Web query results based on domain ontology which can utilize the semantic meanings and relations. A block identification model (BIM based on node similarity is defined to extract data blocks that are relevant to specific domain after reducing noisy nodes. Feature vector of domain books is obtained by result set extraction model (RSEM based on vector space model (VSM. RSEM, in combination with BIM, builds the domain ontology on books which can not only remove the limit of Web page structures when extracting data information, but also make use of semantic meanings of domain ontology. After extracting basic information of Web pages, a ranking algorithm is adopted to offer an ordered list of data records to users. Experimental results show that BIM and RSEM extract data blocks and build domain ontology accurately. In addition, relevant data records and basic information are extracted and ranked. The performances precision and recall show that our proposed method is feasible and efficient.

Network and Database Security: Regulatory Compliance, Network, and Database Security - A Unified Process and Goal

Directory of Open Access Journals (Sweden)

Errol A. Blake

2007-12-01

Full Text Available Database security has evolved; data security professionals have developed numerous techniques and approaches to assure data confidentiality, integrity, and availability. This paper will show that the Traditional Database Security, which has focused primarily on creating user accounts and managing user privileges to database objects are not enough to protect data confidentiality, integrity, and availability. This paper is a compilation of different journals, articles and classroom discussions will focus on unifying the process of securing data or information whether it is in use, in storage or being transmitted. Promoting a change in Database Curriculum Development trends may also play a role in helping secure databases. This paper will take the approach that if one make a conscientious effort to unifying the Database Security process, which includes Database Management System (DBMS selection process, following regulatory compliances, analyzing and learning from the mistakes of others, Implementing Networking Security Technologies, and Securing the Database, may prevent database breach.

Hazard Analysis Database Report

CERN Document Server

Grams, W H

2000-01-01

The Hazard Analysis Database was developed in conjunction with the hazard analysis activities conducted in accordance with DOE-STD-3009-94, Preparation Guide for U S . Department of Energy Nonreactor Nuclear Facility Safety Analysis Reports, for HNF-SD-WM-SAR-067, Tank Farms Final Safety Analysis Report (FSAR). The FSAR is part of the approved Authorization Basis (AB) for the River Protection Project (RPP). This document describes, identifies, and defines the contents and structure of the Tank Farms FSAR Hazard Analysis Database and documents the configuration control changes made to the database. The Hazard Analysis Database contains the collection of information generated during the initial hazard evaluations and the subsequent hazard and accident analysis activities. The Hazard Analysis Database supports the preparation of Chapters 3 ,4 , and 5 of the Tank Farms FSAR and the Unreviewed Safety Question (USQ) process and consists of two major, interrelated data sets: (1) Hazard Analysis Database: Data from t...

Development of Database for Accident Analysis in Indian Mines

Science.gov (United States)

Tripathy, Debi Prasad; Guru Raghavendra Reddy, K.

2016-10-01

Mining is a hazardous industry and high accident rates associated with underground mining is a cause of deep concern. Technological developments notwithstanding, rate of fatal accidents and reportable incidents have not shown corresponding levels of decline. This paper argues that adoption of appropriate safety standards by both mine management and the government may result in appreciable reduction in accident frequency. This can be achieved by using the technology in improving the working conditions, sensitising workers and managers about causes and prevention of accidents. Inputs required for a detailed analysis of an accident include information on location, time, type, cost of accident, victim, nature of injury, personal and environmental factors etc. Such information can be generated from data available in the standard coded accident report form. This paper presents a web based application for accident analysis in Indian mines during 2001-2013. An accident database (SafeStat) prototype based on Intranet of the TCP/IP agreement, as developed by the authors, is also discussed.

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.

Deep Borehole Emplacement Mode Hazard Analysis Revision 0

Energy Technology Data Exchange (ETDEWEB)

Sevougian, S. David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-08-07

This letter report outlines a methodology and provides resource information for the Deep Borehole Emplacement Mode Hazard Analysis (DBEMHA). The main purpose is identify the accident hazards and accident event sequences associated with the two emplacement mode options (wireline or drillstring), to outline a methodology for computing accident probabilities and frequencies, and to point to available databases on the nature and frequency of accidents typically associated with standard borehole drilling and nuclear handling operations. Risk mitigation and prevention measures, which have been incorporated into the two emplacement designs (see Cochran and Hardin 2015), are also discussed. A key intent of this report is to provide background information to brief subject matter experts involved in the Emplacement Mode Design Study. [Note: Revision 0 of this report is concentrated more on the wireline emplacement mode. It is expected that Revision 1 will contain further development of the preliminary fault and event trees for the drill string emplacement mode.

Database for propagation models

Science.gov (United States)

Kantak, Anil V.

1991-07-01

A propagation researcher or a systems engineer who intends to use the results of a propagation experiment is generally faced with various database tasks such as the selection of the computer software, the hardware, and the writing of the programs to pass the data through the models of interest. This task is repeated every time a new experiment is conducted or the same experiment is carried out at a different location generating different data. Thus the users of this data have to spend a considerable portion of their time learning how to implement the computer hardware and the software towards the desired end. This situation may be facilitated considerably if an easily accessible propagation database is created that has all the accepted (standardized) propagation phenomena models approved by the propagation research community. Also, the handling of data will become easier for the user. Such a database construction can only stimulate the growth of the propagation research it if is available to all the researchers, so that the results of the experiment conducted by one researcher can be examined independently by another, without different hardware and software being used. The database may be made flexible so that the researchers need not be confined only to the contents of the database. Another way in which the database may help the researchers is by the fact that they will not have to document the software and hardware tools used in their research since the propagation research community will know the database already. The following sections show a possible database construction, as well as properties of the database for the propagation research.

Update History of This Database - TP Atlas | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us TP Atlas Update History of This Database Date Update contents 2013/12/16 The email address i...s ( http://www.tanpaku.org/tpatlas/ ) is opened. About This Database Database Description Download License Update History of Thi...s Database Site Policy | Contact Us Update History of This Database - TP Atlas | LSDB Archive ... ...n the contact information is corrected. 2013/11/19 TP Atlas English archive site is opened. 2008/4/1 TP Atla

Update History of This Database - KEGG MEDICUS | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available glish archive site is opened. 2010/10/01 KEGG MEDICUS ( http://www.kegg.jp/kegg/medicus/ ) is opened. About ...[ Credits ] English ]; } else if ( url.search(//en//) != -1 ) { url = url.replace(/...switchLanguage; BLAST Search Image Search Home About Archive Update History Data List Contact us KEGG MEDI...CUS Update History of This Database Date Update contents 2014/05/09 KEGG MEDICUS En...This Database Database Description Download License Update History of This Database Site Policy | Contact Us Update History of This Database - KEGG MEDICUS | LSDB Archive ...

Coordinating Mobile Databases: A System Demonstration

OpenAIRE

Zaihrayeu, Ilya; Giunchiglia, Fausto

2004-01-01

In this paper we present the Peer Database Management System (PDBMS). This system runs on top of the standard database management system, and it allows it to connect its database with other (peer) databases on the network. A particularity of our solution is that PDBMS allows for conventional database technology to be effectively operational in mobile settings. We think of database mobility as a database network, where databases appear and disappear spontaneously and their network access point...

Danish Urogynaecological Database

DEFF Research Database (Denmark)

Hansen, Ulla Darling; Gradel, Kim Oren; Larsen, Michael Due

2016-01-01

, complications if relevant, implants used if relevant, 3-6-month postoperative recording of symptoms, if any. A set of clinical quality indicators is being maintained by the steering committee for the database and is published in an annual report which also contains extensive descriptive statistics. The database......The Danish Urogynaecological Database is established in order to ensure high quality of treatment for patients undergoing urogynecological surgery. The database contains details of all women in Denmark undergoing incontinence surgery or pelvic organ prolapse surgery amounting to ~5,200 procedures...... has a completeness of over 90% of all urogynecological surgeries performed in Denmark. Some of the main variables have been validated using medical records as gold standard. The positive predictive value was above 90%. The data are used as a quality monitoring tool by the hospitals and in a number...

Blind CT image quality assessment via deep learning strategy: initial study

Science.gov (United States)

Li, Sui; He, Ji; Wang, Yongbo; Liao, Yuting; Zeng, Dong; Bian, Zhaoying; Ma, Jianhua

2018-03-01

Computed Tomography (CT) is one of the most important medical imaging modality. CT images can be used to assist in the detection and diagnosis of lesions and to facilitate follow-up treatment. However, CT images are vulnerable to noise. Actually, there are two major source intrinsically causing the CT data noise, i.e., the X-ray photo statistics and the electronic noise background. Therefore, it is necessary to doing image quality assessment (IQA) in CT imaging before diagnosis and treatment. Most of existing CT images IQA methods are based on human observer study. However, these methods are impractical in clinical for their complex and time-consuming. In this paper, we presented a blind CT image quality assessment via deep learning strategy. A database of 1500 CT images is constructed, containing 300 high-quality images and 1200 corresponding noisy images. Specifically, the high-quality images were used to simulate the corresponding noisy images at four different doses. Then, the images are scored by the experienced radiologists by the following attributes: image noise, artifacts, edge and structure, overall image quality, and tumor size and boundary estimation with five-point scale. We trained a network for learning the non-liner map from CT images to subjective evaluation scores. Then, we load the pre-trained model to yield predicted score from the test image. To demonstrate the performance of the deep learning network in IQA, correlation coefficients: Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are utilized. And the experimental result demonstrate that the presented deep learning based IQA strategy can be used in the CT image quality assessment.

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.

LandIT Database

DEFF Research Database (Denmark)

Iftikhar, Nadeem; Pedersen, Torben Bach

2010-01-01

and reporting purposes. This paper presents the LandIT database; which is result of the LandIT project, which refers to an industrial collaboration project that developed technologies for communication and data integration between farming devices and systems. The LandIT database in principal is based...... on the ISOBUS standard; however the standard is extended with additional requirements, such as gradual data aggregation and flexible exchange of farming data. This paper describes the conceptual and logical schemas of the proposed database based on a real-life farming case study....

Network-based Database Course

DEFF Research Database (Denmark)

Nielsen, J.N.; Knudsen, Morten; Nielsen, Jens Frederik Dalsgaard

A course in database design and implementation has been de- signed, utilizing existing network facilities. The course is an elementary course for students of computer engineering. Its purpose is to give the students a theoretical database knowledge as well as practical experience with design...... and implementation. A tutorial relational database and the students self-designed databases are implemented on the UNIX system of Aalborg University, thus giving the teacher the possibility of live demonstrations in the lecture room, and the students the possibility of interactive learning in their working rooms...

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.

New optimized drill pipe size for deep-water, extended reach and ultra-deep drilling

Energy Technology Data Exchange (ETDEWEB)

Jellison, Michael J.; Delgado, Ivanni [Grant Prideco, Inc., Hoston, TX (United States); Falcao, Jose Luiz; Sato, Ademar Takashi [PETROBRAS, Rio de Janeiro, RJ (Brazil); Moura, Carlos Amsler [Comercial Perfuradora Delba Baiana Ltda., Rio de Janeiro, RJ (Brazil)

2004-07-01

A new drill pipe size, 5-7/8 in. OD, represents enabling technology for Extended Reach Drilling (ERD), deep water and other deep well applications. Most world-class ERD and deep water wells have traditionally been drilled with 5-1/2 in. drill pipe or a combination of 6-5/8 in. and 5-1/2 in. drill pipe. The hydraulic performance of 5-1/2 in. drill pipe can be a major limitation in substantial ERD and deep water wells resulting in poor cuttings removal, slower penetration rates, diminished control over well trajectory and more tendency for drill pipe sticking. The 5-7/8 in. drill pipe provides a significant improvement in hydraulic efficiency compared to 5-1/2 in. drill pipe and does not suffer from the disadvantages associated with use of 6-5/8 in. drill pipe. It represents a drill pipe assembly that is optimized dimensionally and on a performance basis for casing and bit programs that are commonly used for ERD, deep water and ultra-deep wells. The paper discusses the engineering philosophy behind 5-7/8 in. drill pipe, the design challenges associated with development of the product and reviews the features and capabilities of the second-generation double-shoulder connection. The paper provides drilling case history information on significant projects where the pipe has been used and details results achieved with the pipe. (author)

Deep Reinforcement Learning: An Overview

OpenAIRE

Li, Yuxi

2017-01-01

We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsuperv...

Migration Between NoSQL Databases

OpenAIRE

Opačak, Damir

2013-01-01

The thesis discusses the differences and, consequently, potential problems that may arise when migrating between different types of NoSQL databases. The first chapters introduce the reader to the issues of relational databases and present the beginnings of NoSQL databases. The following chapters present different types of NoSQL databases and some of their representatives with the aim to show specific features of NoSQL databases and the fact that each of them was developed to solve specifi...

Age Estimation Robust to Optical and Motion Blurring by Deep Residual CNN

Directory of Open Access Journals (Sweden)

Jeon Seong Kang

2018-04-01

Full Text Available Recently, real-time human age estimation based on facial images has been applied in various areas. Underneath this phenomenon lies an awareness that age estimation plays an important role in applying big data to target marketing for age groups, product demand surveys, consumer trend analysis, etc. However, in a real-world environment, various optical and motion blurring effects can occur. Such effects usually cause a problem in fully capturing facial features such as wrinkles, which are essential to age estimation, thereby degrading accuracy. Most of the previous studies on age estimation were conducted for input images almost free from blurring effect. To overcome this limitation, we propose the use of a deep ResNet-152 convolutional neural network for age estimation, which is robust to various optical and motion blurring effects of visible light camera sensors. We performed experiments with various optical and motion blurred images created from the park aging mind laboratory (PAL and craniofacial longitudinal morphological face database (MORPH databases, which are publicly available. According to the results, the proposed method exhibited better age estimation performance than the previous methods.

Update History of This Database - Q-TARO | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us Q-TARO Update History of This Database Date Update contents 2014/10/20 The URL of the portal...ption Download License Update History of This Database Site Policy | Contact Us Update History of This Database - Q-TARO | LSDB Archive ... ... site is changed. 2013/12/17 The URL of the portal site is changed. 2013/12/13 Q-TARO English archive site i...s opened. 2009/11/15 Q-TARO ( http://qtaro.abr.affrc.go.jp/ ) is opened. About This Database Database Descri

Report on fiscal 2000 survey for geothermal exploration technology verification. Survey of deep-seated geothermal resources; 2000 nendo chinetsu tansa gijutsu nado kensho chosa hokokusho. Shinbu chinetsu shigen chosa

Energy Technology Data Exchange (ETDEWEB)

NONE

2001-03-01

To promote the development of deep-seated geothermal resources in a rationalized way, studies are conducted about deep-seated geothermal resource assessment techniques, development guidelines, and the like. Data were collected at the Sumikawa-Onuma district, Ogiri district, Mori district, Yanaizu-Nishiyama district, and the Onikobe district, and compiled into a database to be open to the public. Studies were made about methods for estimating parameters for deep-seated geothermal reservoirs. The resultant findings indicate that, in the Uenotai and Sumikawa-Onuma districts where geothermal reservoirs are governed mainly by a fracture network, the relaxation method and extrapolation will be effective for deep-seated reservoir temperature estimation, and the ascending current analysis method and extrapolation for permeability estimation. The findings also indicate that the expanse of deep-seated reservoirs will be suitably estimated using a method similar to that applied to shallow-seated reservoirs. In the study of the estimation of the amount of deep-seated geothermal resources, it is concluded that the simplified model A will be effective in dealing with a geothermal district where there is a well-developed fracture network and the simplified model B in dealing with a geothermal district where supply of deep-seated fluid governed by an extensive fault prevails. (NEDO)

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.

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

Science.gov (United States)

Yildirim, Özal

2018-05-01

Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems. Copyright © 2018 Elsevier Ltd. All rights reserved.

Scientific Considerations for the Assessment and Management of Mine Tailings Disposal in the Deep Sea

Directory of Open Access Journals (Sweden)

Lindsay L. Vare

2018-02-01

Full Text Available Deep-sea tailings disposal (DSTD and its shallow water counterpart, submarine tailings disposal (STD, are practiced in many areas of the world, whereby mining industries discharge processed mud- and rock-waste slurries (tailings directly into the marine environment. Pipeline discharges and other land-based sources of marine pollution fall beyond the regulatory scope of the London Convention and the London Protocols (LC/LP. However, guidelines have been developed in Papua New Guinea (PNG to improve tailings waste management frameworks in which mining companies can operate. DSTD can impact ocean ecosystems in addition to other sources of stress, such as from fishing, pollution, energy extraction, tourism, eutrophication, climate change and, potentially in the future, from deep-seabed mining. Environmental management of DSTD may be most effective when placed in a broader context, drawing expertise, data and lessons from multiple sectors (academia, government, society, industry, and regulators and engaging with international deep-ocean observing programs, databases and stewardship consortia. Here, the challenges associated with DSTD are identified, along with possible solutions, based on the results of a number of robust scientific studies. Also highlighted are the key issues, trends of improved practice and techniques that could be used if considering DSTD (such as increased precaution if considering submarine canyon locations, likely cumulative impacts, and research needed to address current knowledge gaps.

Comparison of the Frontier Distributed Database Caching System with NoSQL Databases

CERN Document Server

Dykstra, David

2012-01-01

One of the main attractions of non-relational "NoSQL" databases is their ability to scale to large numbers of readers, including readers spread over a wide area. The Frontier distributed database caching system, used in production by the Large Hadron Collider CMS and ATLAS detector projects for Conditions data, is based on traditional SQL databases but also has high scalability and wide-area distributability for an important subset of applications. This paper compares the major characteristics of the two different approaches and identifies the criteria for choosing which approach to prefer over the other. It also compares in some detail the NoSQL databases used by CMS and ATLAS: MongoDB, CouchDB, HBase, and Cassandra.

Towards cloud-centric distributed database evaluation

OpenAIRE

Seybold, Daniel

2016-01-01

The area of cloud computing also pushed the evolvement of distributed databases, resulting in a variety of distributed database systems, which can be classified in relation databases, NoSQL and NewSQL database systems. In general all representatives of these database system classes claim to provide elasticity and "unlimited" horizontal scalability. As these characteristics comply with the cloud, distributed databases seem to be a perfect match for Database-as-a-Service systems (DBaaS).

Towards Cloud-centric Distributed Database Evaluation

OpenAIRE

Seybold, Daniel

2016-01-01

The area of cloud computing also pushed the evolvement of distributed databases, resulting in a variety of distributed database systems, which can be classified in relation databases, NoSQL and NewSQL database systems. In general all representatives of these database system classes claim to provide elasticity and "unlimited" horizontal scalability. As these characteristics comply with the cloud, distributed databases seem to be a perfect match for Database-as-a-Service systems (DBaaS).

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.

REPLIKASI UNIDIRECTIONAL PADA HETEROGEN DATABASE

OpenAIRE

Hendro Nindito; Evaristus Didik Madyatmadja; Albert Verasius Dian Sano

2013-01-01

The use of diverse database technology in enterprise today can not be avoided. Thus, technology is needed to generate information in real time. The purpose of this research is to discuss a database replication technology that can be applied in heterogeneous database environments. In this study we use Windows-based MS SQL Server database to Linux-based Oracle database as the goal. The research method used is prototyping where development can be done quickly and testing of working models of the...

The Danish Testicular Cancer database

DEFF Research Database (Denmark)

Daugaard, Gedske; Kier, Maria Gry Gundgaard; Bandak, Mikkel

2016-01-01

AIM: The nationwide Danish Testicular Cancer database consists of a retrospective research database (DaTeCa database) and a prospective clinical database (Danish Multidisciplinary Cancer Group [DMCG] DaTeCa database). The aim is to improve the quality of care for patients with testicular cancer (TC......) in Denmark, that is, by identifying risk factors for relapse, toxicity related to treatment, and focusing on late effects. STUDY POPULATION: All Danish male patients with a histologically verified germ cell cancer diagnosis in the Danish Pathology Registry are included in the DaTeCa databases. Data...... collection has been performed from 1984 to 2007 and from 2013 onward, respectively. MAIN VARIABLES AND DESCRIPTIVE DATA: The retrospective DaTeCa database contains detailed information with more than 300 variables related to histology, stage, treatment, relapses, pathology, tumor markers, kidney function...

Docker Containers for Deep Learning Experiments

OpenAIRE

Gerke, Paul K.

2017-01-01

Deep learning is a powerful tool to solve problems in the area of image analysis. The dominant compute platform for deep learning is Nvidia’s proprietary CUDA, which can only be used together with Nvidia graphics cards. The nivida-docker project allows exposing Nvidia graphics cards to docker containers and thus makes it possible to run deep learning experiments in docker containers.In our department, we use deep learning to solve problems in the area of medical image analysis and use docker ...

Auxiliary Deep Generative Models

DEFF Research Database (Denmark)

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

2016-01-01

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

Accelerating Deep Learning with Shrinkage and Recall

OpenAIRE

Zheng, Shuai; Vishnu, Abhinav; Ding, Chris

2016-01-01

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Lea...

Replikasi Unidirectional pada Heterogen Database

Directory of Open Access Journals (Sweden)

Hendro Nindito

2013-12-01

Full Text Available The use of diverse database technology in enterprise today can not be avoided. Thus, technology is needed to generate information in real time. The purpose of this research is to discuss a database replication technology that can be applied in heterogeneous database environments. In this study we use Windows-based MS SQL Server database to Linux-based Oracle database as the goal. The research method used is prototyping where development can be done quickly and testing of working models of the interaction process is done through repeated. From this research it is obtained that the database replication technolgy using Oracle Golden Gate can be applied in heterogeneous environments in real time as well.

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...

Danish clinical databases: An overview

DEFF Research Database (Denmark)

Green, Anders

2011-01-01

Clinical databases contain data related to diagnostic procedures, treatments and outcomes. In 2001, a scheme was introduced for the approval, supervision and support to clinical databases in Denmark.......Clinical databases contain data related to diagnostic procedures, treatments and outcomes. In 2001, a scheme was introduced for the approval, supervision and support to clinical databases in Denmark....

Dictionary as Database.

Science.gov (United States)

Painter, Derrick

1996-01-01

Discussion of dictionaries as databases focuses on the digitizing of The Oxford English dictionary (OED) and the use of Standard Generalized Mark-Up Language (SGML). Topics include the creation of a consortium to digitize the OED, document structure, relational databases, text forms, sequence, and discourse. (LRW)

INIST: databases reorientation

International Nuclear Information System (INIS)

Bidet, J.C.

1995-01-01

INIST is a CNRS (Centre National de la Recherche Scientifique) laboratory devoted to the treatment of scientific and technical informations and to the management of these informations compiled in a database. Reorientation of the database content has been proposed in 1994 to increase the transfer of research towards enterprises and services, to develop more automatized accesses to the informations, and to create a quality assurance plan. The catalog of publications comprises 5800 periodical titles (1300 for fundamental research and 4500 for applied research). A science and technology multi-thematic database will be created in 1995 for the retrieval of applied and technical informations. ''Grey literature'' (reports, thesis, proceedings..) and human and social sciences data will be added to the base by the use of informations selected in the existing GRISELI and Francis databases. Strong modifications are also planned in the thematic cover of Earth sciences and will considerably reduce the geological information content. (J.S.). 1 tab

The Danish Testicular Cancer database

Directory of Open Access Journals (Sweden)

Daugaard G

2016-10-01

Full Text Available Gedske Daugaard,1 Maria Gry Gundgaard Kier,1 Mikkel Bandak,1 Mette Saksø Mortensen,1 Heidi Larsson,2 Mette Søgaard,2 Birgitte Groenkaer Toft,3 Birte Engvad,4 Mads Agerbæk,5 Niels Vilstrup Holm,6 Jakob Lauritsen1 1Department of Oncology 5073, Copenhagen University Hospital, Rigshospitalet, Copenhagen, 2Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, 3Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, 4Department of Pathology, Odense University Hospital, Odense, 5Department of Oncology, Aarhus University Hospital, Aarhus, 6Department of Oncology, Odense University Hospital, Odense, Denmark Aim: The nationwide Danish Testicular Cancer database consists of a retrospective research database (DaTeCa database and a prospective clinical database (Danish Multidisciplinary Cancer Group [DMCG] DaTeCa database. The aim is to improve the quality of care for patients with testicular cancer (TC in Denmark, that is, by identifying risk factors for relapse, toxicity related to treatment, and focusing on late effects. Study population: All Danish male patients with a histologically verified germ cell cancer diagnosis in the Danish Pathology Registry are included in the DaTeCa databases. Data collection has been performed from 1984 to 2007 and from 2013 onward, respectively. Main variables and descriptive data: The retrospective DaTeCa database contains detailed information with more than 300 variables related to histology, stage, treatment, relapses, pathology, tumor markers, kidney function, lung function, etc. A questionnaire related to late effects has been conducted, which includes questions regarding social relationships, life situation, general health status, family background, diseases, symptoms, use of medication, marital status, psychosocial issues, fertility, and sexuality. TC survivors alive on October 2014 were invited to fill in this questionnaire including 160 validated questions

Science.Gov - A single gateway to the deep web knowledge of U.S. science agencies

International Nuclear Information System (INIS)

Hitson, B.A.

2004-01-01

The impact of science and technology on our daily lives is easily demonstrated. From new drug discoveries, to new and more efficient energy sources, to the incorporation of new technologies into business and industry, the productive applications of R and D are innumerable. The possibility of creating such applications depends most heavily on the availability of one resource: knowledge. Knowledge must be shared for scientific progress to occur. In the past, the ability to share knowledge electronically has been limited by the 'deep Web' nature of scientific databases and the lack of technology to simultaneously search disparate and decentralized information collections. U.S. science agencies invest billions of dollars each year on basic and applied research and development projects. To make the collective knowledge from this R and D more easily accessible and searchable, 12 science agencies collaborated to develop Science.gov - a single, searchable gateway to the deep Web knowledge of U.S. science agencies. This paper will describe Science.gov and its contribution to nuclear knowledge management. (author)

ASSIMILATION OF REAL-TIME DEEP SEA BUOY DATA FOR TSUNAMI FORECASTING ALONG THAILAND’S ANDAMAN COASTLINE

Directory of Open Access Journals (Sweden)

Seree Supharatid

2008-01-01

Full Text Available The occurrence of 2004 Indian Ocean tsunami enhanced the necessity for a tsunami early warning system for countries bordering the Indian Ocean, including Thailand. This paper describes the assimilation of real-time deep sea buoy data for tsunami forecasting along Thailand’s Andaman coastline. Firstly, the numerical simulation (by the linear and non-linear shallow water equations was carried out for hypothetical cases of tsunamigenic earthquakes with epicenters located in the Andaman micro plate. Outputs of the numerical model are tsunami arrival times and the maximum wave height that can be expected at 58 selected communities along Thailand Andaman coastline and two locations of DART buoys in the Indian Ocean. Secondly, a “neural” network model (GRNN was developed to access the data from the numerical computations for subsequent construction of a tsunami database that can be displayed on a web-based system. This database can be updated with the integration from two DART buoys and from several GRNN models.

Comparison of the Frontier Distributed Database Caching System to NoSQL Databases

Science.gov (United States)

Dykstra, Dave

2012-12-01

One of the main attractions of non-relational “NoSQL” databases is their ability to scale to large numbers of readers, including readers spread over a wide area. The Frontier distributed database caching system, used in production by the Large Hadron Collider CMS and ATLAS detector projects for Conditions data, is based on traditional SQL databases but also adds high scalability and the ability to be distributed over a wide-area for an important subset of applications. This paper compares the major characteristics of the two different approaches and identifies the criteria for choosing which approach to prefer over the other. It also compares in some detail the NoSQL databases used by CMS and ATLAS: MongoDB, CouchDB, HBase, and Cassandra.

Comparison of the Frontier Distributed Database Caching System to NoSQL Databases

International Nuclear Information System (INIS)

Dykstra, Dave

2012-01-01

One of the main attractions of non-relational “NoSQL” databases is their ability to scale to large numbers of readers, including readers spread over a wide area. The Frontier distributed database caching system, used in production by the Large Hadron Collider CMS and ATLAS detector projects for Conditions data, is based on traditional SQL databases but also adds high scalability and the ability to be distributed over a wide-area for an important subset of applications. This paper compares the major characteristics of the two different approaches and identifies the criteria for choosing which approach to prefer over the other. It also compares in some detail the NoSQL databases used by CMS and ATLAS: MongoDB, CouchDB, HBase, and Cassandra.

Comparison of the Frontier Distributed Database Caching System to NoSQL Databases

Energy Technology Data Exchange (ETDEWEB)

Dykstra, Dave [Fermilab

2012-07-20

One of the main attractions of non-relational NoSQL databases is their ability to scale to large numbers of readers, including readers spread over a wide area. The Frontier distributed database caching system, used in production by the Large Hadron Collider CMS and ATLAS detector projects for Conditions data, is based on traditional SQL databases but also adds high scalability and the ability to be distributed over a wide-area for an important subset of applications. This paper compares the major characteristics of the two different approaches and identifies the criteria for choosing which approach to prefer over the other. It also compares in some detail the NoSQL databases used by CMS and ATLAS: MongoDB, CouchDB, HBase, and Cassandra.

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...

Development of database on the distribution coefficient. 2. Preparation of database

Energy Technology Data Exchange (ETDEWEB)

Takebe, Shinichi; Abe, Masayoshi [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

2001-03-01

The distribution coefficient is very important parameter for environmental impact assessment on the disposal of radioactive waste arising from research institutes. 'Database on the Distribution Coefficient' was built up from the informations which were obtained by the literature survey in the country for these various items such as value , measuring method and measurement condition of distribution coefficient, in order to select the reasonable distribution coefficient value on the utilization of this value in the safety evaluation. This report was explained about the outline on preparation of this database and was summarized as a use guide book of database. (author)

Development of database on the distribution coefficient. 2. Preparation of database

Energy Technology Data Exchange (ETDEWEB)

Takebe, Shinichi; Abe, Masayoshi [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

2001-03-01

The distribution coefficient is very important parameter for environmental impact assessment on the disposal of radioactive waste arising from research institutes. 'Database on the Distribution Coefficient' was built up from the informations which were obtained by the literature survey in the country for these various items such as value , measuring method and measurement condition of distribution coefficient, in order to select the reasonable distribution coefficient value on the utilization of this value in the safety evaluation. This report was explained about the outline on preparation of this database and was summarized as a use guide book of database. (author)

Balkan Vegetation Database

NARCIS (Netherlands)

Vassilev, Kiril; Pedashenko, Hristo; Alexandrova, Alexandra; Tashev, Alexandar; Ganeva, Anna; Gavrilova, Anna; Gradevska, Asya; Assenov, Assen; Vitkova, Antonina; Grigorov, Borislav; Gussev, Chavdar; Filipova, Eva; Aneva, Ina; Knollová, Ilona; Nikolov, Ivaylo; Georgiev, Georgi; Gogushev, Georgi; Tinchev, Georgi; Pachedjieva, Kalina; Koev, Koycho; Lyubenova, Mariyana; Dimitrov, Marius; Apostolova-Stoyanova, Nadezhda; Velev, Nikolay; Zhelev, Petar; Glogov, Plamen; Natcheva, Rayna; Tzonev, Rossen; Boch, Steffen; Hennekens, Stephan M.; Georgiev, Stoyan; Stoyanov, Stoyan; Karakiev, Todor; Kalníková, Veronika; Shivarov, Veselin; Russakova, Veska; Vulchev, Vladimir

2016-01-01

The Balkan Vegetation Database (BVD; GIVD ID: EU-00-019; http://www.givd.info/ID/EU-00- 019) is a regional database that consists of phytosociological relevés from different vegetation types from six countries on the Balkan Peninsula (Albania, Bosnia and Herzegovina, Bulgaria, Kosovo, Montenegro

Global Tsunami Database: Adding Geologic Deposits, Proxies, and Tools

Science.gov (United States)

Brocko, V. R.; Varner, J.

2007-12-01

A result of collaboration between NOAA's National Geophysical Data Center (NGDC) and the Cooperative Institute for Research in the Environmental Sciences (CIRES), the Global Tsunami Database includes instrumental records, human observations, and now, information inferred from the geologic record. Deep Ocean Assessment and Reporting of Tsunamis (DART) data, historical reports, and information gleaned from published tsunami deposit research build a multi-faceted view of tsunami hazards and their history around the world. Tsunami history provides clues to what might happen in the future, including frequency of occurrence and maximum wave heights. However, instrumental and written records commonly span too little time to reveal the full range of a region's tsunami hazard. The sedimentary deposits of tsunamis, identified with the aid of modern analogs, increasingly complement instrumental and human observations. By adding the component of tsunamis inferred from the geologic record, the Global Tsunami Database extends the record of tsunamis backward in time. Deposit locations, their estimated age and descriptions of the deposits themselves fill in the tsunami record. Tsunamis inferred from proxies, such as evidence for coseismic subsidence, are included to estimate recurrence intervals, but are flagged to highlight the absence of a physical deposit. Authors may submit their own descriptions and upload digital versions of publications. Users may sort by any populated field, including event, location, region, age of deposit, author, publication type (extract information from peer reviewed publications only, if you wish), grain size, composition, presence/absence of plant material. Users may find tsunami deposit references for a given location, event or author; search for particular properties of tsunami deposits; and even identify potential collaborators. Users may also download public-domain documents. Data and information may be viewed using tools designed to extract and

Update History of This Database - GenLibi | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us GenLibi Update History of This Database Date Update contents 2014/03/25 GenLibi English archi...base Description Download License Update History of This Database Site Policy | Contact Us Update History of This Database - GenLibi | LSDB Archive ... ...ve site is opened. 2007/03/01 GenLibi ( http://gene.biosciencedbc.jp/ ) is opened. About This Database Data

Update History of This Database - dbQSNP | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data ...List Contact us dbQSNP Update History of This Database Date Update contents 2017/02/16 dbQSNP English archiv...e Description Download License Update History of This Database Site Policy | Contact Us Update History of This Database - dbQSNP | LSDB Archive ... ...e site is opened. 2002/10/23 dbQSNP (http://qsnp.gen.kyushu-u.ac.jp/) is opened. About This Database Databas

A new relational database structure and online interface for the HITRAN database

Science.gov (United States)

Hill, Christian; Gordon, Iouli E.; Rothman, Laurence S.; Tennyson, Jonathan

2013-11-01

A new format for the HITRAN database is proposed. By storing the line-transition data in a number of linked tables described by a relational database schema, it is possible to overcome the limitations of the existing format, which have become increasingly apparent over the last few years as new and more varied data are being used by radiative-transfer models. Although the database in the new format can be searched using the well-established Structured Query Language (SQL), a web service, HITRANonline, has been deployed to allow users to make most common queries of the database using a graphical user interface in a web page. The advantages of the relational form of the database to ensuring data integrity and consistency are explored, and the compatibility of the online interface with the emerging standards of the Virtual Atomic and Molecular Data Centre (VAMDC) project is discussed. In particular, the ability to access HITRAN data using a standard query language from other websites, command line tools and from within computer programs is described.

Linking the Taiwan Fish Database to the Global Database

Directory of Open Access Journals (Sweden)

Kwang-Tsao Shao

2007-03-01

Full Text Available Under the support of the National Digital Archive Program (NDAP, basic species information about most Taiwanese fishes, including their morphology, ecology, distribution, specimens with photos, and literatures have been compiled into the "Fish Database of Taiwan" (http://fishdb.sinica.edu.tw. We expect that the all Taiwanese fish species databank (RSD, with 2800+ species, and the digital "Fish Fauna of Taiwan" will be completed in 2007. Underwater ecological photos and video images for all 2,800+ fishes are quite difficult to achieve but will be collected continuously in the future. In the last year of NDAP, we have successfully integrated all fish specimen data deposited at 7 different institutes in Taiwan as well as their collection maps on the Google Map and Google Earth. Further, the database also provides the pronunciation of Latin scientific names and transliteration of Chinese common names by referring to the Romanization system for all Taiwanese fishes (2,902 species in 292 families so far. The Taiwanese fish species checklist with Chinese common/vernacular names and specimen data has been updated periodically and provided to the global FishBase as well as the Global Biodiversity Information Facility (GBIF through the national portal of the Taiwan Biodiversity Information Facility (TaiBIF. Thus, Taiwanese fish data can be queried and browsed on the WWW. For contributing to the "Barcode of Life" and "All Fishes" international projects, alcohol-preserved specimens of more than 1,800 species and cryobanking tissues of 800 species have been accumulated at RCBAS in the past two years. Through this close collaboration between local and global databases, "The Fish Database of Taiwan" now attracts more than 250,000 visitors and achieves 5 million hits per month. We believe that this local database is becoming an important resource for education, research, conservation, and sustainable use of fish in Taiwan.

Tradeoffs in distributed databases

OpenAIRE

Juntunen, R. (Risto)

2016-01-01

Abstract In a distributed database data is spread throughout the network into separated nodes with different DBMS systems (Date, 2000). According to CAP-theorem three database properties — consistency, availability and partition tolerance cannot be achieved simultaneously in distributed database systems. Two of these properties can be achieved but not all three at the same time (Brewer, 2000). Since this theorem there has b...

Automated Oracle database testing

CERN Multimedia

CERN. Geneva

2014-01-01

Ensuring database stability and steady performance in the modern world of agile computing is a major challenge. Various changes happening at any level of the computing infrastructure: OS parameters & packages, kernel versions, database parameters & patches, or even schema changes, all can potentially harm production services. This presentation shows how an automatic and regular testing of Oracle databases can be achieved in such agile environment.

Database Systems - Present and Future

Directory of Open Access Journals (Sweden)

2009-01-01

Full Text Available The database systems have nowadays an increasingly important role in the knowledge-based society, in which computers have penetrated all fields of activity and the Internet tends to develop worldwide. In the current informatics context, the development of the applications with databases is the work of the specialists. Using databases, reach a database from various applications, and also some of related concepts, have become accessible to all categories of IT users. This paper aims to summarize the curricular area regarding the fundamental database systems issues, which are necessary in order to train specialists in economic informatics higher education. The database systems integrate and interfere with several informatics technologies and therefore are more difficult to understand and use. Thus, students should know already a set of minimum, mandatory concepts and their practical implementation: computer systems, programming techniques, programming languages, data structures. The article also presents the actual trends in the evolution of the database systems, in the context of economic informatics.

Database on wind characteristics. Contents of database bank

DEFF Research Database (Denmark)

Larsen, Gunner Chr.; Hansen, K.S.

2001-01-01

for the available data in the established database bank and part three is the Users Manual describing the various ways to access and analyse the data. The present report constitutes the second part of the Annex XVII reporting. Basically, the database bank contains three categories of data, i.e. i) high sampled wind...... field time series; ii) high sampled wind turbine structural response time series; andiii) wind resource data. The main emphasis, however, is on category i). The available data, within each of the three categories, are described in details. The description embraces site characteristics, terrain type...

USAID Anticorruption Projects Database

Data.gov (United States)

US Agency for International Development — The Anticorruption Projects Database (Database) includes information about USAID projects with anticorruption interventions implemented worldwide between 2007 and...

Database design using entity-relationship diagrams

CERN Document Server

Bagui, Sikha

2011-01-01

Data, Databases, and the Software Engineering ProcessDataBuilding a DatabaseWhat is the Software Engineering Process?Entity Relationship Diagrams and the Software Engineering Life Cycle          Phase 1: Get the Requirements for the Database          Phase 2: Specify the Database          Phase 3: Design the DatabaseData and Data ModelsFiles, Records, and Data ItemsMoving from 3 × 5 Cards to ComputersDatabase Models     The Hierarchical ModelThe Network ModelThe Relational ModelThe Relational Model and Functional DependenciesFundamental Relational DatabaseRelational Database and SetsFunctional

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....

Respiratory cancer database: An open access database of respiratory cancer gene and miRNA.

Science.gov (United States)

Choubey, Jyotsna; Choudhari, Jyoti Kant; Patel, Ashish; Verma, Mukesh Kumar

2017-01-01

Respiratory cancer database (RespCanDB) is a genomic and proteomic database of cancer of respiratory organ. It also includes the information of medicinal plants used for the treatment of various respiratory cancers with structure of its active constituents as well as pharmacological and chemical information of drug associated with various respiratory cancers. Data in RespCanDB has been manually collected from published research article and from other databases. Data has been integrated using MySQL an object-relational database management system. MySQL manages all data in the back-end and provides commands to retrieve and store the data into the database. The web interface of database has been built in ASP. RespCanDB is expected to contribute to the understanding of scientific community regarding respiratory cancer biology as well as developments of new way of diagnosing and treating respiratory cancer. Currently, the database consist the oncogenomic information of lung cancer, laryngeal cancer, and nasopharyngeal cancer. Data for other cancers, such as oral and tracheal cancers, will be added in the near future. The URL of RespCanDB is http://ridb.subdic-bioinformatics-nitrr.in/.

ADANS database specification

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-01-16

The purpose of the Air Mobility Command (AMC) Deployment Analysis System (ADANS) Database Specification (DS) is to describe the database organization and storage allocation and to provide the detailed data model of the physical design and information necessary for the construction of the parts of the database (e.g., tables, indexes, rules, defaults). The DS includes entity relationship diagrams, table and field definitions, reports on other database objects, and a description of the ADANS data dictionary. ADANS is the automated system used by Headquarters AMC and the Tanker Airlift Control Center (TACC) for airlift planning and scheduling of peacetime and contingency operations as well as for deliberate planning. ADANS also supports planning and scheduling of Air Refueling Events by the TACC and the unit-level tanker schedulers. ADANS receives input in the form of movement requirements and air refueling requests. It provides a suite of tools for planners to manipulate these requirements/requests against mobility assets and to develop, analyze, and distribute schedules. Analysis tools are provided for assessing the products of the scheduling subsystems, and editing capabilities support the refinement of schedules. A reporting capability provides formatted screen, print, and/or file outputs of various standard reports. An interface subsystem handles message traffic to and from external systems. The database is an integral part of the functionality summarized above.

Northrop Grumman TR202 LOX/LH2 Deep Throttling Engine Technology Project Status

Science.gov (United States)

Gromski, Jason; Majamaki, Annik; Chianese, Silvio; Weinstock, Vladimir; Kim, Tony S.

2010-01-01

NASA's Propulsion and Cryogenic Advanced Development (PCAD) project is currently developing enabling propulsion technologies in support of future lander missions. To meet lander requirements, several technical challenges need to be overcome, one of which is the ability for the descent engine(s) to operate over a deep throttle range with cryogenic propellants. To address this need, PCAD has enlisted Northrop Grumman Aerospace Systems (NGAS) in a technology development effort associated with the TR202 engine. The TR202 is a LOX/LH2 expander cycle engine driven by independent turbopump assemblies and featuring a variable area pintle injector similar to the injector used on the TR200 Apollo Lunar Module Descent Engine (LMDE). Since the Apollo missions, NGAS has continued to mature deep throttling pintle injector technology. The TR202 program has completed two series of pintle injector testing. The first series of testing used ablative thrust chambers and demonstrated igniter operation as well as stable performance at discrete points throughout the designed 10:1 throttle range. The second series was conducted with calorimeter chambers and demonstrated injector performance at discrete points throughout the throttle range as well as chamber heat flow adequate to power an expander cycle design across the throttle range. This paper provides an overview of the TR202 program, describing the different phases and key milestones. It describes how test data was correlated to the engine conceptual design. The test data obtained has created a valuable database for deep throttling cryogenic pintle technology, a technology that is readily scalable in thrust level.

The Hanford Site generic component failure-rate database compared with other generic failure-rate databases

International Nuclear Information System (INIS)

Reardon, M.F.; Zentner, M.D.

1992-11-01

The Risk Assessment Technology Group, Westinghouse Hanford Company (WHC), has compiled a component failure rate database to be used during risk and reliability analysis of nonreactor facilities. Because site-specific data for the Hanford Site are generally not kept or not compiled in a usable form, the database was assembled using information from a variety of other established sources. Generally, the most conservative failure rates were chosen from the databases reviewed. The Hanford Site database has since been used extensively in fault tree modeling of many Hanford Site facilities and systems. The purpose of this study was to evaluate the reasonableness of the data chosen for the Hanford Site database by comparing the values chosen with the values from the other databases

Method and electronic database search engine for exposing the content of an electronic database

NARCIS (Netherlands)

Stappers, P.J.

2000-01-01

The invention relates to an electronic database search engine comprising an electronic memory device suitable for storing and releasing elements from the database, a display unit, a user interface for selecting and displaying at least one element from the database on the display unit, and control

The LHCb configuration database

CERN Document Server

Abadie, L; Van Herwijnen, Eric; Jacobsson, R; Jost, B; Neufeld, N

2005-01-01

The aim of the LHCb configuration database is to store information about all the controllable devices of the detector. The experiment's control system (that uses PVSS ) will configure, start up and monitor the detector from the information in the configuration database. The database will contain devices with their properties, connectivity and hierarchy. The ability to store and rapidly retrieve huge amounts of data, and the navigability between devices are important requirements. We have collected use cases to ensure the completeness of the design. Using the entity relationship modelling technique we describe the use cases as classes with attributes and links. We designed the schema for the tables using relational diagrams. This methodology has been applied to the TFC (switches) and DAQ system. Other parts of the detector will follow later. The database has been implemented using Oracle to benefit from central CERN database support. The project also foresees the creation of tools to populate, maintain, and co...

The CUTLASS database facilities

International Nuclear Information System (INIS)

Jervis, P.; Rutter, P.

1988-09-01

The enhancement of the CUTLASS database management system to provide improved facilities for data handling is seen as a prerequisite to its effective use for future power station data processing and control applications. This particularly applies to the larger projects such as AGR data processing system refurbishments, and the data processing systems required for the new Coal Fired Reference Design stations. In anticipation of the need for improved data handling facilities in CUTLASS, the CEGB established a User Sub-Group in the early 1980's to define the database facilities required by users. Following the endorsement of the resulting specification and a detailed design study, the database facilities have been implemented as an integral part of the CUTLASS system. This paper provides an introduction to the range of CUTLASS Database facilities, and emphasises the role of Database as the central facility around which future Kit 1 and (particularly) Kit 6 CUTLASS based data processing and control systems will be designed and implemented. (author)

Kazusa Marker DataBase: a database for genomics, genetics, and molecular breeding in plants

Science.gov (United States)

Shirasawa, Kenta; Isobe, Sachiko; Tabata, Satoshi; Hirakawa, Hideki

2014-01-01

In order to provide useful genomic information for agronomical plants, we have established a database, the Kazusa Marker DataBase (http://marker.kazusa.or.jp). This database includes information on DNA markers, e.g., SSR and SNP markers, genetic linkage maps, and physical maps, that were developed at the Kazusa DNA Research Institute. Keyword searches for the markers, sequence data used for marker development, and experimental conditions are also available through this database. Currently, 10 plant species have been targeted: tomato (Solanum lycopersicum), pepper (Capsicum annuum), strawberry (Fragaria × ananassa), radish (Raphanus sativus), Lotus japonicus, soybean (Glycine max), peanut (Arachis hypogaea), red clover (Trifolium pratense), white clover (Trifolium repens), and eucalyptus (Eucalyptus camaldulensis). In addition, the number of plant species registered in this database will be increased as our research progresses. The Kazusa Marker DataBase will be a useful tool for both basic and applied sciences, such as genomics, genetics, and molecular breeding in crops. PMID:25320561

HIV Structural Database

Science.gov (United States)

SRD 102 HIV Structural Database (Web, free access)   The HIV Protease Structural Database is an archive of experimentally determined 3-D structures of Human Immunodeficiency Virus 1 (HIV-1), Human Immunodeficiency Virus 2 (HIV-2) and Simian Immunodeficiency Virus (SIV) Proteases and their complexes with inhibitors or products of substrate cleavage.

Lung Nodule Detection via Deep Reinforcement Learning

Directory of Open Access Journals (Sweden)

Issa Ali

2018-04-01

Full Text Available Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF recommends annual screening of high risk individuals with low-dose computed tomography (CT. The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV 99.1%, negative predictive value (NPV 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%. These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures.

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.

Towards Sensor Database Systems

DEFF Research Database (Denmark)

Bonnet, Philippe; Gehrke, Johannes; Seshadri, Praveen

2001-01-01

. These systems lack flexibility because data is extracted in a predefined way; also, they do not scale to a large number of devices because large volumes of raw data are transferred regardless of the queries that are submitted. In our new concept of sensor database system, queries dictate which data is extracted...... from the sensors. In this paper, we define the concept of sensor databases mixing stored data represented as relations and sensor data represented as time series. Each long-running query formulated over a sensor database defines a persistent view, which is maintained during a given time interval. We...... also describe the design and implementation of the COUGAR sensor database system....

Hydrogen Leak Detection Sensor Database

Science.gov (United States)

Baker, Barton D.

2010-01-01

This slide presentation reviews the characteristics of the Hydrogen Sensor database. The database is the result of NASA's continuing interest in and improvement of its ability to detect and assess gas leaks in space applications. The database specifics and a snapshot of an entry in the database are reviewed. Attempts were made to determine the applicability of each of the 65 sensors for ground and/or vehicle use.

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.

Meridional overturning circulation conveys fast acidification to the deep Atlantic Ocean

Science.gov (United States)

Perez, Fiz F.; Fontela, Marcos; García-Ibáñez, Maribel I.; Mercier, Herlé; Velo, Anton; Lherminier, Pascale; Zunino, Patricia; de La Paz, Mercedes; Alonso-Pérez, Fernando; Guallart, Elisa F.; Padin, Xose A.

2018-02-01

Since the Industrial Revolution, the North Atlantic Ocean has been accumulating anthropogenic carbon dioxide (CO2) and experiencing ocean acidification, that is, an increase in the concentration of hydrogen ions (a reduction in pH) and a reduction in the concentration of carbonate ions. The latter causes the ‘aragonite saturation horizon’—below which waters are undersaturated with respect to a particular calcium carbonate, aragonite—to move to shallower depths (to shoal), exposing corals to corrosive waters. Here we use a database analysis to show that the present rate of supply of acidified waters to the deep Atlantic could cause the aragonite saturation horizon to shoal by 1,000-1,700 metres in the subpolar North Atlantic within the next three decades. We find that, during 1991-2016, a decrease in the concentration of carbonate ions in the Irminger Sea caused the aragonite saturation horizon to shoal by about 10-15 metres per year, and the volume of aragonite-saturated waters to reduce concomitantly. Our determination of the transport of the excess of carbonate over aragonite saturation (xc[CO32-])—an indicator of the availability of aragonite to organisms—by the Atlantic meridional overturning circulation shows that the present-day transport of carbonate ions towards the deep ocean is about 44 per cent lower than it was in preindustrial times. We infer that a doubling of atmospheric anthropogenic CO2 levels—which could occur within three decades according to a ‘business-as-usual scenario’ for climate change—could reduce the transport of xc[CO32-] by 64-79 per cent of that in preindustrial times, which could severely endanger cold-water coral habitats. The Atlantic meridional overturning circulation would also export this acidified deep water southwards, spreading corrosive waters to the world ocean.

Traditional Chinese and western medicine for the prevention of deep venous thrombosis after lower extremity orthopedic surgery: a meta-analysis of randomized controlled trials.

Science.gov (United States)

Zhu, Shibai; Song, Yi; Chen, Xi; Qian, Wenwei

2018-04-10

Chinese herbal medicine has traditionally been considered to promote blood circulation to remove obstruction in the channels and clear pathogenic heat to drain dampness effects. We conducted this meta-analysis to evaluate its benefits for the prevention of deep venous thrombosis (DVT) after lower extremity orthopedic surgery. Relevant, published studies were identified using the following keywords: lower extremity orthopedic surgery, arthroplasty, joint replacement, fracture, traditional Chinese and western medicine, Chinese herbal medicine, deep venous thrombosis (DVT), and Venous thromboembolism (VTE). The following databases were used to identify the literature consisting of RCTs with a date of search of 31 May 2017: PubMed, Cochrane Library, Web of knowledge, the Chinese National Knowledge Infrastructure Database, the Chongqing VIP Database, the Chinese Biomedical Database, and the Wanfang Database (including three English and four Chinese databases). All relevant data were collected from studies meeting the inclusion criteria. The outcome variables were the incidence rate of DVT, activated partial thromboplastin time (APTT), prothrombin time (PT), and D-dimer; subcutaneous hematoma; and other reported outcomes. RevMan5.2. software was adopted for the meta-analysis. A total of 20 published studies (1862 cases) met the inclusion criteria. The experimental group, 910 patients (48.87%), received the Chinese herbal medicine or traditional Chinese and western medicine for prevention of DVT; the control group, 952 patients (51.13%), received the standard western treatment. The meta-analysis showed that traditional Chinese and western medicine therapy reduced the incidence rates of DVT significantly when compared with controls (risk ratio [RR] = 0.40; 95% CI, 0.30 to 0.54; P < 0.00001), and the D-dimer was lower in the experimental group (P = 0.01). Besides, the incidence rate of subcutaneous hematoma was lower in the experimental group (P < 0

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

RODOS database adapter

International Nuclear Information System (INIS)

Xie Gang

1995-11-01

Integrated data management is an essential aspect of many automatical information systems such as RODOS, a real-time on-line decision support system for nuclear emergency management. In particular, the application software must provide access management to different commercial database systems. This report presents the tools necessary for adapting embedded SQL-applications to both HP-ALLBASE/SQL and CA-Ingres/SQL databases. The design of the database adapter and the concept of RODOS embedded SQL syntax are discussed by considering some of the most important features of SQL-functions and the identification of significant differences between SQL-implementations. Finally fully part of the software developed and the administrator's and installation guides are described. (orig.) [de

Supply Chain Initiatives Database

Energy Technology Data Exchange (ETDEWEB)

None

2012-11-01

The Supply Chain Initiatives Database (SCID) presents innovative approaches to engaging industrial suppliers in efforts to save energy, increase productivity and improve environmental performance. This comprehensive and freely-accessible database was developed by the Institute for Industrial Productivity (IIP). IIP acknowledges Ecofys for their valuable contributions. The database contains case studies searchable according to the types of activities buyers are undertaking to motivate suppliers, target sector, organization leading the initiative, and program or partnership linkages.

The IRPVM-DB database

International Nuclear Information System (INIS)

Davies, L.M.; Gillemot, F.; Yanko, L.; Lyssakov, V.

1997-01-01

The IRPVM-DB (International Reactor Pressure Vessel Material Database) initiated by the IAEA IWG LMNPP is going to collect the available surveillance and research data world-wide on RPV material ageing. This paper presents the purpose of the database; it summarizes the type and the relationship of data included; it gives information about the data access and protection; and finally, it summarizes the state of art of the database. (author). 1 ref., 2 figs

The IRPVM-DB database

Energy Technology Data Exchange (ETDEWEB)

Davies, L M [Davies Consultants, Oxford (United Kingdom); Gillemot, F [Atomic Energy Research Inst., Budapest (Hungary); Yanko, L [Minatom (Russian Federation); Lyssakov, V [International Atomic Energy Agency, Vienna (Austria)

1997-09-01

The IRPVM-DB (International Reactor Pressure Vessel Material Database) initiated by the IAEA IWG LMNPP is going to collect the available surveillance and research data world-wide on RPV material ageing. This paper presents the purpose of the database; it summarizes the type and the relationship of data included; it gives information about the data access and protection; and finally, it summarizes the state of art of the database. (author). 1 ref., 2 figs.

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

Electron Effective-Attenuation-Length Database

Science.gov (United States)

SRD 82 NIST Electron Effective-Attenuation-Length Database (PC database, no charge)   This database provides values of electron effective attenuation lengths (EALs) in solid elements and compounds at selected electron energies between 50 eV and 2,000 eV. The database was designed mainly to provide EALs (to account for effects of elastic-eletron scattering) for applications in surface analysis by Auger-electron spectroscopy (AES) and X-ray photoelectron spectroscopy (XPS).

DATABASE REPLICATION IN HETEROGENOUS PLATFORM

OpenAIRE

Hendro Nindito; Evaristus Didik Madyatmadja; Albert Verasius Dian Sano

2014-01-01

The application of diverse database technologies in enterprises today is increasingly a common practice. To provide high availability and survavibality of real-time information, a database replication technology that has capability to replicate databases under heterogenous platforms is required. The purpose of this research is to find the technology with such capability. In this research, the data source is stored in MSSQL database server running on Windows. The data will be replicated to MyS...

Database security in the cloud

OpenAIRE

Sakhi, Imal

2012-01-01

The aim of the thesis is to get an overview of the database services available in cloud computing environment, investigate the security risks associated with it and propose the possible countermeasures to minimize the risks. The thesis also analyzes two cloud database service providers namely; Amazon RDS and Xeround. The reason behind choosing these two providers is because they are currently amongst the leading cloud database providers and both provide relational cloud databases which makes ...

A Simple Deep Learning Method for Neuronal Spike Sorting

Science.gov (United States)

Yang, Kai; Wu, Haifeng; Zeng, Yu

2017-10-01

Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

A compilation of structure functions in deep inelastic scattering

International Nuclear Information System (INIS)

Gehrmann, T.; Roberts, R.G.; Whalley, M.R.

1999-01-01

A compilation of all the available data on the unpolarized structure functions F 2 and xF 3 , R=(σ L /σ T ), the virtual photon asymmetries A 1 and A 2 and the polarized structure functions g 1 and g 2 , from deep inelastic lepton scattering off protons, deuterium and nuclei is presented. The relevant experiments at CERN, DESY, Fermilab and SLAC from 1991, the date of our earlier review [1], to the present day are covered. A brief general theoretical introduction is given followed by the data presented both in tabular and graphical form and, for the F 2 and xF 3 data, the predictions based on the MRST98 and CTEQ4 parton distribution functions are also displayed. All the data in this review, together with data on a wide variety of other reactions, can be found in and retrieved from the Durham-RAL HEP Databases on the World-Wide-Web (http://durpdg.dur.ac.uk/HEPDATA). (author)

Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network.

Science.gov (United States)

Chi, Jianning; Walia, Ekta; Babyn, Paul; Wang, Jimmy; Groot, Gary; Eramian, Mark

2017-08-01

With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into "malignant" and "benign" cases. The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.

Overview of deep learning in medical imaging.

Science.gov (United States)

Suzuki, Kenji

2017-09-01

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

Clinical databases in physical therapy.

NARCIS (Netherlands)

Swinkels, I.C.S.; Ende, C.H.M. van den; Bakker, D. de; Wees, Ph.J van der; Hart, D.L.; Deutscher, D.; Bosch, W.J.H. van den; Dekker, J.

2007-01-01

Clinical databases in physical therapy provide increasing opportunities for research into physical therapy theory and practice. At present, information on the characteristics of existing databases is lacking. The purpose of this study was to identify clinical databases in which physical therapists

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.

Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning

Science.gov (United States)

Zhou, Tian; Icke, Ilknur; Dogdas, Belma; Parimal, Sarayu; Sampath, Smita; Forbes, Joseph; Bagchi, Ansuman; Chin, Chih-Liang; Chen, Antong

2017-02-01

In developing treatment of cardiovascular diseases, short axis cine MRI has been used as a standard technique for understanding the global structural and functional characteristics of the heart, e.g. ventricle dimensions, stroke volume and ejection fraction. To conduct an accurate assessment, heart structures need to be segmented from the cine MRI images with high precision, which could be a laborious task when performed manually. Herein a fully automatic framework is proposed for the segmentation of the left ventricle from the slices of short axis cine MRI scans of porcine subjects using a deep learning approach. For training the deep learning models, which generally requires a large set of data, a public database of human cine MRI scans is used. Experiments on the 3150 cine slices of 7 porcine subjects have shown that when comparing the automatic and manual segmentations the mean slice-wise Dice coefficient is about 0.930, the point-to-curve error is 1.07 mm, and the mean slice-wise Hausdorff distance is around 3.70 mm, which demonstrates the accuracy and robustness of the proposed inter-species translational approach.

CMS reimbursement reform and the incidence of hospital-acquired pulmonary embolism or deep vein thrombosis.

Science.gov (United States)

Gidwani, Risha; Bhattacharya, Jay

2015-05-01

In October 2008, the Centers for Medicare & Medicaid Services (CMS) stopped reimbursing hospitals for the marginal cost of treating certain preventable hospital-acquired conditions. This study evaluates whether CMS's refusal to pay for hospital-acquired pulmonary embolism (PE) or deep vein thrombosis (DVT) resulted in a lower incidence of these conditions. We employ difference-in-differences modeling using 2007-2009 data from the Nationwide Inpatient Sample, an all-payer database of inpatient discharges in the U.S. Discharges between 1 January 2007 and 30 September 2008 were considered "before payment reform;" discharges between 1 October 2008 and 31 December 2009 were considered "after payment reform." Hierarchical regression models were fit to account for clustering of observations within hospitals. The "before payment reform" and "after payment reform" incidences of PE or DVT among 65-69-year-old Medicare recipients were compared with three different control groups of: a) 60-64-year-old non-Medicare patients; b) 65-69-year-old non-Medicare patients; and c) 65-69-year-old privately insured patients. Hospital reimbursements for the control groups were not affected by payment reform. CMS payment reform for hospital-based reimbursement of patients with hip and knee replacement surgeries. The outcome was the incidence proportion of hip and knee replacement surgery admissions that developed pulmonary embolism or deep vein thrombosis. At baseline, pulmonary embolism or deep vein thrombosis were present in 0.81% of all hip or knee replacement surgeries for Medicare patients aged 65-69 years old. CMS payment reform resulted in a 35% lower incidence of hospital-acquired pulmonary embolism or deep vein thrombosis in these patients (p = 0.015). Results were robust to sensitivity analyses. CMS's refusal to pay for hospital-acquired conditions resulted in a lower incidence of hospital-acquired pulmonary embolism or deep vein thrombosis after hip or knee replacement surgery

Nuclear technology databases and information network systems

International Nuclear Information System (INIS)

Iwata, Shuichi; Kikuchi, Yasuyuki; Minakuchi, Satoshi

1993-01-01

This paper describes the databases related to nuclear (science) technology, and information network. Following contents are collected in this paper: the database developed by JAERI, ENERGY NET, ATOM NET, NUCLEN nuclear information database, INIS, NUclear Code Information Service (NUCLIS), Social Application of Nuclear Technology Accumulation project (SANTA), Nuclear Information Database/Communication System (NICS), reactor materials database, radiation effects database, NucNet European nuclear information database, reactor dismantling database. (J.P.N.)

Administrative Databases Can Yield False Conclusions-An Example of Obesity in Total Joint Arthroplasty.

Science.gov (United States)

George, Jaiben; Newman, Jared M; Ramanathan, Deepak; Klika, Alison K; Higuera, Carlos A; Barsoum, Wael K

2017-09-01

Research using large administrative databases has substantially increased in recent years. Accuracy with which comorbidities are represented in these databases has been questioned. The purpose of this study was to evaluate the extent of errors in obesity coding and its impact on arthroplasty research. Eighteen thousand thirty primary total knee arthroplasties (TKAs) and 10,475 total hip arthroplasties (THAs) performed at a single healthcare system from 2004-2014 were included. Patients were classified as obese or nonobese using 2 methods: (1) body mass index (BMI) ≥30 kg/m 2 and (2) international classification of disease, 9th edition codes. Length of stay, operative time, and 90-day complications were collected. Effect of obesity on various outcomes was analyzed separately for both BMI- and coding-based obesity. From 2004 to 2014, the prevalence of BMI-based obesity increased from 54% to 63% and 40% to 45% in TKA and THA, respectively. The prevalence of coding-based obesity increased from 15% to 28% and 8% to 17% in TKA and THA, respectively. Coding overestimated the growth of obesity in TKA and THA by 5.6 and 8.4 times, respectively. When obesity was defined by coding, obesity was falsely shown to be a significant risk factor for deep vein thrombosis (TKA), pulmonary embolism (THA), and longer hospital stay (TKA and THA). The growth in obesity observed in administrative databases may be an artifact because of improvements in coding over the years. Obesity defined by coding can overestimate the actual effect of obesity on complications after arthroplasty. Therefore, studies using large databases should be interpreted with caution, especially when variables prone to coding errors are involved. Copyright © 2017 Elsevier Inc. All rights reserved.

Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.

Science.gov (United States)

Durant, Thomas J S; Olson, Eben M; Schulz, Wade L; Torres, Richard

2017-12-01

Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated profiling of erythrocytes from digital images by capable machine learning approaches would augment the throughput and value of morphologic analysis. To this end, we sought to evaluate the performance of leading implementation strategies for convolutional neural networks (CNNs) when applied to classification of erythrocytes based on morphology. Erythrocytes were manually classified into 1 of 10 classes using a custom-developed Web application. Using recent literature to guide architectural considerations for neural network design, we implemented a "very deep" CNN, consisting of >150 layers, with dense shortcut connections. The final database comprised 3737 labeled cells. Ensemble model predictions on unseen data demonstrated a harmonic mean of recall and precision metrics of 92.70% and 89.39%, respectively. Of the 748 cells in the test set, 23 misclassification errors were made, with a correct classification frequency of 90.60%, represented as a harmonic mean across the 10 morphologic classes. These findings indicate that erythrocyte morphology profiles could be measured with a high degree of accuracy with "very deep" CNNs. Further, these data support future efforts to expand classes and optimize practical performance in a clinical environment as a prelude to full implementation as a clinical tool. © 2017 American Association for Clinical Chemistry.

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